Medical Image Localization

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Overview

Medical image localization studies how to identify where anatomical landmarks, lesions, organs, and devices appear in 2D and 3D scans or clinical videos, supporting diagnosis, measurement, and image-guided procedures. Common approaches include heatmap and coordinate regression, object detection and segmentation, anatomical priors, weakly or self-supervised learning, temporal tracking, registration, and vision-language grounding. Methods are evaluated across X-ray, CT, MRI, ultrasound, pathology, and endoscopy using landmark error, detection and overlap scores, and tests on external sites; some also guide robotic scanning or surgery. A central challenge is that strong average results can mask failures on small or rare targets, across modalities or hospitals, and under strict 3D alignment, while reliable uncertainty and clinical integration remain open problems.

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104 papers11 papers in the last four weeks, up 10% on the four weeks before. 0.1% of all new papers.

Mar 2, 2026

UltraStar: Semantic-Aware Star Graph Modeling for Echocardiography Navigation

Echocardiography is critical for diagnosing cardiovascular diseases, yet the shortage of skilled sonographers hinders timely patient care, due to high operational difficulties. Consequently, research on automated probe navigation has significant clinical potential. To achieve robust navigation, it is essential to leverage historical scanning information, mimicking how experts rely on past feedback to adjust subsequent maneuvers. Practical scanning data collected from sonographers typically consists of noisy trajectories inherently generated through trial-and-error exploration. However, existing methods typically model this history as a sequential chain, forcing models to overfit these noisy paths, leading to performance degradation on long sequences. In this paper, we propose UltraStar, which reformulates probe navigation from path regression to anchor-based global localization. By establishing a Star Graph, UltraStar treats historical keyframes as spatial anchors connected directly to the current view, explicitly modeling geometric constraints for precise positioning. We further enhance the Star Graph with a semantic-aware sampling strategy that actively selects the representative landmarks from massive history logs, reducing redundancy for accurate anchoring. Extensive experiments on a dataset with over 1.31 million samples demonstrate that UltraStar outperforms baselines and scales better with longer input lengths, revealing a more effective topology for history modeling under noisy exploration. Code is available at https://github.com/LeapLabTHU/UltraStar.

Teng Wang, Haojun Jiang, Chenxi Li +6

Department of Automation, BNRist, Tsinghua University, Beijing, China · School of Automation, Beijing Institute of Technology, Beijing, China · Beijing Academy of Artificial Intelligence, Beijing, China +1

EchocardiographyUltrasound ImagingRobot LocalizationMedical Image Localization
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Feb 26, 2026

GazeXPErT: An Expert Eye-tracking Dataset for Interpretable and Explainable AI in Oncologic FDG-PET/CT Scans

[18F]FDG-PET/CT is a cornerstone imaging modality for guiding oncology therapies, yet human expert shortages necessitate more efficient diagnostic aids. While standalone AI models for automatic lesion detection exist, clinical translation remains hindered by AI explainability, reliability, and workflow integration. Meanwhile, human-computer-interaction in radiology remain limited to keyboard, mouse and voice, ignoring experts' faster, natural gaze signal. We present GazeXPErT, a 4D eye-tracking dataset with annotated expert decision windows for tumor detection and measurement on 346 dual-read FDG-PET/CTs. The dataset contributes 9,030 gaze-to-lesion trajectories derived from 3,948 minutes of 60 Hz eye-tracking data, rendered in COCO-style format. GazeXPErT captures experts' visual reasoning patterns when adjudicating suspicious lesions. It aims to facilitate development of trusted, explainable and interactive AI models through understanding expert gaze patterns. Baseline feasibility experiments suggest salient signal is extractable from routinely collected expert gaze (3D nnU-Net Dice: 0.6819 versus 0.6008 without), that gaze-trained vision transformers may aid dynamic lesion localization (74.95% predicted gaze closer to tumor), and that experts' intent may be predictable from raw gaze (Accuracy 67.53%, AUROC 0.747).

