Medical Image Segmentation

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Overview

Medical image segmentation assigns labels to pixels or voxels to outline structures such as tumors, organs, vessels, and surgical instruments, enabling quantitative analysis and clinical workflows. Common approaches include U-Net-style convolutional networks, transformers and state-space models, and promptable foundation models; work also uses anatomical and topological priors, text guidance, and generative models. To reduce reliance on dense expert labels, researchers study self-supervised pretraining, semi- and weakly supervised learning, synthetic data, and interactive annotation, while domain adaptation addresses differences across scanners, hospitals, and modalities. A central challenge is making models reliable beyond their training data: high overlap scores can conceal boundary, connectivity, small-lesion, or subgroup failures, so external validation, uncertainty estimates, and clinically relevant metrics remain important.

Latest papers

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788 papers62 papers in the last four weeks, down 29% on the four weeks before. 0.4% of all new papers.

Jul 11, 2026

GRC-ProbNet: Uncertainty-aware Feature Extraction for Cardiovascular Disease Classification

The automatic detection and classification of cardiovascular disease (CVD) from computed tomography (CT) images plays an important role in clinical practice. Recently, a hybrid pipeline (GRC-Net) for CVD classification was proposed, which leverages a deep-learning-based segmentation and registration method to extract radiomic and geometric features. However, GRC-Net relies on a deterministic segmentation mask, without considering the inherent ambiguity associated with cardiac anatomy. In this paper, we propose GRC-ProbNet, which takes advantage of a deep ensemble to produce multiple segmentation masks for a given input. From these masks, we extract multiple uncertainty features. We analyze these uncertainty features for both their correlation with segmentation error and their propagation effects on downstream CVD classification performance. Our experiments on the publicly available MM-WHS and ASOCA datasets show that the uncertainty measure that best reflects segmentation quality is not necessarily the one that provides the strongest signal for downstream CVD classification. Overall, our results demonstrate that GRC-ProbNet utilizing uncertainty features substantially improves CVD classification AUROC (92.92) compared to the baseline GRC-Net model (91.25%). Our code is publicly available: https://github.com/biomedia-mira/GRC-ProbNet.

Yash Shah, Omar Todd, Philipp Seeböck +3

1Imperial College London, UK · 2Medical University of Vienna, Austria

Medical Image SegmentationSegmentation Uncertainty EstimationComputed Tomography
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Jul 11, 2026

BiLoG-Net: A Bi-Context Location-Guided Network for Breast Mass Segmentation and Malignancy Classification in Mammography

Breast cancer remains the most commonly diagnosed malignancy among women worldwide, yet accurate detection and characterization of breast masses in mammography remain challenging due to subtle intensity variations, heterogeneous tissue densities, and indistinct lesion boundaries that complicate radiological interpretation. To address these limitations, we propose BiLoG-Net, a deep learning framework that jointly performs breast mass segmentation and malignancy classification through bi-context location-aware feature modeling and segmentation-guided attention mechanisms. Our architecture integrates a novel encoder-decoder paradigm with Fire-based feature extraction, lightweight global and local feature enhancement modules, and adaptive location-aware gating to simultaneously capture long-range contextual dependencies and fine-grained boundary-sensitive details. Unlike conventional multi-stage pipelines, our tightly coupled multi-task design enables mutual reinforcement between pixel-level localization and image-level diagnosis, reducing error propagation while producing spatially grounded malignancy predictions. Evaluated on CBIS-DDSM and INBreast benchmarks, BiLoG-Net achieves state-of-the-art performance with Dice scores of 94.20% and 93.10%, classification accuracies of 95.20% and 93.60%, and AUC values of 97.10% and 96.00%, respectively, substantially outperforming existing CNN and transformer-based baselines. By combining precise boundary delineation with reliable malignancy assessment in a single end-to-end model, this work holds strong potential for clinical computer-aided detection systems, helping radiologists prioritize suspicious cases and improve screening efficiency in busy clinical settings.

Abu Fatema Mohammad Abdun Noor, Md Imam Ahasan, Md Samiul Ahasan +3

Department of Software Engineering, Daffodil International University, Dhaka, Bangladesh · BricksCloud AI, Uttara, Dhaka, Bangladesh · College of Computer Science, Chongqing University, Chongqing, China +3

Multi-Task LearningMedical Image ClassificationMedical Image SegmentationMammography
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Jul 10, 2026

Slide-Level Active Learning Reduces Annotation Burden in H&E images

Deep learning-based segmentation of histopathology whole-slide images (WSIs) requires large amounts of pixel-level annotations, which are costly and time-consuming to obtain. Active learning (AL) has been proposed to reduce this effort, but existing methods exhibit three key limitations. Uncertainty estimation is unreliable on partially annotated WSIs, patch-level acquisition is inconsistent with slide-level annotation workflows, and class imbalance in multi-class settings is not explicitly addressed. To address these challenges, we propose SHAL (Slide-level Hybrid Active Learning), a patient-level AL framework for annotation-efficient multi-class histopathology segmentation. SHAL integrates three complementary components: a foreground-aware strategy that suppresses bias from unlabeled background regions, a stage-adaptive mechanism that hybridizes predictive entropy and epistemic uncertainty across learning stages, and a class-aware strategy that prioritizes diagnostically relevant tissue classes. SHAL is evaluated on the TCGA colorectal cancer dataset. It achieves the highest Macro Dice at the full annotation budget (0.846) and reaches Dice greater than or equal to 0.80 using only 26 percent of the budget (50 of 190 slides), whereas competing methods reach this threshold only at 37 percent (70 slides). Across five independent external cohorts, SHAL attains the highest mean external Macro Dice (0.815) and the smallest internal-to-external generalization gap among all methods (0.025 at Round 3 and 0.026 at the full budget). The results indicate that patient-level hybrid uncertainty acquisition reduces annotation cost without sacrificing cross-domain generalization in computational pathology.

