IEEE Transactions on Medical Imaging最新文献_第3页

End-to-end Spatiotemporal Analysis of Color Doppler Echocardiograms: Application for Rheumatic Heart Disease Detection 彩色多普勒超声心动图的端到端时空分析：在风湿性心脏病检测中的应用

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2025-09-29 DOI: 10.1109/tmi.2025.3615574

Pooneh Roshanitabrizi, Vishwesh Nath, Kelsey Brown, Taylor Gloria Broudy, Zhifan Jiang, Abhijeet Parida, Joselyn Rwebembera, Emmy Okello, Andrea Beaton, Holger R. Roth, Craig A. Sable, Marius George Linguraru

引用次数: 0

Deep Mutual Learning among Partially Labeled Datasets for Multi-Organ Segmentation. 基于部分标记数据集的深度互学习多器官分割。

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2025-09-26 DOI: 10.1109/tmi.2025.3614853

Xiaoyu Liu,Linhao Qu,Ziyue Xie,Yonghong Shi,Zhijian Song

{"title":"Deep Mutual Learning among Partially Labeled Datasets for Multi-Organ Segmentation.","authors":"Xiaoyu Liu,Linhao Qu,Ziyue Xie,Yonghong Shi,Zhijian Song","doi":"10.1109/tmi.2025.3614853","DOIUrl":"https://doi.org/10.1109/tmi.2025.3614853","url":null,"abstract":"Labeling multiple organs for segmentation is a complex and time-consuming process, resulting in a scarcity of comprehensively labeled multi-organ datasets while the emergence of numerous partially labeled datasets. Current methods face three critical limitations: incomplete exploitation of available supervision; complex inference, and insufficient validation of generalization capabilities. This paper proposes a new framework based on mutual learning, aiming to improve multi-organ segmentation performance by complementing information among partially labeled datasets. Specifically, this method consists of three key components: (1) partial-organ segmentation models training with Difference Mutual Learning, (2) pseudo-label generation and filtering, and (3) full-organ segmentation models training enhanced by Similarity Mutual Learning. Difference Mutual Learning enables each partial-organ segmentation model to utilize labels and features from other datasets as complementary signals, improving cross-dataset organ detection for better pseudo labels. Similarity Mutual Learning augments each full-organ segmentation model training with two additional supervision sources: inter-dataset ground truths and dynamic reliable transferred features, significantly boosting segmentation accuracy. The model obtained by this method achieves both high accuracy and efficient inference for multi-organ segmentation. Extensive experiments conducted on nine datasets spanning the head-neck, chest, abdomen, and pelvis demonstrate that the proposed method achieves SOTA performance.","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"77 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145153397","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}

引用次数: 0

Motion simulation of radio-labeled cells in whole-body positron emission tomography 全身正电子发射断层扫描中放射性标记细胞的运动模拟

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2025-09-26 DOI: 10.1109/tmi.2025.3614767

Nils Marquardt, Tobias Hengsbach, Marco Mauritz, Benedikt Wirth, Klaus Schäfers

引用次数: 0

Conditional Virtual Imaging for Few-Shot Vascular Image Segmentation. 基于条件虚拟成像的少镜头血管图像分割。

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2025-09-25 DOI: 10.1109/tmi.2025.3608467

Yanglong He,Rongjun Ge,Hui Tang,Yuxin Liu,Mengqing Su,Jean-Louis Coatrieux,Huazhong Shu,Yang Chen,Yuting He

