IEEE Transactions on Medical Imaging最新文献_第6页

Glancing Beyond Patch: Spatial Contextual Cues for 3D Neuron Segmentation 超越补丁：三维神经元分割的空间上下文线索

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2025-08-25 DOI: 10.1109/tmi.2025.3602097

Haiyang Yan, Yanchao Zhang, Zhenchen Li, Jinyue Guo, Hao Zhai, Jiazheng Liu, Yongwei Zhong, Jingbin Yuan, Lijun Shen, Linlin Li, Xufei Du, Hua Han

引用次数: 0

Addressing Glaucoma Structure-Function Relationship: A Multi-Task Learning Framework with Multi-Modal and Unpaired Data 解决青光眼结构-功能关系：一个多模态和非配对数据的多任务学习框架

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2025-08-25 DOI: 10.1109/tmi.2025.3600311

Xuming An, Jacqueline Chua, Yujin Wang, Ruben Hemelings, Rahat Husain, Rachel Chong, Tina Wong, Tin Aung, Damon Wong, Chen Zhang, Leopold Schmetterer

引用次数: 0

Multicycle Temporal Compounding Sonography for Despeckling at Specified Respiratory Phases 多周期时间复合超声在特定呼吸期去斑

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2025-08-25 DOI: 10.1109/tmi.2025.3602633

Fansong Dong, Yiyun Wu, Chuhao Yin, Juan Tu, Dong Zhang, Xiasheng Guo

引用次数: 0

Uncertainty-Aware Survival Analysis with Dirichlet Distribution for Multi-Scale Pathology and Genomics 多尺度病理和基因组学中Dirichlet分布的不确定性感知生存分析

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2025-08-23 DOI: 10.1109/tmi.2025.3601892

Songhan Jiang, Linghan Cai, Zhengyu Gan, Yifeng Wang, Guo Tang, Yongbing Zhang

引用次数: 0

Robust Deep Learning for Pulse-echo Speed of Sound Imaging via Time-shift Maps 基于时移图的脉冲回波声成像速度鲁棒深度学习

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2025-08-23 DOI: 10.1109/tmi.2025.3602000

Haotian Chen, Aiguo Han

引用次数: 0

Diversity-enhanced Collaborative Mamba for Semi-supervised Medical Image Segmentation 半监督医学图像分割的多样性增强协作曼巴

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2025-08-21 DOI: 10.1109/tmi.2025.3601450

Shumeng Li, Jian Zhang, Lei Qi, Luping Zhou, Yinghuan Shi, Yang Gao

引用次数: 0

Harnessing Text Insights with Visual Alignment for Medical Image Segmentation 利用文本洞察与视觉对齐医学图像分割

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2025-08-21 DOI: 10.1109/tmi.2025.3601359

Qingjie Zeng, Huan Luo, Zilin Lu, Yutong Xie, Zhiyong Wang, Yanning Zhang, Yong Xia

引用次数: 0

An Unsupervised Learning Approach for Reconstructing 3T-Like Images from 0.3T MRI Without Paired Training Data. 一种无监督学习方法在无配对训练数据的情况下从0.3T MRI重建3t样图像。

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2025-08-11 DOI: 10.1109/tmi.2025.3597401

Huaishui Yang,Shaojun Liu,Yilong Liu,Lingyan Zhang,Shoujin Huang,Jiayu Zheng,Jingzhe Liu,Hua Guo,Ed X Wu,Mengye Lyu

{"title":"An Unsupervised Learning Approach for Reconstructing 3T-Like Images from 0.3T MRI Without Paired Training Data.","authors":"Huaishui Yang,Shaojun Liu,Yilong Liu,Lingyan Zhang,Shoujin Huang,Jiayu Zheng,Jingzhe Liu,Hua Guo,Ed X Wu,Mengye Lyu","doi":"10.1109/tmi.2025.3597401","DOIUrl":"https://doi.org/10.1109/tmi.2025.3597401","url":null,"abstract":"Magnetic resonance imaging (MRI) is powerful in medical diagnostics, yet high-field MRI, despite offering superior image quality, incurs significant costs for procurement, installation, maintenance, and operation, restricting its availability and accessibility, especially in low- and middle-income countries. Addressing this, our study proposes an unsupervised learning algorithm based on cycle-consistent generative adversarial networks. This framework transforms 0.3T low-field MRI into higher-quality 3T-like images, bypassing the need for paired low/high-field training data. The proposed architecture integrates two novel modules to enhance reconstruction quality: (1) an attention block that dynamically balances high-field-like features with the original low-field input, and (2) an edge block that refines boundary details, providing more accurate structural reconstruction. The proposed generative model is trained on large-scale, unpaired, public datasets, and further validated on paired low/high-field acquisitions of three major clinical MRI sequences: T1-weighted, T2-weighted, and fluid-attenuated inversion recovery (FLAIR) imaging. It demonstrates notable improvements in tissue contrast and signal-to-noise ratio while preserving anatomical fidelity. This approach utilizes rich information from publicly available MRI resources, providing a data-efficient unsupervised alternative that complements supervised methods to enhance the utility of low-field MRI.","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"12 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144819720","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