Joy T Wu, Daniel Beckmann, Sarah Miller +15

Stanford University/Stanford Health Care, Radiology Department, Stanford, CA 94305, USA · University of Münster, Computer Vision and Machine Learning Systems, Heisenbergstraße 2, Münster 48149, Germany · University of Illinois Urbana-Champaign, School of Information Sciences, Champaign, IL 61820, USA +1

Explainable Medical Image AnalysisMedical Image BenchmarksMedical Image LocalizationPET/CT Imaging
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Feb 25, 2026

Following the Diagnostic Trace: Visual Cognition-guided Cooperative Network for Chest X-Ray Diagnosis

Computer-aided diagnosis (CAD) has significantly advanced automated chest X-ray diagnosis but remains isolated from clinical workflows and lacks reliable decision support and interpretability. Human-AI collaboration seeks to enhance the reliability of diagnostic models by integrating the behaviors of controllable radiologists. However, the absence of interactive tools seamlessly embedded within diagnostic routines impedes collaboration, while the semantic gap between radiologists' decision-making patterns and model representations further limits clinical adoption. To overcome these limitations, we propose a visual cognition-guided collaborative network (VCC-Net) to achieve the cooperative diagnostic paradigm. VCC-Net centers on visual cognition (VC) and employs clinically compatible interfaces, such as eye-tracking or the mouse, to capture radiologists' visual search traces and attention patterns during diagnosis. VCC-Net employs VC as a spatial cognition guide, learning hierarchical visual search strategies to localize diagnostically key regions. A cognition-graph co-editing module subsequently integrates radiologist VC with model inference to construct a disease-aware graph. The module captures dependencies among anatomical regions and aligns model representations with VC-driven features, mitigating radiologist bias and facilitating complementary, transparent decision-making. Experiments on the public datasets SIIM-ACR, EGD-CXR, and self-constructed TB-Mouse dataset achieved classification accuracies of 88.40%, 85.05%, and 92.41%, respectively. The attention maps produced by VCC-Net exhibit strong concordance with radiologists' gaze distributions, demonstrating a mutual reinforcement of radiologist and model inference. The code is available at https://github.com/IPMI-NWU/VCC-Net.

Shaoxuan Wu, Jingkun Chen, Chong Ma +3

College of Computer Science, Northwest University, Xi’an, China, 710127 · Department of Engineering Science, University of Oxford, Oxford, OX3 7DQ, United Kingdom · School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China +1

Medical Image AnalysisChest X-Ray ClassificationHuman-AI CollaborationExplainable Medical Image AnalysisMedical Image Localization
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Feb 22, 2026

Detector-in-the-Loop Tracking: Active Memory Rectification for Stable Glottic Opening Localization

Temporal stability in glottic opening localization remains challenging due to the complementary weaknesses of single-frame detectors and foundation-model trackers: the former lacks temporal context, while the latter suffers from memory drift. Specifically, in video laryngoscopy, rapid tissue deformation, occlusions, and visual ambiguities in emergency settings require a robust, temporally aware solution that can prevent progressive tracking errors. We propose Closed-Loop Memory Correction (CL-MC), a detector-in-the-loop framework that supervises Segment Anything Model 2(SAM2) through confidence-aligned state decisions and active memory rectification. High-confidence detections trigger semantic resets that overwrite corrupted tracker memory, effectively mitigating drift accumulation with a training-free foundation tracker in complex endoscopic scenes. On emergency intubation videos, CL-MC achieves state-of-the-art performance, significantly reducing drift and missing rate compared with the SAM2 variants and open loop based methods. Our results establish memory correction as a crucial component for reliable clinical video tracking. Our code will be available in https://github.com/huayuww/CL-MR.