Mahsa Vali, Zhilong Weng, Noémie Moreaua +2

Institute for Biomedical Informatics, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany · Center for Molecular Medicine Cologne (CMMC), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany · Faculty of Mathematics and Natural Sciences, University of Cologne, Cologne, Germany +3

Computational PathologyHistopathology Image SegmentationMedical Image SegmentationWhole-Slide Image Analysis
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Jul 9, 2026

Uncertainty-Aware Multi-Source Retinal Fluid Segmentation in OCT

Measuring retinal fluid from optical coherence tomography (OCT) drives treatment decisions in macular disease, but manual annotation is slow and segmentation models trained on one scanner degrade on another. We present an attention-guided TransUNet that segments three fluid types across four independent OCT sources, combining a domain-adaptive normalisation scheme with an uncertainty estimate that flags unreliable pixels. The model reaches a mean fluid Dice of 0.78, and -- most usefully for clinicians -- its uncertainty is 1.34x higher exactly where expert graders disagree (p<10^-4), turning a raw segmentation map into an actionable clinical triage signal.

Animesh Kumar

Newcastle University, Newcastle upon Tyne NE1 7RU, United Kingdom

Domain AdaptationMedical ImagingSegmentation Uncertainty EstimationMulti-Source Domain AdaptationOptical Coherence TomographyMedical Image Segmentation+1
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Jul 9, 2026

Multi-Resolution Feature Stem for Diabetic Retinopathy lesion segmentation

Diabetic Retinopathy (DR) is a leading cause of preventable blindness worldwide, requiring automated lesion segmentation using deep learning models for early detection and monitoring. However, DR lesions vary dramatically in size from tiny microaneurysms to large hemorrhages and exudates. This variability creates conflicting demands on the model architecture and input resolution, posing a challenge for effective design. This work investigates the impact of input resolution on different lesion types. Through systematic experimentation with multiple architectures (U-Net, UNet++, Vision Transformers, DeepLabV3+) at 512×512512 \times 512 and 1024×10241024 \times 1024 resolutions, we identify a critical, counter-intuitive phenomenon where increasing input resolution has opposing effects on different lesion types. We demonstrate that while higher resolution is essential for resolving fine-grained microaneurysms, it can unexpectedly degrade performance on larger hemorrhages. This finding challenges the common assumption that higher resolution is uniformly beneficial. To address this, we propose a novel Multi-Resolution Feature Stem, an input-level pyramid integrated with a UNet++ backbone. This architecture processes multiple scales in parallel, capturing fine-grained details without sacrificing contextual information. This work contributes crucial empirical evidence of this complex, resolution-dependent behavior and a practical, parameter-efficient architecture that successfully resolves this trade-off.

Indranil Dutta, Taehee Jeong

San Jose State University

U-NetMulti-Scale Feature FusionMedical Image Segmentation
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Jul 9, 2026

Metrics or Mirage? An Audit of Evaluation Inconsistencies in Colonoscopy Polyp Segmentation Benchmarks

Progress in colonoscopy polyp segmentation is routinely reported through leaderboard comparisons on a small set of public benchmarks. We argue that this apparent progress is difficult to verify: a systematic audit of 27 papers published between 2015 and 2026 reveals three structural problems in how the community evaluates models. First, 25 of 27 papers omit the Hausdorff distance. Hausdorff distance is a boundary-accuracy metric with direct clinical relevance for detecting flat or small polyps, and is a standard in radiotherapy segmentation. Second, at least five incompatible train/test split protocols co-exist across papers reporting results on the same two datasets (Kvasir-SEG and CVC-ClinicDB), making published Dice scores non-comparable even when they appear in the same leaderboard column. Third, 26 of 27 papers make performance claims without any statistical significance test. Strikingly, four papers published after the Metrics Reloaded framework~\cite{metricsreloaded2024} (Maier-Hein et al., Nature Methods 2024) perpetuate these same problems, suggesting that general-purpose metric guidance has not yet reached the colonoscopy sub-community. To show these problems are not merely cosmetic, we re-evaluate five representative models under three controlled protocols with a single uniform scorer, and find that the reported metric conceals large boundary and recall failures, that the ``best'' model changes with the metric, and that near-tied rankings reverse across random splits. We propose a five-point Polyp Segmentation Reporting Checklist~(PSRC) as a lightweight, domain-adapted corrective.

Aisha Urooj, Zain Ul Abdien, Neelu Madan

Lunit, South Korea · RPTU Kaiserslautern, Germany · Aalborg University & Pioneer Center, Denmark

Polyp SegmentationBenchmark ReproducibilityMedical Image SegmentationScientific ReproducibilityMedical Image Benchmarks
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Jul 9, 2026

Attention-Based Segmentation of WMHs and Differentiation of Vascular vs. Demyelinating Lesions

White Matter Hyperintensities (WMHs) are commonly observed in brain Magnetic Resonance Imaging (MRI) scans. They are associated with various neurological conditions, including vascular and inflammatory demyelinating diseases. Despite differing in etiology, WMHs from these conditions often appear similar on Fluid Attenuated Inversion Recovery (FLAIR) images. This similarity makes differential diagnosis challenging. In this work, we highlight the potential of combining attention-based segmentation with feature-driven classification. This approach supports more accurate and efficient classification between vascular and demyelinating white matter pathologies. For segmentation, we evaluate the effectiveness of attention mechanisms, specifically the Bottleneck Attention Module (BAM) and the Convolutional Block Attention Module (CBAM). We also test different architectures, particularly Attention U-Net. In addition, we explore advanced training strategies, such as patch-based learning and a 2.5D approach, to enhance lesion detection. After segmentation, we extract morphological features from the lesion masks. We then use them to classify WMHs based on their underlying cause. Our experiments utilize five publicly available datasets with diverse imaging protocols to promote model generalizability, despite limited sample sizes. The results suggest that attention-based segmentation and feature-driven classification offer a promising direction for discriminating vascular and demyelinating white matter lesions. Further validation in larger clinical cohorts is still needed.

Aina Tur-Serrano, Gabriel Moyà-Alcover, Francisco J. Perales López

UGiVIA Research Group, Dpt. of Mathematics and Computer Science, Universitat de les Illes Balears, Palma de Mallorca, 07122, Spain

Image SegmentationMedical Image SegmentationNeuroimaging
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Jul 8, 2026

False Confidence: Automated Labels Confound Fairness Audits in Cervical Spine Segmentation

Automated segmentation of cervical-spine MRI is increasingly used in clinical workflows, yet no fairness audit exists for this anatomy. We show that auditing these segmentation tasks is complicated by a common property of modern segmentation datasets: expert-annotated gold labels are expensive, so abundant machine-generated (silver) labels are added to limit annotation cost. This matters because the reference used to judge a model can itself be biased. In this study, we present the first fairness audit of cervical-spine MRI segmentation across sex, age, and race using the CSpineSeg dataset. We observe that the deployed model is demographically fair, but the choice of reference label, however, is not neutral. Because a dataset's silver labels are generated by a model trained on its gold labels, any new model trained on those same gold labels agrees more with the silver labels than with expert truth: scoring identical predictions against silver rather than gold overestimates performance by ~8 Dice points and turns the fairness verdict for age from non-significant to significant - not by the gap inflation Parikh et al. report (which we term false magnitude) but by collapsing within-group variance (which we term false confidence). Reference-label provenance is thus a first-order confounder in segmentation evaluation: performance and fairness should be reported against expert labels, and any fairness claim stated together with the provenance of its reference.