{"title":"Conditional Virtual Imaging for Few-Shot Vascular Image Segmentation.","authors":"Yanglong He,Rongjun Ge,Hui Tang,Yuxin Liu,Mengqing Su,Jean-Louis Coatrieux,Huazhong Shu,Yang Chen,Yuting He","doi":"10.1109/tmi.2025.3608467","DOIUrl":"https://doi.org/10.1109/tmi.2025.3608467","url":null,"abstract":"In the field of medical image processing, vascular image segmentation plays a crucial role in clinical diagnosis, treatment planning, prognosis, and medical decision-making. Accurate and automated segmentation of vascular images can assist clinicians in understanding the vascular network structure, leading to more informed medical decisions. However, manual annotation of vascular images is time-consuming and challenging due to the fine and low-contrast vascular branches, especially in the medical imaging domain where annotation requires specialized knowledge and clinical expertise. Data-driven deep learning models struggle to achieve good performance when only a small number of annotated vascular images are available. To address this issue, this paper proposes a novel Conditional Virtual Imaging (CVI) framework for few-shot vascular image segmentation learning. The framework combines limited annotated data with extensive unlabeled data to generate high-quality images, effectively improving the accuracy and robustness of segmentation learning. Our approach primarily includes two innovations: First, aligned image-mask pair generation, which leverages the powerful image generation capabilities of large pre-trained models to produce high-quality vascular images with complex structures using only a few training images; Second, the Dual-Consistency Learning (DCL) strategy, which simultaneously trains the generator and segmentation model, allowing them to learn from each other and maximize the utilization of limited data. Experimental results demonstrate that our CVI framework can generate high-quality medical images and effectively enhance the performance of segmentation models in few-shot scenarios. Our code will be made publicly available online.","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"61 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145140266","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}

引用次数: 0

Data-driven System Matrix Manipulation Enabling Fast Functional Imaging in Tomography 数据驱动的系统矩阵操作在断层扫描中实现快速功能成像

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2025-09-22 DOI: 10.1109/tmi.2025.3612437

Peng Hu, Xin Tong, Li Lin, Lihong V. Wang

引用次数: 0

MUSCLE: A New Perspective to Multi-scale Fusion for Medical Image Classification based on the Theory of Evidence 肌肉：基于证据理论的医学图像多尺度融合分类新视角

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2025-09-19 DOI: 10.1109/tmi.2025.3612188

Junlai Qiu, Junyue Cao, Yawen Huang, Ziwei Zhu, Fubo Wang, Cheng Lu, Yuexiang Li, Yefeng Zheng

引用次数: 0

ProGIS: Prototype-Guided Interactive Segmentation for Pathological Images ProGIS：病理图像的原型引导交互式分割

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2025-09-17 DOI: 10.1109/tmi.2025.3611123

Jiusong Ge, Di Zhang, Yingkang Zhan, Jiashuai Liu, Tieliang Gong, Jialun Wu, Mireia Crispin, Chen Li, Zeyu Gao

引用次数: 0

Towards Trustworthy Multi-View Representation with Fine-Grained Explainability Embeddings. 基于细粒度可解释性嵌入的可信多视图表示。

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2025-09-15 DOI: 10.1109/tmi.2025.3607141

Jin Zhang,Yan Yang,Muheng Shang,Lei Guo,Daoqiang Zhang,Lei Du

{"title":"Towards Trustworthy Multi-View Representation with Fine-Grained Explainability Embeddings.","authors":"Jin Zhang,Yan Yang,Muheng Shang,Lei Guo,Daoqiang Zhang,Lei Du","doi":"10.1109/tmi.2025.3607141","DOIUrl":"https://doi.org/10.1109/tmi.2025.3607141","url":null,"abstract":"Multiomics co-learning is a powerful analytical paradigm that has benefited biomedical studies substantially. However, due to the diverse information and complex relationships of multiomics data, naive multi-view learning methods usually run into spurious correlations and biased signatures irrelevant to the diseases of interest. Therefore, the learned representations and cross-omics associations cannot translate into clinical knowledge for disease prediction. This issue becomes particularly severe when clinical data are limited and scarce. To handle this issue, we propose a novel and powerful scheme, referred to as the Causality-driven Trustworthy Multi-View maPping approach (Cad-TMVP). Specifically, we design a fined multi-directional mapping module to extract co-expression patterns across different modalities and capture fine-grained interpretability factors. We also meticulously design dynamic mechanisms to facilitate adaptive loss-term reweighting and trustworthy integration of multiple modalities. Cad-TMVP enhances downstream tasks by developing a cooperative learning module that simultaneously performs automated diagnosis and result interpretation. Furthermore, we develop an efficient search strategy and support computation to reduce the high computational burden, making our approach practicable. We conduct extensive experiments on different types of multiomics data. The proposed method establishes new state-of-the-art results in various settings while maintaining excellent interpretability. Thus, it sets a potentially newparadigm in trustworthy multi-modal learning and verifies its flexibility and versatility in real biomedical applications.","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"36 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145068353","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}

