Pub Date : 2026-02-10DOI: 10.1016/j.media.2026.103988
Ting Luo, Jinxian Zhang, Tao Chen, Zhouyan He, Yanda Meng, Mengting Liu, Jiong Zhang, Dan Zhang
Optical coherence tomography angiography (OCTA) enables non-invasive visualization of retinal microvasculature, and accurate 3D vessel segmentation is essential for quantifying biomarkers critical for early diagnosis and monitoring of diabetic retinopathy. However, reliable 3D OCTA segmentation is hindered by capillary invisibility, complex vascular topology, and motion artifacts, which compromise biomarker accuracy. Furthermore, the scarcity of manually annotated 3D OCTA microvascular data constrains methodological development. To address this challenge, we introduce our publicly accessible 3D microvascular dataset and propose MT-Net, a multi-view, topology-aware 3D retinal microvascular segmentation network. First, a novel dimension transformation strategy is employed to enhance topological accuracy by effectively encoding spatial dependencies across multiple planes. Second, to mitigate the impact of motion artifacts, we introduce a unidirectional Artifact Suppression Module (ASM) that selectively suppresses noise along the B-scan direction. Third, a Twin-Cross Attention Module (TCAM), guided by vessel centerlines, is designed to enhance the continuity and completeness of segmented vessels by reinforcing cross-view contextual information. Experiments on two 3D OCTA datasets show that MT-Net achieves state-of-the-art accuracy and topological consistency, with strong generalizability validated by cross-dataset analysis. We plan to release our manual annotations to facilitate future research in retinal OCTA segmentation.
{"title":"Artifact-suppressed 3D Retinal Microvascular Segmentation via Multi-scale Topology Regulation","authors":"Ting Luo, Jinxian Zhang, Tao Chen, Zhouyan He, Yanda Meng, Mengting Liu, Jiong Zhang, Dan Zhang","doi":"10.1016/j.media.2026.103988","DOIUrl":"https://doi.org/10.1016/j.media.2026.103988","url":null,"abstract":"Optical coherence tomography angiography (OCTA) enables non-invasive visualization of retinal microvasculature, and accurate 3D vessel segmentation is essential for quantifying biomarkers critical for early diagnosis and monitoring of diabetic retinopathy. However, reliable 3D OCTA segmentation is hindered by capillary invisibility, complex vascular topology, and motion artifacts, which compromise biomarker accuracy. Furthermore, the scarcity of manually annotated 3D OCTA microvascular data constrains methodological development. To address this challenge, we introduce our publicly accessible 3D microvascular dataset and propose MT-Net, a multi-view, topology-aware 3D retinal microvascular segmentation network. First, a novel dimension transformation strategy is employed to enhance topological accuracy by effectively encoding spatial dependencies across multiple planes. Second, to mitigate the impact of motion artifacts, we introduce a unidirectional Artifact Suppression Module (ASM) that selectively suppresses noise along the B-scan direction. Third, a Twin-Cross Attention Module (TCAM), guided by vessel centerlines, is designed to enhance the continuity and completeness of segmented vessels by reinforcing cross-view contextual information. Experiments on two 3D OCTA datasets show that MT-Net achieves state-of-the-art accuracy and topological consistency, with strong generalizability validated by cross-dataset analysis. We plan to release our manual annotations to facilitate future research in retinal OCTA segmentation.","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"11 1","pages":""},"PeriodicalIF":10.9,"publicationDate":"2026-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-09DOI: 10.1016/j.media.2026.103986
Yaqi Wang, Zhi Li, Chengyu Wu, Jun Liu, Yifan Zhang, Jiaxue Ni, Qian Luo, Jialuo Chen, Hongyuan Zhang, Jin Liu, Can Han, Kaiwen Fu, Changkai Ji, Xinxu Cai, Jing Hao, Zhihao Zheng, Shi Xu, Junqiang Chen, Xiaoyang Yu, Qianni Zhang, Dahong Qian, Shuai Wang, Huiyu Zhou
Orthopantomogram (OPGs) and Cone-Beam Computed Tomography (CBCT) are vital for dentistry, but creating large datasets for automated tooth segmentation is hindered by the labor-intensive process of manual instance-level annotation. This research aimed to benchmark and advance semi-supervised learning (SSL) as a solution for this data scarcity problem. We organized the 2nd Semi-supervised Teeth Segmentation (STS 2024) Challenge at MICCAI 2024. We provided a large-scale dataset comprising over 90,000 2D images and 3D axial slices, which includes 2,380 OPG images and 330 CBCT scans, all featuring detailed instance-level FDI annotations on part of the data. The challenge attracted 114 (OPG) and 106 (CBCT) registered teams. To ensure algorithmic excellence and full transparency, we rigorously evaluated the valid, open-source submissions from the top 10 (OPG) and top 5 (CBCT) teams, respectively. All successful submissions were deep learning-based SSL methods. The winning semi-supervised models demonstrated impressive performance gains over a fully-supervised nnU-Net baseline trained only on the labeled data. For the 2D OPG track, the top method improved the Instance Affinity (IA) score by over 44 percentage points. For the 3D CBCT track, the winning approach boosted the Instance Dice score by 61 percentage points. This challenge demonstrates the potential benefit benefit of SSL for complex, instance-level medical image segmentation tasks where labeled data is scarce. The most effective approaches consistently leveraged hybrid semi-supervised frameworks that combined knowledge from foundational models like SAM with multi-stage, coarse-to-fine refinement pipelines. Both the challenge dataset and the participants’ submitted code have been made publicly available on GitHub (https://github.com/ricoleehduu/STS-Challenge-2024), ensuring transparency and reproducibility.