Linus Juni, Aasa Feragen, Aditya Parikh

Section for Visual Computing, DTU Compute, Technical University of Denmark, Kongens Lyngby, Denmark

Algorithmic FairnessMedical Image SegmentationAutomated Evaluation
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Jul 8, 2026

Automatic Echocardiography Segmentation via Transition Probability Correlation for Stable Semantic Extraction

While echocardiography is essential for cardiovascular diagnosis, inherent speckle noise and low signal-to-noise ratio often lead to ambiguous semantic features and fragmented boundaries. These limitations significantly hinder the segmentation accuracy of deep learning models in complex clinical cases. Moreover, temporal motion of the heart plays a critical role in recognizing anatomical structures. To address these challenges, we designed a STLSF module which comprises a window-matching-based semantic correction component and a semantics-guided texture enhancement component. By leveraging local transition probability correlations to correct semantics and employing semantics-guided texture enhancement, the STLSF module effectively mitigates texture instability and ambiguous semantic interpretations caused by disadvantaged echocardiography quality. Additionally, to facilitate the encoder's adaptation to the intrinsic priors of ultrasound-specific imaging patterns, we propose a frequency-aware denoising pre-training method. The entire work builds a convolution-based network with locality inductive bias and long-range dependencies. Extensive experiments confirm our SOTA performance, achieving 93.87% Dice on CAMUS and 92.62% on EchoNet-Dynamic, with respective HD95 values of 3.29mm and 2.73mm.

Xinran Chen, Xiyuan Wang, Guangquan Zhou +1

School of Biological Science and Medical Engineering, Southeast University, Nanjing, China

Ultrasound Image SegmentationSelf-Supervised Pre-TrainingEchocardiographyMedical Image SegmentationSemantic Segmentation
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Jul 8, 2026

Towards Accurate and Fast Clinical Body Composition: A Resource-Efficient Hierarchical Segmentation Framework for Multi-Source CT

Background: Automated 3D segmentation of muscles and adipose tissue from CT is vital for body composition analysis, but multi-source data heterogeneity and high CPU memory demands hinder clinical deployment. Methods: We propose a coarse-to-fine hierarchical framework to segment ten tissue structures. Efficiency is optimized using Dynamic Spacing and Anisotropic Patching, a Group Inference mechanism for low-memory sliding-window processing, and Topology-Aware Asymmetric Resampling for fast post-processing. Results: The framework was trained on 1,558 CT volumes from seven public and two private datasets, and evaluated on an independent test cohort (N=105), per-structure Dice coefficients ranged from 0.924 to 0.982. Eight major structures met the +-10% relative error clinical acceptance limit. On a 12-core CPU workstation, the GPU-free pipeline averaged 44.5 seconds per volume with 4.73 GB peak memory. Conclusion: This framework balances accuracy and efficiency, enabling robust, large-scale body composition analysis on standard CPU workstations.

Xiaodi Shen, Qingzhu Zheng, Yaoyang Qiu +12

Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China · Research & Development Center, Canon Medical Systems (China) Co. Ltd. Beijing 100015, China

Medical Image AnalysisMedical Image Segmentation3D Medical Image SegmentationComputed TomographySemantic Segmentation
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Jul 8, 2026

Widest-Path Reachability Fields for Connectivity-Preserving Slender Structure Segmentation

Segmenting slender curvilinear structures such as retinal vessels, cracks, and roads demands topological correctness, as even a single-pixel discontinuity can fragment a continuous network and invalidate downstream analysis. Under standard binary-mask supervision, models optimized for pixel-level overlap frequently produce topologically broken predictions. We trace this to a fundamental mismatch: pixel-wise losses distribute gradients uniformly, yet connectivity hinges on a sparse set of bottleneck pixels. These pixels are vastly outnumbered by thick structures and background, rendering their aggregate gradient contribution negligible. We term this phenomenon topological gradient starvation (TGS). To address it, we propose Widest-Path Reachability Fields (WPRF), a differentiable Max-Min reachability objective that redirects gradient flow to connectivity bottlenecks. The module is plug-and-play, backbone-agnostic, and incurs no inference overhead. WPRF implements a differentiable Max-Min objective via dynamic programming on a domain-restricted graph, coupled with a bottleneck-aware observation term that balances gradient contributions across varying structures. Compared to prior topology-aware losses that rely on post-hoc skeletonization or homology computation, WPRF directly optimizes end-to-end reachability via differentiable Max-Min algebra, enabling gradient flow to concentrate on connectivity bottlenecks without auxiliary structures. We introduce OMVIS, a new oral microvessel segmentation dataset. Experiments across nine architectures and six datasets validate the bottleneck-focused gradient routing mechanism. WPRF improves 87% of experiments with fixed hyperparameters and achieves clDice gains of 7.2 percentage points on structurally fragile datasets.

Youcheng Zong, Runda Jia, Minxuan Hu +2

College of Information Science and Engineering, Northeastern University, Shenyang 110004, China · Changsha Stomatological Hospital, Changsha 410005, China

Image SegmentationTopology-Preserving Image SegmentationMedical Image SegmentationDifferentiable OptimizationThin-Structure Segmentation
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Jul 8, 2026

HPR-SAM: Hierarchical Probabilistic Representation Learning for Prompt-free SAM-based Medical Image Segmentation

Prompt-free adaptation of the Segment Anything Model (SAM) has emerged as a promising paradigm for automatic medical image segmentation. Existing methods mainly focus on prompt generation, while overlooking that prompt quality is fundamentally constrained by the expressiveness of anatomical representations. However, deterministic prototypes or semantic tokens are insufficient to jointly capture global anatomical priors, intra-structure diversity, and local structural reliability. To address this limitation, we propose the Hierarchical Probabilistic Representation (HPR) framework, which learns complementary anatomical representations through Distributional Anatomical Representation (DAR), Multi-component Anatomical Representation (MAR), and Local Reliability Representation (LRR), and integrates their predictions via Hierarchical Prediction Fusion (HPF) while remaining compatible with the original SAM decoder. Experiments on the Synapse, LA, and PROMISE12 datasets demonstrate that HPR-SAM achieves state-of-the-art performance on Synapse and the best performance under few-shot settings on LA and PROMISE12, validating the effectiveness of the proposed hierarchical probabilistic representation learning framework for prompt-free medical image segmentation. Code is available at https://anonymous.4open.science/r/HPR-SAM-E4AF.