Huayu Wang, Bahaa Alattar, Cheng-Yen Yang +5

University of Washington, Seattle, WA · Ajou University School of Medicine, Suwon, Republic of Korea · Harborview Medical Center, Seattle, WA +1

Visual Object TrackingMedical Image LocalizationSegment Anything ModelsEndoscopy
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Feb 22, 2026

Artefact-Aware Fungal Detection in Dermatophytosis: A Real-Time Transformer-Based Approach for KOH Microscopy

Dermatophytosis is commonly assessed using potassium hydroxide (KOH) microscopy, yet accurate recognition of fungal hyphae is hindered by artefacts, heterogeneous keratin clearance, and notable inter-observer variability. This study presents a transformer-based detection framework using the RT-DETR model architecture to achieve precise, query-driven localization of fungal structures in high-resolution KOH images. A dataset of 2,540 routinely acquired microscopy images was manually annotated using a multi-class strategy to explicitly distinguish fungal elements from confounding artefacts. The model was trained with morphology-preserving augmentations to maintain the structural integrity of thin hyphae. Evaluation on an independent test set demonstrated robust object-level performance, with a recall of 0.9737, precision of 0.8043, and an [email protected] of 93.56%. When aggregated for image-level diagnosis, the model achieved 100% sensitivity and 98.8% accuracy, correctly identifying all positive cases without missing a single diagnosis. Qualitative outputs confirmed the robust localization of low-contrast hyphae even in artefact-rich fields. These results highlight that an artificial intelligence (AI) system can serve as a highly reliable, automated screening tool, effectively bridging the gap between image-level analysis and clinical decision-making in dermatomycology.

Rana Gursoy, Abdurrahim Yilmaz, Baris Kizilyaprak +5

Department of Mechatronics Engineering, Yildiz Technical University · Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London · Department of Dermatology and Venereology, Istanbul Research and Training Hospital +2

Microscopy Image AnalysisMedical Image Localization
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Feb 18, 2026

Towards Autonomous Robotic Kidney Ultrasound: Spatial-Efficient Volumetric Imaging via Template Guided Optimal Pivoting

Medical ultrasound (US) imaging is a frontline tool for the diagnosis of kidney diseases. However, traditional freehand imaging procedure suffers from inconsistent, operator-dependent outcomes, lack of 3D localization information, and risks of work-related musculoskeletal disorders. While robotic ultrasound (RUS) systems offer the potential for standardized, operator-independent 3D kidney data acquisition, the existing scanning methods lack the ability to determine the optimal imaging window for efficient imaging. As a result, the scan is often blindly performed with excessive probe footprint, which frequently leads to acoustic shadowing and incomplete organ coverage. Consequently, there is a critical need for a spatially efficient imaging technique that can maximize the kidney coverage through minimum probe footprint. Here, we propose an autonomous workflow to achieve efficient kidney imaging via template-guided optimal pivoting. The system first performs an explorative imaging to generate partial observations of the kidney. This data is then registered to a kidney template to estimate the organ pose. With the kidney localized, the robot executes a fixed-point pivoting sweep where the imaging plane is aligned with the kidney long axis to minimize the probe translation. The proposed method was validated in simulation and in-vivo. Simulation results indicate that a 60% exploration ratio provides optimal balance between kidney localization accuracy and scanning efficiency. In-vivo evaluation on two male subjects demonstrates a kidney localization accuracy up to 7.36 mm and 13.84 degrees. Moreover, the optimal pivoting approach shortened the probe footprint by around 75 mm when compared with the baselines. These results valid our approach of leveraging anatomical templates to align the probe optimally for volumetric sweep.

