{"title":"A Trust-Guided Approach to MR Image Reconstruction with Side Information","authors":"Arda Atalık, Sumit Chopra, Daniel K. Sodickson","doi":"10.1109/tmi.2025.3594363","DOIUrl":"https://doi.org/10.1109/tmi.2025.3594363","url":null,"abstract":"","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"34 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144755692","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}
{"title":"HoloDx: Knowledge- and Data-Driven Multimodal Diagnosis of Alzheimer’s Disease","authors":"Qiuhui Chen, Jintao Wang, Gang Wang, Yi Hong","doi":"10.1109/tmi.2025.3594364","DOIUrl":"https://doi.org/10.1109/tmi.2025.3594364","url":null,"abstract":"","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"285 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144755753","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}
Hongshuo Li, Baikai Ma, Lei Mou, Yonghuai Liu, Qinxiang Zheng, Hong Qi, Yitian Zhao
{"title":"Progressive Distillation for Incremental Learning in Corneal Confocal Microscopy Segmentation","authors":"Hongshuo Li, Baikai Ma, Lei Mou, Yonghuai Liu, Qinxiang Zheng, Hong Qi, Yitian Zhao","doi":"10.1109/tmi.2025.3593472","DOIUrl":"https://doi.org/10.1109/tmi.2025.3593472","url":null,"abstract":"","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"149 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144747493","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}
{"title":"Dual Cross-image Semantic Consistency with Self-aware Pseudo Labeling for Semi-supervised Medical Image Segmentation","authors":"Han Wu, Chong Wang, Zhiming Cui","doi":"10.1109/tmi.2025.3594081","DOIUrl":"https://doi.org/10.1109/tmi.2025.3594081","url":null,"abstract":"","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"20 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144747494","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}
{"title":"3D Deep-learning-based Segmentation of Human Skin Sweat Glands and Their 3D Morphological Response to Temperature Variations","authors":"Shaoyu Pei, Renxiong Wu, Shuaichen Lin, Lang Qin, Yuxing Gan, Wenjing Huang, Hao Zheng, Zhixuan Wang, Mohan Qin, Yong Liu, Guangming Ni","doi":"10.1109/tmi.2025.3593284","DOIUrl":"https://doi.org/10.1109/tmi.2025.3593284","url":null,"abstract":"","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"130 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144736821","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}
Jiayue Li, Ken Y. Foo, Rowan W. Sanderson, Renate Zilkens, Mireille Hardie, Laura Gale, Yen L. Yeow, Celia Green, Farah Abdul-Aziz, Juliana Hamzah, James Stephenson, Ammar Tayaran, Jose Cid Fernandez, Lee Jackson, Synn Lynn Chin, Saud Hamza, Anmol Rijhumal, Christobel M. Saunders, Brendan F. Kennedy
{"title":"Visualization of breast cancer using contrast-enhanced optical coherence elastography based on tissue heterogeneity","authors":"Jiayue Li, Ken Y. Foo, Rowan W. Sanderson, Renate Zilkens, Mireille Hardie, Laura Gale, Yen L. Yeow, Celia Green, Farah Abdul-Aziz, Juliana Hamzah, James Stephenson, Ammar Tayaran, Jose Cid Fernandez, Lee Jackson, Synn Lynn Chin, Saud Hamza, Anmol Rijhumal, Christobel M. Saunders, Brendan F. Kennedy","doi":"10.1109/tmi.2025.3593507","DOIUrl":"https://doi.org/10.1109/tmi.2025.3593507","url":null,"abstract":"","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"27 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144736751","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}
{"title":"ToothMaker: Realistic Panoramic Dental Radiograph Generation via Disentangled Control.","authors":"Weihao Yu,Xiaoqing Guo,Wuyang Li,Xinyu Liu,Hui Chen,Yixuan Yuan","doi":"10.1109/tmi.2025.3588466","DOIUrl":"https://doi.org/10.1109/tmi.2025.3588466","url":null,"abstract":"Generating high-fidelity dental radiographs is essential for training diagnostic models. Despite the development of numerous methods for other medical data, generative approaches in dental radiology remain unexplored. Due to the intricate tooth structures and specialized terminology, these methods often yield ambiguous tooth regions and incorrect dental concepts when applied to dentistry. In this paper, we take the first attempt to investigate diffusion-based teeth X-ray image generation and propose ToothMaker, a novel framework specifically designed for the dental domain. Firstly, to synthesize X-ray images that possess accurate tooth structures and realistic radiological styles simultaneously, we design control-disentangled fine-tuning (CDFT) strategy. Specifically, we present two separate controllers to handle style and layout control respectively, and introduce a gradient-based decoupling method that optimizes each using their corresponding disentangled gradients. Secondly, to enhance model's understanding of dental terminology, we propose prior-disentangled guidance module (PDGM), enabling precise synthesis of dental concepts. It utilizes large language model to decompose dental terminology into a series of meta-knowledge elements and performs interactions and refinements through hypergraph neural network. These elements are then fed into the network to guide the generation of dental concepts. Extensive experiments demonstrate the high fidelity and diversity of the images synthesized by our approach. By incorporating the generated data, we achieve substantial performance improvements on downstream segmentation and visual question answering tasks, indicating that our method can greatly reduce the reliance on manually annotated data. Code will be public available at https://github.com/CUHK-AIM-Group/ToothMaker.","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"90 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144720145","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}
Liu Li,Qiang Ma,Cheng Oyang,Johannes C Paetzold,Daniel Rueckert,Bernhard Kainz
{"title":"Topology Optimization in Medical Image Segmentation with Fast χ Euler Characteristic.","authors":"Liu Li,Qiang Ma,Cheng Oyang,Johannes C Paetzold,Daniel Rueckert,Bernhard Kainz","doi":"10.1109/tmi.2025.3589495","DOIUrl":"https://doi.org/10.1109/tmi.2025.3589495","url":null,"abstract":"Deep learning-based medical image segmentation techniques have shown promising results when evaluated based on conventional metrics such as the Dice score or Intersection-over-Union. However, these fully automatic methods often fail to meet clinically acceptable accuracy, especially when topological constraints should be observed, e.g., continuous boundaries or closed surfaces. In medical image segmentation, the correctness of a segmentation in terms of the required topological genus sometimes is even more important than the pixel-wise accuracy. Existing topology-aware approaches commonly estimate and constrain the topological structure via the concept of persistent homology (PH). However, these methods are difficult to implement for high dimensional data due to their polynomial computational complexity. To overcome this problem, we propose a novel and fast approach for topology-aware segmentation based on the Euler Characteristic (χ). First, we propose a fast formulation for χ computation in both 2D and 3D. The scalar χ error between the prediction and ground-truth serves as the topological evaluation metric. Then we estimate the spatial topology correctness of any segmentation network via a so-called topological violation map, i.e., a detailed map that highlights regions with χ errors. Finally, the segmentation results from the arbitrary network are refined based on the topological violation maps by a topology-aware correction network. Our experiments are conducted on both 2D and 3D datasets and show that our method can significantly improve topological correctness while preserving pixel-wise segmentation accuracy.","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"13 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144720144","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}