Yingzhen Hu, Yiheng Zhong, Keying Zhu +5

Yale University, United States

Medical Imaging Foundation ModelsRepresentation LearningHierarchical Representation LearningMedical Image Segmentation
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Jul 7, 2026

OBBSeg: Irregular Lesion Segmentation under Oriented Bounding Box Annotations

Pixel-level annotation remains a major bottleneck in medical image segmentation, making weak supervision an attractive yet under-constrained alternative. We propose OBBSeg, an intermediate supervision paradigm guided by Oriented Bounding Boxes (OBBs) that bridges the gap between full and weak supervision. By jointly encoding spatial extent and orientation, OBBs provide compact geometric supervision that better aligns with elongated or anisotropic lesions, reducing the ambiguity of coarse box annotations. To mitigate the inherent rectangular bias of OBBs, we introduce a Mask-to-OBB loss, a differentiable formulation that enforces geometric consistency between predicted masks and OBB regions. Furthermore, we incorporate prompt-driven semantic guidance through two complementary modules-PAFE and DBFE-which enhance foreground representation and suppress background interference. Extensive experiments on 13 datasets across 5 imaging modalities show that OBBSeg not only outperforms existing weakly supervised methods but also achieves performance comparable to fully supervised approaches, demonstrating its potential for efficient and scalable medical image segmentation. The code is available at https://github.com/StarLxc3/OBBSeg.

Jun Wei, Xinchang Liu, Yu Liu +3

Guangdong Provincial Key Laboratory of Visual Media and Multidimensional Intelligence, CSSE, Shenzhen University, China · Institute of Computing Technology, Chinese Academy of Sciences, China

Weakly Supervised Image SegmentationMedical Image Segmentation
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Jul 7, 2026

Decoupled Single-Mask Annotation Noise Detection via Cross-Sectional Patch Self-Consistency

Vascular computed tomography datasets are commonly annotated only once per scan, yielding the pervasive yet under addressed problem of single mask annotation noise. Existing solutions either require costly multirater fusion or are coupled with network training, preventing explicit auditing of where and why labels fail. We introduce a decoupled framework for single-mask annotation noise detection that leverages cross-sectional patch self-consistency to produce interpretable and auditable noise evidence. Tubular anatomy exhibits strong cross-sectional recurrence: patches extracted orthogonally along vessel centrelines recur in appearance across locations and subjects. Thus, anatomically similar patches should have consistent masks, and disagreement signals unreliable annotation. Our method samples cross-sectional patches, retrieves intensity-equivalent neighbours via scalable vector search, and computes a patch-level noise score from statistical mask disagreement, yielding explicit image-mask evidence for every flagged region. Aggregating scores produces scan-level quality maps for dataset quality assessment or quality-weighted training. Experiments on the coronary CT dataset validate the detected noise for improving training robustness and reveal systematic annotation biases. Specifically, transverse and oblique vessels exhibit 5.1 times higher error rates than axis-aligned structures, with additional correlations to cross-sectional area and intensity. Code is available here.

Yinheng Zhu, Xiaowei Xu

Tsinghua University, Beijing, China · Guangdong Provincial People’s Hospital, Guangzhou, China

Medical Image Quality AssessmentMedical Image SegmentationComputed Tomography
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Jul 7, 2026

Complementary Roles of Image Classification and Vessel Segmentation in AI-Based Screening for Retinopathy of Prematurity Plus Disease in a Kenyan Preterm Cohort

Background. Retinopathy of prematurity (ROP) is a preventable cause of childhood blindness, with rising burden in low- and middle-income countries where ROP-trained ophthalmologists are scarce. Plus disease, marked by retinal vessel dilation and tortuosity, triggers treatment but is subjective and variable. Automated screening could extend specialist reach, but African evidence remains limited. Methods. We analysed 121 Kenyan preterm infants, covering 237 eyes and 1,635 fundus images graded as No Plus, Pre-Plus or Plus. Vessel annotations from two graders supported segmentation training. Eleven configurations were evaluated for eye-level Plus detection using patient-grouped nested cross-validation, including image classifiers, multiple-instance learning, multi-task segmentation-classification, and segment-then-classify pipelines. Results. Vessel segmentation was feasible, achieving pooled Dice 0.533, IoU 0.368, sensitivity 0.623 and specificity 0.979 on held-out images. RGB classifiers were highly sensitive but over-referred, while segmentation-coupled models were more specific. Combining approaches improved performance: an OR-based screen achieved the highest sensitivity, an AND-based confirmation achieved the highest specificity, and a probability ensemble gave the best balanced performance, with sensitivity 0.692, specificity 0.914 and balanced accuracy 0.803, outperforming the vision classifier alone. Conclusions. Classification and vessel segmentation are complementary for ROP Plus detection in Kenyan data. Classifiers support sensitive case-finding, while segmentation improves specificity and reduces over-referral. African ROP AI systems should use combined workflows and undergo prospective multi-site validation.