{"title":"MICCAI STS 2024 Challenge: Semi-Supervised Instance-Level Tooth Segmentation in Panoramic X-ray and CBCT Images","authors":"Yaqi Wang, Zhi Li, Chengyu Wu, Jun Liu, Yifan Zhang, Jiaxue Ni, Qian Luo, Jialuo Chen, Hongyuan Zhang, Jin Liu, Can Han, Kaiwen Fu, Changkai Ji, Xinxu Cai, Jing Hao, Zhihao Zheng, Shi Xu, Junqiang Chen, Xiaoyang Yu, Qianni Zhang, Dahong Qian, Shuai Wang, Huiyu Zhou","doi":"10.1016/j.media.2026.103986","DOIUrl":"https://doi.org/10.1016/j.media.2026.103986","url":null,"abstract":"Orthopantomogram (OPGs) and Cone-Beam Computed Tomography (CBCT) are vital for dentistry, but creating large datasets for automated tooth segmentation is hindered by the labor-intensive process of manual instance-level annotation. This research aimed to benchmark and advance semi-supervised learning (SSL) as a solution for this data scarcity problem. We organized the 2nd Semi-supervised Teeth Segmentation (STS 2024) Challenge at MICCAI 2024. We provided a large-scale dataset comprising over 90,000 2D images and 3D axial slices, which includes 2,380 OPG images and 330 CBCT scans, all featuring detailed instance-level FDI annotations on part of the data. The challenge attracted 114 (OPG) and 106 (CBCT) registered teams. To ensure algorithmic excellence and full transparency, we rigorously evaluated the valid, open-source submissions from the top 10 (OPG) and top 5 (CBCT) teams, respectively. All successful submissions were deep learning-based SSL methods. The winning semi-supervised models demonstrated impressive performance gains over a fully-supervised nnU-Net baseline trained only on the labeled data. For the 2D OPG track, the top method improved the Instance Affinity (IA) score by over 44 percentage points. For the 3D CBCT track, the winning approach boosted the Instance Dice score by 61 percentage points. This challenge demonstrates the potential benefit benefit of SSL for complex, instance-level medical image segmentation tasks where labeled data is scarce. The most effective approaches consistently leveraged hybrid semi-supervised frameworks that combined knowledge from foundational models like SAM with multi-stage, coarse-to-fine refinement pipelines. Both the challenge dataset and the participants’ submitted code have been made publicly available on GitHub (<ce:inter-ref xlink:href=\"https://github.com/ricoleehduu/STS-Challenge-2024\" xlink:type=\"simple\">https://github.com/ricoleehduu/STS-Challenge-2024</ce:inter-ref>), ensuring transparency and reproducibility.","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"108 1","pages":""},"PeriodicalIF":10.9,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-09DOI: 10.1016/j.media.2026.103981
Md Kamrul Hasan, Guang Yang, Choon Hwai Yap
Cardiac anatomy segmentation is essential for clinical assessment of cardiac function and disease diagnosis to inform treatment and intervention. Deep learning (DL) has improved cardiac anatomy segmentation accuracy, especially when information on cardiac motion dynamics is integrated into the networks. Several methods for incorporating motion information have been proposed; however, existing methods are not yet optimal: adding the time dimension to input data causes high computational costs, and incorporating registration into the segmentation network remains computationally costly and can be affected by errors of registration, especially with non-DL registration. While attention-based motion modeling is promising, suboptimal design constrains its capacity to learn the complex and coherent temporal interactions inherent in cardiac image sequences. Here, we propose a novel approach to incorporating motion information in the DL segmentation networks: a computationally efficient yet robust Temporal Attention Module (TAM), modeled as a small, multi-headed, cross-temporal attention module, which can be plug-and-play inserted into a broad range of segmentation networks (CNN, transformer, or hybrid) without a drastic architecture modification. Extensive experiments on multiple cardiac imaging datasets, such as 2D echocardiography (CAMUS and EchoNet-Dynamic), 3D echocardiography (MITEA), and 3D cardiac MRI (ACDC), confirm that TAM consistently improves segmentation performance across datasets when added to a range of networks, including UNet, FCN8s, UNetR, SwinUNetR, and the recent I2UNet and DT-VNet. Integrating TAM into SAM yields a temporal SAM that reduces Hausdorff distance (HD) from 3.99 mm to 3.51 mm on the CAMUS dataset, while integrating TAM into a pre-trained MedSAM reduces HD from 3.04 to 2.06 pixels after fine-tuning on the EchoNet-Dynamic dataset. On the ACDC 3D dataset, our TAM-UNet and TAM-DT-VNet achieve substantial reductions in HD, from 7.97 mm to 4.23 mm and 6.87 mm to 4.74 mm, respectively. Additionally, TAM’s training does not require segmentation of ground truths from all time frames and can be achieved with sparse temporal annotation. TAM is thus a robust, generalizable, and adaptable solution for motion-awareness enhancement that is easily scaled from 2D to 3D. The code is available at https://github.com/kamruleee51/TAM.
