IEEE Transactions on Medical Imaging最新文献

CLIF-Net: Intersection-guided Cross-view Fusion Network for Infection Detection from Cranial Ultrasound. CLIF-Net：用于颅脑超声感染检测的交叉引导交叉视图融合网络。

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2025-05-15 DOI: 10.1109/tmi.2025.3570316

Mingzhao Yu,Mallory R Peterson,Kathy Burgoine,Thaddeus Harbaugh,Peter Olupot-Olupot,Melissa Gladstone,Cornelia Hagmann,Frances M Cowan,Andrew Weeks,Sarah U Morton,Ronald Mulondo,Edith Mbabazi-Kabachelor,Steven J Schiff,Vishal Monga

{"title":"CLIF-Net: Intersection-guided Cross-view Fusion Network for Infection Detection from Cranial Ultrasound.","authors":"Mingzhao Yu,Mallory R Peterson,Kathy Burgoine,Thaddeus Harbaugh,Peter Olupot-Olupot,Melissa Gladstone,Cornelia Hagmann,Frances M Cowan,Andrew Weeks,Sarah U Morton,Ronald Mulondo,Edith Mbabazi-Kabachelor,Steven J Schiff,Vishal Monga","doi":"10.1109/tmi.2025.3570316","DOIUrl":"https://doi.org/10.1109/tmi.2025.3570316","url":null,"abstract":"This paper addresses the problem of detecting possible serious bacterial infection (pSBI) of infancy, i.e. a clinical presentation consistent with bacterial sepsis in newborn infants using cranial ultrasound (cUS) images. The captured image set for each patient enables multiview imagery: coronal and sagittal, with geometric overlap. To exploit this geometric relation, we develop a new learning framework, called the intersection-guided Crossview Local- and Image-level Fusion Network (CLIF-Net). Our technique employs two distinct convolutional neural network branches to extract features from coronal and sagittal images with newly developed multi-level fusion blocks. Specifically, we leverage the spatial position of these images to locate the intersecting region. We then identify and enhance the semantic features from this region across multiple levels using cross-attention modules, facilitating the acquisition of mutually beneficial and more representative features from both views. The final enhanced features from the two views are then integrated and projected through the image-level fusion layer, outputting pSBI and non-pSBI class probabilities. We contend that our method of exploiting multi-view cUS images enables a first of its kind, robust 3D representation tailored for pSBI detection. When evaluated on a dataset of 302 cUS scans from Mbale Regional Referral Hospital in Uganda, CLIF-Net demonstrates substantially enhanced performance, surpassing the prevailing state-of-the-art infection detection techniques.","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"39 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144065795","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

2.5D Multi-view Averaging Diffusion Model for 3D Medical Image Translation: Application to Low-count PET Reconstruction with CT-less Attenuation Correction 三维医学图像平移的2.5D多视点平均扩散模型：应用于无ct衰减校正的低计数PET重建

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2025-05-15 DOI: 10.1109/tmi.2025.3570342

Tianqi Chen, Jun Hou, Yinchi Zhou, Huidong Xie, Xiongchao Chen, Qiong Liu, Xueqi Guo, Menghua Xia, James S. Duncan, Chi Liu, Bo Zhou

引用次数: 0

Uncertainty Co-estimator for Improving Semi-Supervised Medical Image Segmentation. 改进半监督医学图像分割的不确定性协估计。

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2025-05-15 DOI: 10.1109/tmi.2025.3570310

Xiang Zeng,Shengwu Xiong,Jinming Xu,Guangxing Du,Yi Rong

{"title":"Uncertainty Co-estimator for Improving Semi-Supervised Medical Image Segmentation.","authors":"Xiang Zeng,Shengwu Xiong,Jinming Xu,Guangxing Du,Yi Rong","doi":"10.1109/tmi.2025.3570310","DOIUrl":"https://doi.org/10.1109/tmi.2025.3570310","url":null,"abstract":"Recently, combining the strategy of consistency regularization with uncertainty estimation has shown promising performance on semi-supervised medical image segmentation tasks. However, most existing methods estimate the uncertainty solely based on the outputs of a single neural network, which results in imprecise uncertainty estimations and eventually degrades the segmentation performance. In this paper, we propose a novel Uncertainty Co-estimator (UnCo) framework to deal with this problem. Inspired by the co-training technique, UnCo establishes two different mean-teacher modules (i.e., two pairs of teacher and student models), and estimates three types of uncertainty from the multi-source predictions generated by these models. Through combining these uncertainties, their differences will help to filter out incorrect noise in each estimate, thus allowing the final fused uncertainty maps to be more accurate. These resulting maps are then used to enhance a cross-consistency regularization imposed between the two modules. In addition, UnCo also designs an internal consistency regularization within each module, so that the student models can aggregate diverse feature information from both modules, thus promoting the semi-supervised segmentation performance. Finally, an adversarial constraint is introduced to maintain the model diversity. Experimental results on four medical image datasets indicate that UnCo can achieve new state-of-the-art performance on both 2D and 3D semi-supervised segmentation tasks. The source code will be available at https://github.com/z1010x/UnCo.","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"28 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144065789","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