Fred Mutisya, Oscar Onyango, Sarah Sitati +8

Center for Health AI Research, Innovation & Implementation · College of Ophthalmology of Eastern Central and Southern · Kenyatta National Hospital +6

Medical ImagingRetinal Vessel SegmentationMedical Image ClassificationMedical Image Segmentation
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Jul 5, 2026

Spatial Graph Representation and Morphometric Analysis of the Pulmonary Vascular Tree From Computed Tomography Using Multi-Scale Hessian-Based Filter Fusion and TEASAR Skeletonization

Reconstructing the pulmonary vascular tree from computed tomography (CT) images is essential for quantitative lung analysis, vascular morphology assessment, and patient-specific modeling, yet it remains challenging because vessels span multiple scales, from proximal arteries to distal microvasculature. Clinical chest CT is further affected by limited spatial resolution, partial volume effects, heterogeneous image quality, and respiratory motion artifacts. Unlike deep learning-based pulmonary vessel segmentation methods that require large annotated datasets, we propose a deterministic, training-free, and explainable pipeline for CT-based pulmonary vascular tree reconstruction. The method fuses multiscale Hessian-based Frangi and Sato vesselness filters using a weighted maximum response across 12 spatial scales from 1 to 8 mm, enabling detection of large pulmonary arteries and peripheral branches. Lung parenchyma is segmented by Hounsfield unit thresholding, morphological post-processing, and Chan-Vese active contour refinement. Vascular centerlines are extracted using the Kimimaro implementation of the TEASAR algorithm; separate left- and right-lung vascular graphs are then constructed, pruned, and verified for acyclicity. Geometric plausibility is assessed using volumetric fractal dimension, Strahler order analysis, Horton ratios, and Murray's law. The resulting fractal dimension of approximately 2.3 is consistent with reported values for the human pulmonary vasculature. At the same time, residual deviations in branching metrics reflect distal-vessel truncation caused by finite CT resolution. These results indicate that the proposed explainable pipeline can generate geometrically plausible pulmonary vascular tree models and may support quantitative pulmonary imaging, vascular morphometry, and computational lung modeling.

Piotr Mackiewicz, Jakub Kołyska, Radoslaw Roszczyk

Faculty of Electrical Engineering Warsaw University of Technology Warsaw, Poland

3D Medical ImagingMedical Image Segmentation
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Jul 4, 2026

GLOW-FDG: Generalized cancer LesiOn Whole-body segmentation model for 18^{18}F-FDG-PET/CT

Whole-body fluorodeoxyglucose positron emission tomography combined with computed tomography is widely used in cancer care, but manual lesion delineation is slow, subjective, and difficult to scale. We present GLOW-FDG, an open-source artificial intelligence model for whole-body cancer lesion segmentation in fluorodeoxyglucose positron emission tomography and computed tomography. The model was trained on 1,563 scans spanning multiple cancer types and evaluated on 185 external scans from independent institutions. Across breast cancer, nonmetastatic and oligometastatic lung cancer, head and neck cancer, and metastatic melanoma, GLOW-FDG consistently outperformed publicly available benchmark models in lesion detection, while reducing false positives and maintaining strong segmentation accuracy. Quantification of total tumor burden and total lesion glycolysis was robust across cohorts, and performance approached the variability observed between expert radiation oncologists. These results support GLOW-FDG as a generalizable tool for automated cancer segmentation and quantitative imaging biomarker extraction in whole-body imaging.

Maksym Fritsak, Maximilian Rokuss, Hubert S. Gabryś +10

Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland · Faculty of Medicine, University of Zurich, Zurich, Switzerland · Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany +4

Cross-Modality Medical Image SegmentationPET/CT ImagingMedical Image SegmentationOncologyPET/CT Tumor Segmentation
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Jul 3, 2026

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI

Objective: Accurate identification of acute ischemic infarcts on diffusion-weighted magnetic resonance imaging (DWI) is a critical prerequisite for reliable lesion quantification and effective clinical decision support in the management of cerebrovascular events. Methods: This study presents EPRA U-Net (Efficient Pyramid Residual Attention U-Net), a task-specific integrated architecture for efficient and accurate infarct segmentation of DWI images. In the proposed architecture, an EfficientNet-based encoder was used as a hierarchical feature extractor with a minimized parameterization. In addition, a Residual-Recurrent (R2) block (recurrent unrolling step t = 2, following the original formulation) and Atrous Spatial Pyramid Pooling (ASPP) were integrated to enhance the performance of spatial dependency modeling. Additionally, a dual attention mechanism was incorporated to highlight lesion-related activations while concurrently enabling the suppression of extraneous background responses. To prioritize lesion detection consistent with clinical imperative, a Tversky loss function was adopted, emphasizing the sensitivity of detection over its specificity during the optimization process. Results: Experimental evaluations were conducted utilizing an in-house dataset comprising 167 patients with 4,895 DWI slices; subsequently, the performance of the proposed EPRA U-Net was assessed in comparison with state-of-the-art models, specifically UNet++, DeepLabV3+, and TransUNet. The experimental results suggest that EPRA U-Net attained superior performance, evidenced by a pixel-aggregated Dice of 0.8984, a per-sample Dice of 0.9469, an IoU of 0.8155, a Recall of 0.8887, a Lesion F1 of 0.9378, and an HD95 of 11.62 px. Furthermore, a clear reduction in the rate of missed lesions, specifically by 16%, 25%, and 29%, was observed when compared with UNet++, DeepLabV3+, and TransUNet, respectively.

Hasan Ulutas, Muhammet Emin Sahin, Mustafa Fatih Erkoc +4

Yozgat Bozok University, Department of Computer Engineering, Yozgat, Turkey · Izmir Bakircay University, Department of Computer Engineering, Izmir, Turkey · Queen Mary’s Digital Environment Research Institute (DERI), London, UK +3

U-NetMagnetic Resonance ImagingMedical Image SegmentationDiffusion MRI
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Jul 3, 2026

Semantic Segmentation-Driven Image-Level Diagnosis of Liver Cancers in Hematoxylin and Eosin Histopathology Images

As hematoxylin & eosin (H&E) staining constitutes the primary entry point in routine diagnostic workflows, computer-aided diagnosis from whole-slide H&E images is of particular clinical relevance. However, substantial variability in specimen preparation, staining protocols, and scanning conditions, together with inherent uncertainty in expert pixel-level annotations, makes automated analysis of H&E-stained images challenging. In this study, we propose a semantic segmentation-based framework for image-level diagnosis, grounded in the clinically motivated assumption that each histopathological image corresponds to a single cancer type. Image-level predictions are obtained by assigning the class of the dominant pixel-level label in the segmentation output. To ensure clinical relevance, we adopt the nnU-Net architecture and train it on a publicly available dataset collected in our study with pixel-level annotations for three liver cancer types: hepatocellular cacrcinoma (HCC; 55 images from 30 patients), cholangiocellular carcinoma (CCA; 55 images from 29 patients), and colorectal metastatic adenocarcinoma (CMA; 60 images from 30 patients). Annotations were independently provided by four pathologist. We hypothesize that the combination of stain normalization and semantic segmentation mitigates domain shift and reduces sensitivity to annotation noise. Five-fold cross-validation yielded balanced accuracy of 0.975 (HCC), 0.950 (CCA), and 1.000 (CMA), comparable to results obtained with immunohosthochemical staining and superior to several deep learning models trained on patch-level annotations. The proposed framework has the potential to support pathologists in prioritizing immunohistochemical marker selection, thereby reducing diagnostic costs and turnaround time. Integration with immunohistochemical findings improve overall diagnostic reliability.