{"title":"An Efficient, Scalable, and Adaptable Plug-and-Play Temporal Attention Module for Motion-Guided Cardiac Segmentation with Sparse Temporal Labels","authors":"Md Kamrul Hasan, Guang Yang, Choon Hwai Yap","doi":"10.1016/j.media.2026.103981","DOIUrl":"https://doi.org/10.1016/j.media.2026.103981","url":null,"abstract":"Cardiac anatomy segmentation is essential for clinical assessment of cardiac function and disease diagnosis to inform treatment and intervention. Deep learning (DL) has improved cardiac anatomy segmentation accuracy, especially when information on cardiac motion dynamics is integrated into the networks. Several methods for incorporating motion information have been proposed; however, existing methods are not yet optimal: adding the time dimension to input data causes high computational costs, and incorporating registration into the segmentation network remains computationally costly and can be affected by errors of registration, especially with non-DL registration. While attention-based motion modeling is promising, suboptimal design constrains its capacity to learn the complex and coherent temporal interactions inherent in cardiac image sequences. Here, we propose a novel approach to incorporating motion information in the DL segmentation networks: a computationally efficient yet robust Temporal Attention Module (TAM), modeled as a small, multi-headed, cross-temporal attention module, which can be plug-and-play inserted into a broad range of segmentation networks (CNN, transformer, or hybrid) without a drastic architecture modification. Extensive experiments on multiple cardiac imaging datasets, such as 2D echocardiography (CAMUS and EchoNet-Dynamic), 3D echocardiography (MITEA), and 3D cardiac MRI (ACDC), confirm that TAM consistently improves segmentation performance across datasets when added to a range of networks, including UNet, FCN8s, UNetR, SwinUNetR, and the recent I<ce:sup loc=\"post\">2</ce:sup>UNet and DT-VNet. Integrating TAM into SAM yields a temporal SAM that reduces Hausdorff distance (HD) from 3.99 mm to 3.51 mm on the CAMUS dataset, while integrating TAM into a pre-trained MedSAM reduces HD from 3.04 to 2.06 pixels after fine-tuning on the EchoNet-Dynamic dataset. On the ACDC 3D dataset, our TAM-UNet and TAM-DT-VNet achieve substantial reductions in HD, from 7.97 mm to 4.23 mm and 6.87 mm to 4.74 mm, respectively. Additionally, TAM’s training does not require segmentation of ground truths from all time frames and can be achieved with sparse temporal annotation. TAM is thus a robust, generalizable, and adaptable solution for motion-awareness enhancement that is easily scaled from 2D to 3D. The code is available at <ce:inter-ref xlink:href=\"https://github.com/kamruleee51/TAM\" xlink:type=\"simple\">https://github.com/kamruleee51/TAM</ce:inter-ref>.","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"22 1","pages":""},"PeriodicalIF":10.9,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-07DOI: 10.1016/j.media.2026.103974
Julian Suk, Dieuwertje Alblas, Barbara Hutten, Albert Wiegman, Christoph Brune, Pim Van Ooij, Jelmer M. Wolterink
{"title":"Physics-informed graph neural networks for flow field estimation in carotid arteries","authors":"Julian Suk, Dieuwertje Alblas, Barbara Hutten, Albert Wiegman, Christoph Brune, Pim Van Ooij, Jelmer M. Wolterink","doi":"10.1016/j.media.2026.103974","DOIUrl":"https://doi.org/10.1016/j.media.2026.103974","url":null,"abstract":"","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"4 1","pages":""},"PeriodicalIF":10.9,"publicationDate":"2026-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146138282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-29DOI: 10.1016/j.media.2026.103967
Yijie Li, Wei Zhang, Xi Zhu, Ye Wu, Yogesh Rathi, Lauren J. O’Donnell, Fan Zhang
{"title":"DDTracking: A Diffusion Model-Based Deep Generative Framework with Local-Global Spatiotemporal Modeling for Diffusion MRI Tractography","authors":"Yijie Li, Wei Zhang, Xi Zhu, Ye Wu, Yogesh Rathi, Lauren J. O’Donnell, Fan Zhang","doi":"10.1016/j.media.2026.103967","DOIUrl":"https://doi.org/10.1016/j.media.2026.103967","url":null,"abstract":"","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"117 1","pages":""},"PeriodicalIF":10.9,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：