In interventional radiology, Cone-Beam Computed Tomography (CBCT) is a helpful imaging modality that provides guidance to practicians during minimally invasive procedures. CBCT differs from traditional Computed Tomography (CT) due to its limited reconstructed field of view, specific artefacts, and the intra-arterial administration of contrast medium. While CT benefits from abundant publicly available annotated datasets, interventional CBCT data remain scarce and largely unannotated, with existing datasets focused primarily on radiotherapy applications. To address this limitation, we leverage a proprietary collection of unannotated interventional CBCT scans in conjunction with annotated CT data, employing domain adaptation techniques to bridge the modality gap and enhance liver segmentation performance on CBCT. We propose a novel unsupervised domain adaptation (UDA) framework based on the formalism of Margin Disparity Discrepancy (MDD), which improves target domain performance through a reformulation of the original MDD optimization framework. Experimental results on CT and CBCT datasets for liver segmentation demonstrate that our method achieves state-of-the-art performance in UDA, as well as in the few-shot setting.
Figures & tables
Figure 1: Axial slices from abdominal CT (left, source) and CBCT (right, target) of the same patient using identical visualization windows. The liver is shown as a blue overlay. High-intensity regions within the liver caused by intra-arterial contrast enhancement in CBCT are marked with yellow arrows.
Figure 2: Our feature-alignment UDA for segmentation. A U-Net is decomposed into a feature extractor ψ , and a segmentation head f . An adversary f′ is built as a duplication of f for adversarial training during UDA. After UDA, the features zS=ψ(xS) and zT=ψ(xT) are supposed to be domain-invariant, and f′ is removed at inference. The loss Ltask is the supervision segmentation loss, and LCE is used for UDA.
Type
Method
F1 (%) ↑
Fully supervised
Source Only
54.1
Target Only (1 vol)
54.3 ± 3.7
Target Only (5 vol)
68.1 ± 3.9
Target Only (20 vol)
77.4 ± 0.6
Target Only (50 vol)
81.4 ± 0.5
Target Only (100%)
85.5
Table 1: 2D CT to CBCT segmentation F1 scores for various methods. Best results are highlighted in bold . Standard deviations are reported for experiments repeated 5 times due to randomness in selecting a point for foundation model prompts or randomly choosing slices in few-shot settings.
Type
Method
F1 (%) ↑
Fully supervised
Source Only
80.1
Target Only (1 vol)
61.0 ± 1.7
Target Only (5 vol)
84.7 ± 0.9
Target Only (20 vol)
89.6 ± 0.6
Target Only (100%)
93.7
Foundation model
SAM-MED 3D [ 8 ] (1 pt)
53.6 ± 0.3
Table 2: 3D CT to CBCT segmentation F1 scores for various methods. Best results are highlighted in bold . Standard deviations are reported for experiments repeated 5 times due to randomness in selecting a point for foundation model prompts or randomly choosing scans in few-shot settings.
Figure 3: Segmentation results from various 3D methods. Ground truth mask is shown in red, and predicted segmentations in blue. High-intensity regions within the liver that cause the networks to fail are marked with yellow arrows. The same axial slice is displayed for each method with the corresponding prediction overlaid. The SAM-MED 3D prediction was generated using 5 randomly sampled points. The F1 score above each method, reported as a percentage, represents the 3D F1 score computed across the entire volume.
Figure 4: Stability Analysis of Hyperparameters in 2D CT to CBCT Unsupervised Domain Adaptation (UDA). This figure reports the F1 scores obtained on the target domain test set for different combinations of the hyperparameters α and γ , with F1 values presented as percentages. The consistent performance across varying values of α and γ confirms the robustness and stability of our method with respect to these hyperparameters.