Ivica Kopriva, Dario Sitnik, Arijana Pacic +3

Division of Computing and Data Science, Ruđer Bošković Institute, Bijenička cesta 54, Zagreb Croatia · Sitnik Ventures UG, Lautensackstr. 25, 80687 Munich, Germany · Department of Pathology and Cytology, Dubrava University Hospital, Avenija Gojka Šuška 6, 10000 Zagreb, Croatia +2

Computational PathologyHistopathology Image SegmentationMedical Image SegmentationOncologyHistopathology Image Classification
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Jul 3, 2026

SNR-Adaptive Unified Diffusion for Multi-Task Medical Image Segmentation

Clinical cardiac imaging pipelines currently deploy separate models for each dataset and modality, incurring redundant training costs and precluding knowledge sharing across anatomically related tasks. Consolidating semi-supervised learning, unsupervised domain adaptation, and domain generalisation into one model is therefore a practical necessity, yet naive joint training exposes a fundamental barrier: conflicting label semantics between datasets collapse LA Dice from 90.31% to 83.38%, while gradient imbalance across tasks of unequal complexity suppresses the weaker tasks throughout training. We present UniT-Diff, a unified diffusion segmentation framework that resolves these conflicts through three targeted mechanisms. An 11-channel task-specific output space physically partitions label categories, eliminating cross-task gradient sign reversal by construction. SNR-Adaptive Task Conditioning (SATC) scales the task token by the log signal-to-noise ratio of the current diffusion timestep, suppressing domain-specific bias during coarse denoising and restoring full task guidance as the signal clears. Task-Type-Aware Conditional Dropout (TTACD) permanently removes the task token for domain-generalisation inputs, routing them through a shared neutral pathway that draws on cross-dataset cardiac anatomy rather than source-vendor statistics. Under a single parameter set, UniT-Diff surpasses independently trained task-specific baselines on all three benchmarks simultaneously: +0.87% on LA, +1.77% on MMWHS, and +0.88% on MNMS.

Jiahao Liu, Hang Wei, Shuai Wu

School of Telecommunications Engineering, Xidian University · School of Computer Science and Technology, Xidian University

Unsupervised Domain AdaptationDomain GeneralizationMulti-Task LearningSemi-Supervised Medical Image SegmentationDiffusion Models for Medical Image SegmentationMedical Image Segmentation
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Jul 3, 2026

Lightweight Polyp Segmentation via a Gain-Aware Prediction-Space Recursive Controller

While lightweight polyp segmentation is highly desirable for low-cost deployment, reported performance gains often stem from upgraded backbone encoders, complex decoders, or heavy refinement branches. Consequently, it remains difficult to isolate whether a lightweight correction mechanism is inherently effective on its own. We address this limitation by formulating refinement as a prediction-space recursive correction task, introducing a recursive controller that operates directly on backbone logits. Under a fixed recursion budget, this controller aggregates discrepancy and uncertainty evidence, updates a compact state tracking recent correction utility, and applies additive residual logit corrections. By design, this correction path remains small, host-portable, and deployment-explicit. Utilizing a unified Kvasir-trained protocol, we evaluate our approach across seven lightweight backbones on Kvasir-SEG and three transfer datasets, measuring segmentation accuracy (Dice/IoU) alongside deployment efficiency (parameters, GMACs, and peak memory). The controller yields consistent improvements in the source domain, achieves competitive performance against both training-side baselines and heavier structural refiners on representative hosts, and delivers selective transfer gains with minimal static overhead. Code is available at https://github.com/tyui99/Gain-Aware-Prediction-Space-Recursive-Controller.

Jiachi Zhang, Zhuoyu Wu, Quanjun Wang +2

Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China · South China University of Technology, Guangzhou, China · CyPhi(ΨΦ) AI Research Lab, School of IT, Monash University Malaysia +1

Polyp SegmentationMedical Image SegmentationIterative Refinement
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Jul 3, 2026

RIGS-Refiner: Risk-Guided Recursive Refinement in Prediction Space for Colonoscopy Polyp Segmentation

Post-refinement can improve colonoscopy segmentation after host inference, but many designs still rely on extra correction heads or multi-stage pipelines with non-negligible parameter or computational cost. For polyp segmentation, host predictions are often already reasonable globally, with remaining errors clustered around ambiguous boundaries and difficult local structures. These residual errors matter in colonoscopy images because useful masks need correct lesion coverage and clean contour delineation across subtle mucosal transitions. This setting favors selective local repair in prediction space over reprocessing the entire mask. We therefore propose RIGS-Refiner, a lightweight post-refinement plugin for risk-guided recursive refinement in prediction space. Starting from a frozen host anchor prediction, RIGS-Refiner extracts lightweight image priors and prediction cues, applies risk-guided update, and writes back residual corrections through a shared recursive cell. The module adds only +519 parameters and +0.631 GFLOPs, keeping the refinement path compact for deployment. Experiments use Kvasir-SEG for training and Kvasir, ClinicDB, ColonDB, and ETIS for evaluation under two frozen hosts, namely PraNet and SegFormer-B0. Results show consistent gains on both hosts and a favorable efficiency-accuracy trade-off against representative post-refinement methods. Code is available at https://github.com/tyui99/RIGS-Refiner.