Xihan Ma, Haichong Zhang

Robotic UltrasoundUltrasound ImagingMedical Image Localization
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Feb 16, 2026

MB-DSMIL-CL-PL: Scalable Weakly Supervised Ovarian Cancer Subtype Classification and Localisation Using Contrastive and Prototype Learning with Frozen Patch Features

The study of histopathological subtypes is valuable for the personalisation of effective treatment strategies for ovarian cancer. However, increasing diagnostic workloads present a challenge for UK pathology departments, leading to the rise in AI approaches. While traditional approaches in this field have relied on pre-computed, frozen image features, recent advances have shifted towards end-to-end feature extraction, providing an improvement in accuracy but at the expense of significantly reduced scalability during training and time-consuming experimentation. In this paper, we propose a new approach for subtype classification and localisation in ovarian cancer histopathology images using contrastive and prototype learning with pre-computed, frozen features via feature-space augmentations. Compared to DSMIL, our method achieves an improvement of 70.4% and 15.3% in F1 score for instance- and slide-level classification, respectively, along with AUC gains of 16.9% for instance localisation and 2.3% for slide classification, while maintaining the use of frozen patch features.

Marcus Jenkins, Jasenka Mazibrada, Bogdan Leahu +1

School of Computing Sciences, University of East Anglia, Norwich, Norfolk, NR4 7TJ, UK · Norfolk and Norwich University Hospital, Norwich, Norfolk, NR4 7UY, UK

Histopathology Image ClassificationCancer Subtype ClassificationMedical Image LocalizationMultiple Instance LearningPrototype-Based ClassificationContrastive Learning
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Feb 16, 2026

CT-Bench: A Benchmark for Multimodal Lesion Understanding in Computed Tomography

Artificial intelligence (AI) can automatically delineate lesions on computed tomography (CT) and generate radiology report content, yet progress is limited by the scarcity of publicly available CT datasets with lesion-level annotations. To bridge this gap, we introduce CT-Bench, a first-of-its-kind benchmark dataset comprising two components: a Lesion Image and Metadata Set containing 20,335 lesions from 7,795 CT studies with bounding boxes, descriptions, and size information, and a multitask visual question answering benchmark with 2,850 QA pairs covering lesion localization, description, size estimation, and attribute categorization. Hard negative examples are included to reflect real-world diagnostic challenges. We evaluate multiple state-of-the-art multimodal models, including vision-language and medical CLIP variants, by comparing their performance to radiologist assessments, demonstrating the value of CT-Bench as a comprehensive benchmark for lesion analysis. Moreover, fine-tuning models on the Lesion Image and Metadata Set yields significant performance gains across both components, underscoring the clinical utility of CT-Bench.

Qingqing Zhu, Qiao Jin, Tejas S. Mathai +10

Medical Image BenchmarksComputed TomographyMedical VQAMedical Image LocalizationMedical VLMs
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Dec 18, 2025

CLARiTy: A Vision Transformer for Multi-Label Classification and Weakly-Supervised Localization of Chest X-ray Pathologies

The interpretation of chest X-rays poses significant challenges in achieving accurate multi-label pathology classification and spatial localization. These tasks demand different annotation granularities but are constrained by the scarcity of region-level (dense) annotations. We introduce CLARiTy (Class Localizing and Attention Refining Image Transformer), a vision transformer-based model for joint multi-label classification and weakly-supervised localization of thoracic pathologies. CLARiTy employs multiple class-specific tokens to generate discriminative attention maps and a SegmentCAM module for foreground segmentation and background suppression using explicit anatomical priors. Trained on image-level labels from the NIH ChestXray14 dataset, it leverages distillation from a ConvNeXtV2 teacher for efficiency. Localization heatmaps are converted to bounding boxes with OneBox, a hysteresis-gated extractor whose parameters are calibrated per finding on held-out validation data. Evaluated on the official NIH split, CLARiTy achieves competitive classification across 14 pathologies and strong weakly-supervised localization on 8 pathologies, outperforming prior methods by up to 50.7% relative Macro Intersection over Union (IoU) Accuracy at stringent thresholds. Gains are most pronounced for small pathologies: at T(IoU) = 0.5, the 224 variant reaches an IoU Accuracy of 0.154 on nodules, where the compared methods remain at or below 0.013. The lower-resolution CLARiTy-S-16-224 offers high efficiency while surpassing baselines, and could potentially be applied in low-resource settings. An ablation study confirms the contributions of SegmentCAM, DINO pretraining, and attention pooling. CLARiTy advances beyond CNN-ViT hybrids by harnessing ViT self-attention for global context and class-specific localization, refined through convolutional background suppression for precise, noise-reduced heatmaps.

