{"title":"Machine Learning Approach to 3×4 Mueller Polarimetry for Complete Reconstruction of Diagnostic Polarimetric Images of Biological Tissues.","authors":"Sooyong Chae,Tongyu Huang,Omar Rodriguez-Nunez,Theotim Lucas,Jean-Charles Vanel,Jeremy Vizet,Angelo Pierangelo,Gennadii Piavchenko,Tsanislava Genova,Ajmal Ajmal,Jessica C Ramella-Roman,Alexander Doronin,Hui Ma,Tatiana Novikova","doi":"10.1109/tmi.2025.3567570","DOIUrl":"https://doi.org/10.1109/tmi.2025.3567570","url":null,"abstract":"The translation of imaging Mueller polarimetry to clinical practice is often hindered by large footprint and relatively slow acquisition speed of the existing instruments. Using polarization-sensitive camera as a detector may reduce instrument dimensions and allow data streaming at video rate. However, only the first three rows of a complete 4×4 Mueller matrix can be measured. To overcome this hurdle we developed a machine learning approach using sequential neural network algorithm for the reconstruction of missing elements of a Mueller matrix from the measured elements of the first three rows. The algorithm was trained and tested on the dataset of polarimetric images of various excised human tissues (uterine cervix, colon, skin, brain) acquired with two different imaging Mueller polarimeters operating in either reflection (wide-field imaging system) or transmission (microscope) configurations at different wavelengths of 550 nm and 385 nm, respectively. Reconstruction performance was evaluated using various error metrics, all of which confirmed low error values. The reconstruction of full images of the fourth row of Mueller matrix with GPU parallelization and increasing batch size took less than 50 milliseconds. It suggests that a machine learning approach with parallel processing of all image pixels combined with the partial Mueller polarimeter operating at video rate can effectively substitute for the complete Mueller polarimeter and produce accurate maps of depolarization, linear retardance and orientation of the optical axis of biological tissues, which can be used for medical diagnosis in clinical settings.","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"20 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143915077","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}
Xinrui Song,Xuanang Xu,Jiajin Zhang,Diego Machado Reyes,Pingkun Yan
{"title":"Dino-Reg: Efficient Multimodal Image Registration with Distilled Features.","authors":"Xinrui Song,Xuanang Xu,Jiajin Zhang,Diego Machado Reyes,Pingkun Yan","doi":"10.1109/tmi.2025.3567247","DOIUrl":"https://doi.org/10.1109/tmi.2025.3567247","url":null,"abstract":"Medical image registration is a crucial process for aligning anatomical structures, enabling applications such as atlas mapping, longitudinal analysis, and multimodal data fusion. This paper introduces DINO-Reg, an adaptation-free registration method leveraging the vision foundation model, DINOv2, to extract features for deformable 3D medical image alignment. Although DINOv2 was originally trained on natural images, our study links the vision foundation model with medical image registration and demonstrates that the generic image encoder could readily generalize to medical images with state-of-the-art performance. We further propose DINO-Reg-Eco, a knowledge-distilled version using a UNet-structured 3D convolutional neural network (CNN) for feature extraction. The Eco model reduces encoding time by 99% while maintaining state-of-the-art performance, which is essential for resource-limited settings and significantly lowers the carbon footprint associated with intensive computational demands. Benchmarking across diverse datasets shows that both methods outperform existing supervised and unsupervised approaches without fine-tuning, demonstrating the transformative potential of foundation models in medical image registration. Our code is open-sourced at https: //github.com/RPIDIAL/DINO-Reg.","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"38 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143915041","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}
Theodoros P. Vagenas, Maria Vakalopoulou, Christos Sachpekidis, Antonia Dimitrakopoulou-Strauss, George K. Matsopoulos
{"title":"Representation learning in PET scans enhanced by semantic and 3D position specific characteristics","authors":"Theodoros P. Vagenas, Maria Vakalopoulou, Christos Sachpekidis, Antonia Dimitrakopoulou-Strauss, George K. Matsopoulos","doi":"10.1109/tmi.2025.3566996","DOIUrl":"https://doi.org/10.1109/tmi.2025.3566996","url":null,"abstract":"","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"113 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143909861","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}
Soodeh Kalaie, Andy Bulpitt, Alejandro F. Frangi, Ali Gooya
{"title":"An End-to-End Deep Learning Generative Framework for Refinable Shape Matching and Generation","authors":"Soodeh Kalaie, Andy Bulpitt, Alejandro F. Frangi, Ali Gooya","doi":"10.1109/tmi.2025.3562756","DOIUrl":"https://doi.org/10.1109/tmi.2025.3562756","url":null,"abstract":"","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"9 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143909862","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}
Md Tarikul Islam, Juliana Benavides, Ravi Prakash, Mohsin Zafar, Laura McGuire, Fady Charbel, Amanda P. Siegel, Danilo Erricolo, James Lin, Juri G. Gelovani, Kamran Avanaki
{"title":"Transfontanelle Thermoacoustic Imaging of Intraventricular Brain Hemorrhages in Live Sheep","authors":"Md Tarikul Islam, Juliana Benavides, Ravi Prakash, Mohsin Zafar, Laura McGuire, Fady Charbel, Amanda P. Siegel, Danilo Erricolo, James Lin, Juri G. Gelovani, Kamran Avanaki","doi":"10.1109/tmi.2025.3566372","DOIUrl":"https://doi.org/10.1109/tmi.2025.3566372","url":null,"abstract":"","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"44 1","pages":"1-1"},"PeriodicalIF":10.6,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143901367","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}
Ziwang Xu, Lanqing Guo, Satoshi Tsutsui, Shuyan Zhang, Alex C. Kot, Bihan Wen
{"title":"Digital Staining with Knowledge Distillation: A Unified Framework for Unpaired and Paired-But-Misaligned Data","authors":"Ziwang Xu, Lanqing Guo, Satoshi Tsutsui, Shuyan Zhang, Alex C. Kot, Bihan Wen","doi":"10.1109/tmi.2025.3565329","DOIUrl":"https://doi.org/10.1109/tmi.2025.3565329","url":null,"abstract":"","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"34 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143898274","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}
Zihan Cheng, Jintao Guo, Jian Zhang, Lei Qi, Luping Zhou, Yinghuan Shi, Yang Gao
{"title":"Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation","authors":"Zihan Cheng, Jintao Guo, Jian Zhang, Lei Qi, Luping Zhou, Yinghuan Shi, Yang Gao","doi":"10.1109/tmi.2025.3564765","DOIUrl":"https://doi.org/10.1109/tmi.2025.3564765","url":null,"abstract":"","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"25 1","pages":"1-1"},"PeriodicalIF":10.6,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143893129","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}