Jiachi Zhang, Zhuoyu Wu, Wenqi Fang

Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China · South China University of Technology, Guangzhou, China · CyPhi(ΨΦ) AI Research Lab, School of IT, Monash University Malaysia

Polyp SegmentationMedical Image Segmentation
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Jul 2, 2026

MedSaab-US: A Backpropagation-Free Multi-Scale Wavelet-Saab Framework for Thyroid Nodule Segmentation in Ultrasound Images

Deep learning (DL) methods dominate thyroid nodule segmentation in ultrasound (US) images, achieving high Dice scores but at the cost of millions of parameters, GPU-dependent training via backpropagation, and limited mathematical tractability. These limitations impede deployment in resource-constrained environments. In this paper, we propose MedSaab-US, a backpropagation-free segmentation framework grounded in the Green Learning paradigm. MedSaab-US extracts multi-scale spatial-frequency features by combining multi-level Discrete Wavelet Transform (DWT) with multi-scale channel-wise Saab (Subspace Approximation with Adjusted Bias) transforms at patch sizes of 5 x 5, 11 x 11, and 21 x 21 pixels. Label-Assisted Greedy (LAG) feature selection retains the most discriminative features, which are fed to an XGBoost classifier for pixel-wise prediction. The Saab transform parameters are determined analytically from data statistics, while XGBoost employs iterative greedy tree construction without requiring backpropagation. Evaluated on the TN3K dataset (2,879 training and 614 test images), MedSaab-US achieves a mean Dice coefficient of 0.4784 +/- 0.2190, precision of 0.5768, and recall of 0.5604, with a model footprint under 500K parameters and CPU-only inference in approximately 0.3 seconds per image. We present this result as an exploratory non-DL baseline for thyroid ultrasound segmentation and analyze the specific challenges posed by isoechoic nodules. An ablation study further quantifies the contribution of each pipeline component, including separate evaluations of LAG feature selection and training-set size.

Mohammad Amanour Rahman

Department of Computer Science and Engineering Ahsanullah University of Science and Technology (AUST) Dhaka, Bangladesh

Ultrasound Image SegmentationMedical Image Segmentation
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Jul 2, 2026

RadiomicNet: A Hybrid Radiomics-Guided Lightweight Architecture for Interpretable Medical Image Segmentation

Deep learning has achieved remarkable performance in medical image segmentation, yet it suffers from critical limitations: mathematical intractability, substantial parameter requirements, and lack of clinical interpretability. We propose RadiomicNet, a novel two-stream hybrid architecture that enhances standard deep learning by integrating handcrafted radiomics features directly into the segmentation learning process. The key contribution is the Radiomics Attention Gate (RAG), which leverages Gray-Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP) features to modulate skip-connection attention in a lightweight MobileNetV2-based encoder-decoder, providing ante-hoc interpretability without post-hoc approximations. A novel Radiomics Consistency Loss further enforces alignment between texture complexity and prediction uncertainty, reducing Expected Calibration Error (ECE) from 0.142 to 0.118. RadiomicNet achieves a Dice Similarity Coefficient (DSC) of 0.763 +/- 0.231 on the Breast Ultrasound Images (BUSI) dataset and 0.854 +/- 0.112 on Kvasir-SEG, outperforming U-KAN by 1.2% and 1.8%, respectively (p < 0.05, Wilcoxon signed-rank test), with only 3.27M parameters, 9.5x fewer than standard U-Net and 4.3x fewer than U-KAN. Gradient-based feature importance analysis reveals that GLCM dissimilarity (15.24%), GLCM energy (14.56%), and LBP entropy (11.49%) are the dominant radiomics cues, providing clinically meaningful explanations for segmentation decisions. The proposed approach demonstrates that compact, interpretable models grounded in domain knowledge can deliver state-of-the-art segmentation performance with substantially reduced computational overhead.

Mohammad Amanour Rahman

Department of Computer Science and Engineering Ahsanullah University of Science and Technology (AUST) Dhaka, Bangladesh

Ultrasound Image SegmentationMedical Image SegmentationExplainable Medical Image AnalysisRadiomics
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Jul 2, 2026

Population-Scale Segmentation of Penile Tissue in DIXON MRI using Deep Learning for Quantitative Phenotyping in Male Reproductive Health

Penile measurement is clinically relevant across male reproductive and urogenital health, including conditions such as micropenis, congenital and endocrine disorders, and sexual or urinary dysfunction. However, quantitative assessment of penile size has relied mainly on external length or circumference measurements, which are difficult to standardize, sensitive to measurement conditions, and unable to capture the internal portion of the penis. MRI enables volumetric assessment of the whole penis in vivo, but automated segmentation has not previously been established at population scale. Automated whole-organ volumetry would enable high-throughput phenotyping for multi-omics and clinical studies of male reproductive disease. Here, we present a deep learning framework for whole-penis segmentation in multi-channel DIXON MRI. Using a newly curated expert-annotated training dataset (n=145n = 145 subjects; 13,05013,050 annotated slices) and a double-annotated independent test benchmark (n=24n = 24 subjects; 2,1602,160 double-annotated slices), we optimized a 3D nnU-Net architecture. The model achieved a 5-fold cross-validation Dice score of 0.900.90 and performed at observer-level accuracy on the independent test set (Dice: 0.920.92; Hausdorff distance: 3.583.58). We deployed the model in 34,41234,412 UK Biobank participants, enabling automated quantification of total penile tissue, including both external and internal components. Longitudinal evaluation in 2,282 men demonstrated high inter-session reproducibility (r=0.87r = 0.87). This framework establishes a reproducible and population-scalable method for MRI-based assessment of penile anatomy and provides an open technical resource for future studies in urological imaging and male reproductive health. The trained model weights will be publicly released.

Jan Ernsting, Gunnar Paul Kordes, Nils Johannaber +5

University of Münster Germany

HealthcareMagnetic Resonance ImagingMedical Image Segmentation3D Medical Image Segmentation
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Jul 2, 2026

Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision

Due to the scarcity of expert-annotated data, Semi-Supervised Medical Image Segmentation (SSMIS) has emerged as a promising approach. Many anatomical structures in medical images exhibit significant intra-class heterogeneity, with different regions showing heterogeneous intensity patterns within the same structure. However, existing methods inadequately exploit this intensity-manifested intra-class heterogeneity, resulting in uniform structural representations and imprecise segmentation. Furthermore, the scarcity of labeled data makes it more difficult to effectively capture such complex heterogeneity. To address this, we propose Multiple Prototype Contrastive Learning (MPCL), an SSMIS framework that possesses better diversity and better precision. It consists of three novel designs: First, we provide structural representations with better diversity and propose Intensity-aligned Heterogeneous Prototype Generation (IHPG) that effectively models intra-class heterogeneity by generating multiple prototypes aligned with intensity characteristics. Second, we further enhance more diverse structural representations and build a solid foundation for more precise segmentation through Prototypical Space Optimization (PSO) that systematically optimizes a more discriminative and generalizable prototypical space. Finally, we achieve segmentation results with better precision through Dual-branch Knowledge Alignment (DKA) that efficiently promotes intra-class heterogeneity knowledge transfer from prototypical space to the segmentation network. Extensive experiments on three medical image datasets with significant intra-class heterogeneity demonstrate that MPCL significantly outperforms existing methods, especially under extremely limited labeled data.