Accurate 3D segmentation of cone-beam CT (CBCT) is critical for interventional and radiation therapy applications, yet it remains limited by two compounding challenges: the scarcity of annotated CBCT data and the large domain shift from diagnostic CT. Interventional CBCT exhibits fundamental modality differences from conventional CT, driven by acquisition and physics effects as well as contrast-specific vascular content, thereby limiting effective cross-modality model transfer. We propose a novel unsupervised domain adaptation (UDA) framework based on redundancy-reducing feature alignment, enabling 3D CBCT segmentation with no target-domain annotations or inference-time adaptation. Our framework is architecture-agnostic, seamlessly adapting both CNN-based and ViT-based foundation models. We evaluate our method on two challenging CT-CBCT liver segmentation benchmarks: one for interventional vascular procedures and one for radiation therapy, demonstrating that even large-scale pretrained segmentation networks require explicit feature-space bridging to generalize across acquisition modalities, and that our approach consistently outperforms existing pretrained foundation model and UDA strategies. To support reproducibility and benchmarking, we release the liver segmentations for a public CBCT dataset, along with the code, trained models, and weights.
Gauthier Miralles, Loic Le Folgoc, Vincent Jugnon +1
LTCI, Télécom Paris, Institut Polytechnique de Paris, Palaiseau, France · GE HealthCare, Buc, France
In mixed domain semi-supervised medical image segmentation (MiDSS), achieving superior performance under domain shift and limited annotations is challenging. This scenario presents two primary issues: (1) distributional differences between labeled and unlabeled data hinder effective knowledge transfer, and (2) inefficient learning from unlabeled data causes severe confirmation bias. In this paper, we propose the bidirectional correlation maps domain adaptation (BCMDA) framework to overcome these issues. On the one hand, we employ knowledge transfer via virtual domain bridging (KTVDB) to facilitate cross-domain learning. First, to construct a distribution-aligned virtual domain, we leverage bidirectional correlation maps between labeled and unlabeled data to synthesize both labeled and unlabeled images, which are then mixed with the original images to generate virtual images using two strategies, a fixed ratio and a progressive dynamic MixUp. Next, dual bidirectional CutMix is used to enable initial knowledge transfer within the fixed virtual domain and gradual knowledge transfer from the dynamically transitioning labeled domain to the real unlabeled domains. On the other hand, to alleviate confirmation bias, we adopt prototypical alignment and pseudo label correction (PAPLC), which utilizes learnable prototype cosine similarity classifiers for bidirectional prototype alignment between the virtual and real domains, yielding smoother and more compact feature representations. Finally, we use prototypical pseudo label correction to generate more reliable pseudo labels. Empirical evaluations on three public multi-domain datasets demonstrate the superiority of our method, particularly showing excellent performance even with very limited labeled samples. Code available at https://github.com/pascalcpp/BCMDA.
Bentao Song, Jun Huang, Qingfeng Wang
School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, Sichuan, 621010, China
Robust medical image segmentation across imaging modalities is challenging because of large differences in appearance and intensity distributions. Models trained on a single modality often show substantial performance drops when applied to unseen domains. In this work, we develop a unified 3D pancreas segmentation framework that applies domain-adversarial learning to 4,604 heterogeneous CT and MRI scans to learn anatomical representations. A shared nnU-Net encoder-decoder is trained for whole-pancreas segmentation, with a latent domain discriminator encouraging CT-MRI feature alignment. The learned encoder is subsequently transferred to pancreatic head-body-tail segmentation using limited MRI-only subregion annotations. An average Dice score of 87.31% on the in-distribution test set and Dice scores ranging from 84.20% to 88.09% across external OOD datasets were achieved in whole pancreas segmentation. Dice scores of 80.53% on MRI and 83.05% on CT were achieved for downstream subregion segmentation, without using CT subregion annotations. These results demonstrate that a unified anatomical representation can support both cross-modality pancreas segmentation and label-efficient downstream transfer.
Ziliang Hong, Hongyi Pan, Halil Ertugrul Aktas +7
Department of Radiology, Northwestern University, Chicago, IL, USA · Division of Gastroenterology and Hepatology, Mayo Clinic Florida, Jacksonville, FL, USA · Department of Gastroenterology and Hepatology, Northwestern University, Chicago, IL, USA