IEEE Transactions on Medical Imaging最新文献_第9页

HoloDx: Knowledge- and Data-Driven Multimodal Diagnosis of Alzheimer’s Disease HoloDx：知识和数据驱动的阿尔茨海默病多模式诊断

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2025-07-31 DOI: 10.1109/tmi.2025.3594364

Qiuhui Chen, Jintao Wang, Gang Wang, Yi Hong

引用次数: 0

Progressive Distillation for Incremental Learning in Corneal Confocal Microscopy Segmentation 渐进式蒸馏用于角膜共聚焦显微分割的增量学习

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2025-07-30 DOI: 10.1109/tmi.2025.3593472

Hongshuo Li, Baikai Ma, Lei Mou, Yonghuai Liu, Qinxiang Zheng, Hong Qi, Yitian Zhao

引用次数: 0

Dual Cross-image Semantic Consistency with Self-aware Pseudo Labeling for Semi-supervised Medical Image Segmentation 基于自意识伪标记的双交叉图像语义一致性半监督医学图像分割

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2025-07-30 DOI: 10.1109/tmi.2025.3594081

Han Wu, Chong Wang, Zhiming Cui

引用次数: 0

3D Deep-learning-based Segmentation of Human Skin Sweat Glands and Their 3D Morphological Response to Temperature Variations 基于深度学习的人体皮肤汗腺三维分割及其对温度变化的三维形态响应

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2025-07-29 DOI: 10.1109/tmi.2025.3593284

Shaoyu Pei, Renxiong Wu, Shuaichen Lin, Lang Qin, Yuxing Gan, Wenjing Huang, Hao Zheng, Zhixuan Wang, Mohan Qin, Yong Liu, Guangming Ni

引用次数: 0

Visualization of breast cancer using contrast-enhanced optical coherence elastography based on tissue heterogeneity 基于组织异质性的对比度增强光学相干弹性成像在乳腺癌可视化中的应用

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2025-07-29 DOI: 10.1109/tmi.2025.3593507

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

引用次数: 0

ToothMaker: Realistic Panoramic Dental Radiograph Generation via Disentangled Control. 牙机：现实全景牙科x光片生成通过解开控制。

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2025-07-28 DOI: 10.1109/tmi.2025.3588466

Weihao Yu,Xiaoqing Guo,Wuyang Li,Xinyu Liu,Hui Chen,Yixuan Yuan

{"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}

引用次数: 0

Topology Optimization in Medical Image Segmentation with Fast χ Euler Characteristic. 基于快速χ欧拉特征的医学图像分割拓扑优化。

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2025-07-28 DOI: 10.1109/tmi.2025.3589495

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}

引用次数: 0

Analytical Reconstruction of Human-Scale Dark-Field CT 人尺度暗场CT的分析重建

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2025-07-25 DOI: 10.1109/tmi.2025.3592849

Peiyuan Guo, Li Zhang, Longchao Men, Jincheng Lu, Yan Xu, Hongxia Yin, Zhenchang Wang, Zhentian Wang

引用次数: 0

A Deep Learning Multimodal Fusion-Based Method for Cell and Nucleus Segmentation 基于深度学习多模态融合的细胞和细胞核分割方法

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2025-07-25 DOI: 10.1109/tmi.2025.3592625

Bin Shen, Zhen Gu, Jiale Zhou, Bingyong Yan, Yiquan Fang, Huifeng Wang

引用次数: 0

SUP-Net: Slow-time Upsampling Network for Aliasing Removal in Doppler Ultrasound. sup.net：用于多普勒超声混叠去除的慢时间上采样网络。

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2025-07-24 DOI: 10.1109/tmi.2025.3591820

Hassan Nahas,Alfred C H Yu

{"title":"SUP-Net: Slow-time Upsampling Network for Aliasing Removal in Doppler Ultrasound.","authors":"Hassan Nahas,Alfred C H Yu","doi":"10.1109/tmi.2025.3591820","DOIUrl":"https://doi.org/10.1109/tmi.2025.3591820","url":null,"abstract":"Doppler ultrasound modalities, which include spectral Doppler and color flow imaging, are frequently used tools for flow diagnostics because of their real-time point-of-care applicability and high temporal resolution. When implemented using pulse-echo sensing and phase shift estimation principles, this modality's pulse repetition frequency (PRF) is known to influence the maximum detectable velocity. If the PRF is inevitably set below the Nyquist limit due to imaging requirements or hardware constraints, aliasing errors or spectral overlap may corrupt the estimated flow data. To solve this issue, we have devised a deep learning-based framework, powered by a custom slow-time upsampling network (SUP-Net) that leverages spatiotemporal characteristics to upsample the received ultrasound signals across pulse echoes acquired using high-frame-rate ultrasound (HiFRUS). Our framework infers high-PRF signals from signals acquired at low PRF, thereby improving Doppler ultrasound's flow estimation quality. SUP-Net was trained and evaluated on in vivo femoral acquisitions from 20 participants and was applied recursively to resolve scenarios with excessive aliasing across a range of PRFs. We report the successful reconstruction of slow-time signals with frequency content that exceeds the Nyquist limit once and twice. By operating on the fundamental slow-time signals, our framework can resolve aliasing-related artifacts in several downstream modalities, including color Doppler and pulse wave Doppler.","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"18 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144701288","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