引用次数: 0

MultiASNet: Multimodal Label Noise Robust Framework for the Classification of Aortic Stenosis in Echocardiography. 超声心动图主动脉狭窄分类的多模态标签噪声鲁棒框架。

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2025-09-12 DOI: 10.1109/tmi.2025.3609319

Victoria Wu,Andrea Fung,Bahar Khodabakhshian,Baraa Abdelsamad,Hooman Vaseli,Neda Ahmadi,Jamie A D Goco,Michael Y Tsang,Christina Luong,Purang Abolmaesumi,Teresa S M Tsang

{"title":"MultiASNet: Multimodal Label Noise Robust Framework for the Classification of Aortic Stenosis in Echocardiography.","authors":"Victoria Wu,Andrea Fung,Bahar Khodabakhshian,Baraa Abdelsamad,Hooman Vaseli,Neda Ahmadi,Jamie A D Goco,Michael Y Tsang,Christina Luong,Purang Abolmaesumi,Teresa S M Tsang","doi":"10.1109/tmi.2025.3609319","DOIUrl":"https://doi.org/10.1109/tmi.2025.3609319","url":null,"abstract":"Aortic stenosis (AS), a prevalent and serious heart valve disorder, requires early detection but remains difficult to diagnose in routine practice. Although echocardiography with Doppler imaging is the clinical standard, these assessments are typically limited to trained specialists. Point-of-care ultrasound (POCUS) offers an accessible alternative for AS screening but is restricted to basic 2D B-mode imaging, often lacking the analysis Doppler provides. Our project introduces MultiASNet, a multimodal machine learning framework designed to enhance AS screening with POCUS by combining 2D B-mode videos with structured data from echocardiography reports, including Doppler parameters. Using contrastive learning, MultiASNet aligns video features with report features in tabular form from the same patient to improve interpretive quality. To address misalignment where a single report corresponds to multiple video views, some irrelevant to AS diagnosis, we use cross-attention in a transformer-based video and tabular network to assign less importance to irrelevant report data. The model integrates structured data only during training, enabling independent use with B-mode videos during inference for broader accessibility. MultiASNet also incorporates sample selection to counteract label noise from observer variability, yielding improved accuracy on two datasets. We achieved balanced accuracy scores of 93.0% on a private dataset and 83.9% on the public TMED-2 dataset for AS detection. For severity classification, balanced accuracy scores were 80.4% and 59.4% on the private and public datasets, respectively. This model facilitates reliable AS screening in non-specialist settings, bridging the gap left by Doppler data while reducing noise-related errors. Our code is publicly available at github.com/DeepRCL/MultiASNet.","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"15 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145043575","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}

引用次数: 0

Label-Efficient Deep Color Deconvolution of Brightfield Multiplex IHC Images 亮场多重IHC图像的标签高效深彩色反卷积

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2025-09-11 DOI: 10.1109/tmi.2025.3609245

Shahira Abousamra, Danielle Fassler, Rajarsi Gupta, Tahsin Kurc, Luisa F. Escobar-Hoyos, Dimitris Samaras, Kenneth R. Shroyer, Joel Saltz, Chao Chen

引用次数: 0