HERON: High-Efficiency Real-Time mOtion quantification and re-acquisitioN for Fetal diffusion MRI HERON：胎儿扩散MRI的高效实时运动量化和再采集

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2025-05-14 DOI: 10.1109/tmi.2025.3569853

Jordina Aviles Verdera, Antonia Bortolazzi, Sara Neves Silva, Kelly Payette, Kamilah St. Clair, Sarah McElroy, Shaihan Malik, Joseph Hajnal, Raphael Tomi-Tricot, Mary Rutherford, Jana Hutter

引用次数: 0

Dynamic Client Distillation for Semi-supervised Federated Learning in A Realistic Scenario 现实场景中半监督联邦学习的动态客户端蒸馏

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2025-05-14 DOI: 10.1109/tmi.2025.3570054

Ning Shen, Tingfa Xu, Shiqi Huang, Zhenxiang Chen, Jianan Li

引用次数: 0

Disentangled Pseudo-bag Augmentation for Whole Slide Image Multiple Instance Learning 全幻灯片图像多实例学习的解缠伪袋增强

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2025-05-14 DOI: 10.1109/tmi.2025.3569941

Jiuyang Dong, Junjun Jiang, Kui Jiang, Jiahan Li, Linghan Cai, Yongbing Zhang

引用次数: 0

Advancing Volumetric Medical Image Segmentation via Global-Local Masked Autoencoders 基于全局-局部掩码自编码器的体积医学图像分割

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2025-05-14 DOI: 10.1109/tmi.2025.3569782

Jiaxin Zhuang, Luyang Luo, Qiong Wang, Mingxiang Wu, Lin Luo, Hao Chen

引用次数: 0

SA-Seg: Annotation-Efficient Segmentation for Airway Tree Using Saliency-based Annotation SA-Seg：基于显著性标注的气道树高效分割

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2025-05-12 DOI: 10.1109/tmi.2025.3568855

Kai Zhou, Nan Chen, Zhang Yi, Xiuyuan Xu

引用次数: 0

VesselDiffusion: 3D Vascular Structure Generation Based on Diffusion Model 血管扩散：基于扩散模型的三维血管结构生成

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2025-05-09 DOI: 10.1109/tmi.2025.3568602

Zhanqiang Guo, Zimeng Tan, Jianjiang Feng, Jie Zhou

引用次数: 0

Anatomy-Aware Deep Unrolling for Task-Oriented Acceleration of Multi-Contrast MRI. 多对比MRI任务导向加速的解剖感知深度展开。

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2025-05-08 DOI: 10.1109/tmi.2025.3568157

Yuzhu He,Chunfeng Lian,Ruyi Xiao,Fangmao Ju,Chao Zou,Zongben Xu,Jianhua Ma

{"title":"Anatomy-Aware Deep Unrolling for Task-Oriented Acceleration of Multi-Contrast MRI.","authors":"Yuzhu He,Chunfeng Lian,Ruyi Xiao,Fangmao Ju,Chao Zou,Zongben Xu,Jianhua Ma","doi":"10.1109/tmi.2025.3568157","DOIUrl":"https://doi.org/10.1109/tmi.2025.3568157","url":null,"abstract":"Multi-contrast magnetic resonance imaging (MC-MRI) plays a crucial role in clinical practice. However, its performance is hindered by long scanning times and the isolation between image acquisition and downstream clinical diagnoses/treatments. Despite the activated research on accelerated MC-MRI, few existing studies prioritize personalized imaging tailored to individual patient characteristics and clinical needs. That is, the current approach often aims to enhance overall image quality, disregarding the specific pathologies or anatomical regions that are of particular interest to clinicians. To tackle this challenge, we propose an anatomy-aware unrolling-based deep network, dubbed as A2MC-MRI, offering promising interpretability and learning capacity for fast MC-MRI catering to downstream clinical needs. The network is unfolded from the iterative algorithm designed for a task-oriented MC-MRI reconstruction model. Specifically, to enhance concurrent MC-MRI of specific targets of interest (TOIs), the model integrates a learnable group sparsity with an anatomyaware denoising prior. Within the anatomy-aware denoising prior, a segmentation network is involved to provide critical location information for TOI-enhanced denoising. Finally, such an unrolled network is jointly learned with k-space sampling patterns for task-oriented MC-MR reconstruction. Comprehensive evaluations on two public benchmarks as well as an in-house dataset demonstrate that our A2MCMRI led to state-of-the-art performance in MC-MRI reconstruction under high acceleration rates, featuring notable enhancements in TOI imaging quality. The code will be available at https://github.com/ladderlab-xjtu/A2MC-MRI.","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"48 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143926380","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