John M. Statheros, Hairong Wang, Richard Klein

School of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg, South Africa

Chest X-Ray ClassificationMedical Image LocalizationVision Transformer
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Sep 26, 2025

RAU: Reference-based Anatomical Understanding with Vision Language Models

Anatomical understanding, which is the ability to identify, localize, or segment anatomical structures, is critical in medical image analysis; however, its progress is constrained by the scarcity of expert-labeled data. A promising remedy is to leverage an annotated reference image to guide the interpretation of an unlabeled target. Although recent vision-language models (VLMs) exhibit non-trivial visual reasoning, their reference-based understanding and fine-grained localization remain limited. We introduce RAU, a framework for reference-based anatomical understanding with VLMs. We first show that a VLM learns to identify anatomical regions through relative spatial reasoning between reference and target images, trained on a moderately sized dataset. We validate this capability through visual question answering (VQA) and bounding box prediction. Next, we demonstrate that the VLM-derived spatial cues can be seamlessly integrated with the fine-grained segmentation capability of SAM2, enabling localization and pixel-level segmentation of small anatomical regions, such as vessel segments. Across two in-distribution and two out-of-distribution datasets, RAU consistently outperforms a SAM2 fine-tuning baseline using the same memory setup, yielding more accurate segmentations and more reliable localization. More importantly, its generalization ability to unseen modalities makes it scalable to unseen datasets, a property crucial for medical image applications. To the best of our knowledge, RAU is the first to explore the capability of VLMs for reference-based identification, localization, and segmentation of anatomical structures in medical images. Its promising performance highlights the potential of VLM-driven approaches for anatomical understanding in automated clinical workflows.

Yiwei Li, Yikang Liu, Jiaqi Guo +7

United Imaging Intelligence, Boston, MA, USA · School of Computing, University of Georgia, Athens, GA, USA · Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, USA

Image SegmentationMedical VLMsMedical Image SegmentationMedical Image Localization
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Mar 21, 2025

Echo-E3^3Net: Efficient Endocardial Spatio-Temporal Network for Ejection Fraction Estimation

Left ventricular ejection fraction (LVEF) is a primary marker of cardiac function. However, routine estimation from endocardial measurements requires manual delineation at end-diastole (ED) and end-systole (ES), a process that is time-consuming and subject to inter-observer variability. Reliable automation is especially valuable for point-of-care ultrasound (POCUS), where computational resources are limited and acquisition quality varies. We propose Echo-E3^3Net, an anatomy-guided spatio-temporal network that explicitly embeds cardiac anatomy into LVEF prediction. A dual-phase Endocardial Border Detector (E2^2CBD) uses phase-specific cross-attention to localize ED/ES endocardial landmarks and produce phase-aware landmark embeddings, while an Endocardial Feature Aggregator (E2^2FA) fuses these embeddings with global statistical descriptors of deep feature maps to refine EF regression. Training is guided by a lightweight geometric loss that uses ED and ES endocardial landmarks to regularize EF prediction. On EchoNet-Dynamic and a PSAX subset of EchoNet-Pediatric, Echo-E3^3Net attains competitive performance using only 1.55M parameters and 8.05 GFLOPs, an order-of-magnitude compute reduction versus recent baselines, supporting real-time deployment. Our code is publicly available at https://github.com/moeinheidari7829/Echo-E3Net.