EPDiff: Erasure Perception Diffusion Model for Unsupervised Anomaly Detection in Preoperative Multimodal Images. EPDiff：用于术前多模态图像无监督异常检测的擦除感知扩散模型。

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2025-08-11 DOI: 10.1109/tmi.2025.3597545

Jiazheng Wang,Min Liu,Wenting Shen,Renjie Ding,Yaonan Wang,Erik Meijering

{"title":"EPDiff: Erasure Perception Diffusion Model for Unsupervised Anomaly Detection in Preoperative Multimodal Images.","authors":"Jiazheng Wang,Min Liu,Wenting Shen,Renjie Ding,Yaonan Wang,Erik Meijering","doi":"10.1109/tmi.2025.3597545","DOIUrl":"https://doi.org/10.1109/tmi.2025.3597545","url":null,"abstract":"Unsupervised anomaly detection (UAD) methods typically detect anomalies by learning and reconstructing the normative distribution. However, since anomalies constantly invade and affect their surroundings, sub-healthy areas in the junction present structural deformations that could be easily misidentified as anomalies, posing difficulties for UAD methods that solely learn the normative distribution. The use of multimodal images can facilitate to address the above challenges, as they can provide complementary information of anomalies. Therefore, this paper propose a novel method for UAD in preoperative multimodal images, called Erasure Perception Diffusion model (EPDiff). First, the Local Erasure Progressive Training (LEPT) framework is designed to better rebuild sub-healthy structures around anomalies through the diffusion model with a two-phase process. Initially, healthy images are used to capture deviation features labeled as potential anomalies. Then, these anomalies are locally erased in multimodal images to progressively learn sub-healthy structures, obtaining a more detailed reconstruction around anomalies. Second, the Global Structural Perception (GSP) module is developed in the diffusion model to realize global structural representation and correlation within images and between modalities through interactions of high-level semantic information. In addition, a training-free module, named Multimodal Attention Fusion (MAF) module, is presented for weighted fusion of anomaly maps between different modalities and obtaining binary anomaly outputs. Experimental results show that EPDiff improves the AUPRC and mDice scores by 2% and 3.9% on BraTS2021, and by 5.2% and 4.5% on Shifts over the state-of-the-art methods, which proves the applicability of EPDiff in diverse anomaly diagnosis. The code is available at https://github.com/wjiazheng/EPDiff.","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"744 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144819772","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

Automatic Choroid Segmentation and Thickness Measurement Based on Mixed Attention-guided Multiscale Feature Fusion Network. 基于混合注意引导的多尺度特征融合网络的脉络自动分割与厚度测量。

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2025-08-08 DOI: 10.1109/tmi.2025.3597026

Xiaoyu Zhu,Shiyin Li,HongLiang Bi,Lina Guan,Haiyang Liu,Zhaolin Lu

{"title":"Automatic Choroid Segmentation and Thickness Measurement Based on Mixed Attention-guided Multiscale Feature Fusion Network.","authors":"Xiaoyu Zhu,Shiyin Li,HongLiang Bi,Lina Guan,Haiyang Liu,Zhaolin Lu","doi":"10.1109/tmi.2025.3597026","DOIUrl":"https://doi.org/10.1109/tmi.2025.3597026","url":null,"abstract":"Choroidal thickness variations serve as critical biomarkers for numerous ophthalmic diseases. Accurate segmentation and quantification of the choroid in optical coherence tomography (OCT) images is essential for clinical diagnosis and disease progression monitoring. Due to the small number of disease types in the public OCT dataset involving changes in choroidal thickness and the lack of a publicly available labeled dataset, we constructed the Xuzhou Municipal Hospital (XZMH)-Choroid dataset. This dataset contains annotated OCT images of normal and eight choroid-related diseases. However, segmentation of the choroid in OCT images remains a formidable challenge due to the confounding factors of blurred boundaries, non-uniform texture, and lesions. To overcome these challenges, we proposed a mixed attention-guided multiscale feature fusion network (MAMFF-Net). This network integrates a Mixed Attention Encoder (MAE) for enhanced fine-grained feature extraction, a deformable multiscale feature fusion path (DMFFP) for adaptive feature integration across lesion deformations, and a multiscale pyramid layer aggregation (MPLA) module for improved contextual representation learning. Through comparative experiments with other deep learning methods, we found that the MAMFF-Net model has better segmentation performance than other deep learning methods (mDice: 97.44, mIoU: 95.11, mAcc: 97.71). Based on the choroidal segmentation implemented in MAMFF-Net, an algorithm for automated choroidal thickness measurement was developed, and the automated measurement results approached the level of senior specialists.","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"113 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144802501","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