Yuqi Liu, Yufei Chen, Wei Fu +2

Prototype-Based LearningContrastive LearningSemi-Supervised Medical Image SegmentationMedical Image Segmentation
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Jul 2, 2026

HistoSeg++: Delving deeper with attention and multiscale feature fusion for biomarker segmentation

Segmentation of biomarkers in medical images is frequently viewed as a first step towards medical image analysis in any bioinformatics or biomedical application. Despite progress, existing methods still struggle to capture information at multiple scales and to perform upsampling effectively across different datasets. These shortcomings often result in suboptimal generalization capabilities. Recently, architectures belonging to the Nested-UNet family excel in capturing multiscale contextual information and upsample them effectively. In this work, We propose a novel Nested-UNet architecture that effectively captures multi-scale contextual information. It includes inner and outer attention units to enhance focus during upsampling, along with channel-wise feature recalibration using squeeze-and-excitation modules, leading to improved segmentation performance. Additionally, the architecture integrates an edge-aware loss to emphasize boundary accuracy by assigning greater importance to edge regions. Tested extensively on three publicly available benchmark datasets. Our method demonstrates a generalization performance superior to existing Nested-UNet methods. Code: https://github.com/saadwazir/histosegplusplus

Saad Wazir, Rao Faizan, Daeyoung Kim

School of Computing, KAIST Daejeon, Republic of Korea · Kyung Hee University (Global Campus) Yongin-si, Republic of Korea

U-NetMulti-Scale Feature FusionMedical Image SegmentationAttention Mechanisms
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Jul 1, 2026

MedCAGD: Context-Aware Gated Decoder for Efficient Medical Image Segmentation

Medical image segmentation relies on the ability of encoder-decoder architectures to translate rich feature representations into accurate pixel-level predictions under challenging conditions such as low contrast, structural ambiguity, and scale variability. While recent advances in large-scale pretraining and transformer-based encoders have substantially improved feature extraction, segmentation accuracy remains constrained by decoder design, particularly in terms of cross-scale alignment, contextual integration, and boundary preservation. In this work, we revisit medical image segmentation from a decoder-centric perspective and propose a context-aware gated decoder that systematically regulates feature fusion and contextual aggregation throughout the decoding process. The proposed decoder integrates lightweight multi-scale channel recalibration, gated skip fusion with spatial competition and a global context aggregation mechanism that injects encoder-wide information into intermediate decoding stages. This design enables effective translation of strong pretrained encoder representations into spatially consistent predictions. Extensive experiments across 11 medical image segmentation benchmarks validate the effectiveness and demonstrate that the proposed approach consistently outperforms strong baselines while remaining computationally practical. Code: https://github.com/saadwazir/MedCAGD

Saad Wazir, Patrick Dominique Vibild, Dinh Phu Tran +2

School of Computing, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea · Department of Energy, Aalborg University, Aalborg, Denmark

Medical Image Segmentation
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Jun 30, 2026

Does Your ViT Still Need U-Net for Segmentation?

Medical image segmentation is dominated by U-Net-style encoder-decoder architectures. Vision Transformers (ViTs) overcome the limited receptive field of convolutional networks through self-attention, enabling modeling of long-range dependencies. Early ViT-based segmentation methods typically retained U-Net-style decoders because pretrained ViT representations were insufficient to support accurate dense prediction. Recent advances in large-scale pretraining have redefined the representation capability of ViTs, reducing the reliance on U-Net-style decoder architectures in modern vision models. This prompts two questions: Is the U-Net paradigm still necessary for medical image segmentation? If not, how should an encoder-only segmentation framework be designed? Motivated by these questions, we explore key architectural choices for encoder-only medical image segmentation based on modern ViT backbones and establish a query-based encoder-only design with multi-level query modeling and learnable block fusion, realized in Encoder-only Segmentation (EoSeg). Extensive experiments across seven benchmark datasets spanning CT, MRI, histopathology, endoscopy, and dermoscopy validate the effectiveness of the proposed design across diverse medical imaging modalities, including mDice scores of 85.50% on Synapse, 91.73% on ACDC, and 93.27% on GlaS. The results demonstrate that a U-Net-style decoder is no longer necessary for medical image segmentation with modern ViT backbones and further show that EoSeg provides an effective encoder-only design. Code is available at: https://github.com/Retinal-Research/EoSeg

Xin Li, Wenhui Zhu, Xuanzhao Dong +6

Arizona State University, Tempe, AZ, USA · Clemson University, Clemson, SC, USA · Mayo Clinic, Scottsdale, AZ, USA

Vision TransformerU-NetMedical Image Segmentation
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Jun 30, 2026

Trust the Prior (or Not): Uncertainty-Aware Abdominal Aortic Aneurysm Segmentation

Robust segmentation of intraluminal thrombus is critical for risk assessment in Abdominal Aortic Aneurysm, yet it remains challenging due to heterogeneous thrombus features and low contrast with surrounding non-enhanced tissues. Domain shifts induced by different Computed Tomography Angiography (CTA) protocols further inhibit multi-center generalization of deep learning models. To address these challenges, we propose a patient-specific framework that integrates discriminative learning with anatomically informed priors. Our approach introduces two key components: (1) a patient-specific intensity normalization based on a Gaussian Mixture Model of local anatomy, and (2) an Uncertainty-Gated Anatomical Attention module that incorporates spatial priors while adaptively modulating their influence according to voxel-wise confidence. This design allows for anatomical guidance in ambiguous regions while suppressing unreliable priors. The proposed method achieves state-of-the-art performance on in-distribution test data and substantially outperforms existing alternatives in generalization to external multi-center CTA data, while remaining interpretable through an explicit separation of visual and anatomical evidence.

Erich Robbi, Daniele Ravanelli, Andrea Passerini

Department of Computer Science University of Trento Trento, Italy · Medical Physics Unit Santa Chiara Hospital Trento, Italy

Domain GeneralizationMedical Image SegmentationSegmentation Uncertainty Estimation
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