Moein Heidari, Afshin Bozorgpour, AmirHossein Zarif-Fakharnia +5

University of British Columbia, Vancouver, BC, Canada · University of Regensburg, Regensburg, Germany · Iran University of Science and Technology, Tehran, Iran +1

Efficient Neural Network InferenceLeft Ventricular Ejection Fraction EstimationMedical Image AnalysisEchocardiographyUltrasound ImagingMedical Image Localization
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Oct 6, 2024

Optimising for the Unknown: Domain Alignment for Cephalometric Landmark Detection

Cephalometric Landmark Detection is the process of identifying key areas for cephalometry. Each landmark is a single GT point labelled by a clinician. A machine learning model predicts the probability locus of a landmark represented by a heatmap. This work, for the 2024 CL-Detection MICCAI Challenge, proposes a domain alignment strategy with a regional facial extraction module and an X-ray artefact augmentation procedure. The challenge ranks our method's results as the best in MRE of 1.186mm and third in the 2mm SDR of 82.04% on the online validation leaderboard. The code is available at https://github.com/Julian-Wyatt/OptimisingfortheUnknown.

Julian Wyatt, Irina Voiculescu

Department of Computer Science, University of Oxford

Anatomical Landmark DetectionMedical Image LocalizationDomain Adaptation
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Oct 11, 2023

Precise localization within the GI tract by combining classification of CNNs and time-series analysis of HMMs

This paper presents a method to efficiently classify the gastroenterologic section of images derived from Video Capsule Endoscopy (VCE) studies by exploring the combination of a Convolutional Neural Network (CNN) for classification with the time-series analysis properties of a Hidden Markov Model (HMM). It is demonstrated that successive time-series analysis identifies and corrects errors in the CNN output. Our approach achieves an accuracy of 98.04%98.04\% on the Rhode Island (RI) Gastroenterology dataset. This allows for precise localization within the gastrointestinal (GI) tract while requiring only approximately 1M parameters and thus, provides a method suitable for low power devices

Julia Werner, Christoph Gerum, Moritz Reiber +2

Department of Computer Science, University of Tübingen, Germany. · Department of Mathematics, ETH Zürich, Switzerland.

EndoscopyMedical ImagingMedical Image LocalizationHidden Markov Models
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Mar 3, 2022

Structure-Guided Self-Supervised Matching for One-Shot Medical Landmark Detection

Medical landmark detection usually requires accurate expert annotations, which are laborious and difficult to scale across anatomical regions. In this work, we study an extreme annotation-efficient setting where only a single annotated template image is available. We propose SGB-Match, a structure-guided coarse-to-fine self-supervised matching framework for one-shot medical landmark detection. The framework first learns dense anatomical correspondence from unlabeled augmented image pairs, and then transfers the landmark definition from the annotated template to each target image through feature matching. Different from standard contrastive correspondence learning, where negative candidates are penalized by a structure-agnostic rule, we introduce a structure-guided bias into the contrastive objective. The bias is constructed from relative distance and edge-aware anatomical cues, and explicitly reweights the negative gradients: nearby structure-relevant candidates are weakly repelled, while distant or structure-irrelevant negatives are strongly suppressed. As a result, the learned feature space better preserves local anatomical structures around template landmarks and reduces confusing responses from repeated textures. We further adopt a global-to-local design, where a global encoder provides coarse landmark localization and a local encoder refines the prediction in a cropped region. Extensive experiments on four 2D radiological landmark datasets demonstrate that SGB-Match achieves strong one-shot performance across both public and newly collected datasets and consistently benefits from both structure-guided bias and two-stage refinement.

Qingsong Yao, Zhen Huang, Ao Wang +4

Soochow School of Artificial Intelligence Renmin University of China · Department of Computer Science University of Science and Technology of China · School of Biomedical Engineering University of Science and Technology of China +3

Few-Shot LearningImage MatchingLocal Feature MatchingSelf-Supervised LearningMedical Image Representation LearningMedical Image Localization+1
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