Pub Date : 2025-12-18DOI: 10.1109/tmi.2025.3646046
Yuheng Li, Yuxiang Lai, Maria Thor, Deborah Marshall, Zachary Buchwald, David S. Yu, Xiaofeng Yang
{"title":"OpenVocabCT: Towards Universal Text-driven CT Image Segmentation","authors":"Yuheng Li, Yuxiang Lai, Maria Thor, Deborah Marshall, Zachary Buchwald, David S. Yu, Xiaofeng Yang","doi":"10.1109/tmi.2025.3646046","DOIUrl":"https://doi.org/10.1109/tmi.2025.3646046","url":null,"abstract":"","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"27 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145777588","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}
Pub Date : 2025-12-18DOI: 10.1109/tmi.2025.3645860
Kai Fan,Zhi Wang,Aiqiu Wu,Anli Zhang,Ao Li,Minghui Wang
Accurate detection of nuclei is a crucial step in advancing pathology image analysis for disease diagnosis and treatment. However, significant domain discrepancies exist among pathology images, which severely degrade the performance of detection models. Despite promising results from existing domain adaptation approaches, they may overlook the detrimental impact of intra-domain variation (IDV) at both cell and image scales. The IDV issue typically manifests as dramatic differences in nuclei composition, morphology, and spatial arrangement, which occurs not only across different pathology images but also within a single image. This inherent heterogeneity produces highly complex feature distributions, ultimately making cross-domain alignment significantly more arduous. Moreover, the presence of IDV further injects noise into pseudo-labels, reducing the signal-to-noise ratio in the feature space and complicating the alignment process. To tackle these challenges, we propose a novel variation-robust graph-level feature alignment (VGFA) framework for unsupervised domain adaptive nuclei detection. Specifically, our method first incorporates a prior-based nuclei graph pruning scheme that harnesses nuclei spatial contextual priors and dynamically eliminates unreliable nodes from the nuclei graph. Then, a local-global nuclei encoding network is designed to learn nuclei graph representations that holistically encapsulate the consistent traits among various nuclei, thereby mitigating challenges posed by cell-scale IDV. Moreover, VGFA leverages a nuclei graph discrepancy loss that is resilient to image-scale IDV, achieving effective feature alignment in cross-domain graph feature space. Extensive experiments across different adaptation scenarios demonstrate that our VGFA framework achieves state-of-the-art performance, outperforming existing feature alignment methods in domain adaptive nuclei detection.
{"title":"VGFA: Variation-Robust Graph-Level Feature Alignment for Domain Adaptive Nuclei Detection.","authors":"Kai Fan,Zhi Wang,Aiqiu Wu,Anli Zhang,Ao Li,Minghui Wang","doi":"10.1109/tmi.2025.3645860","DOIUrl":"https://doi.org/10.1109/tmi.2025.3645860","url":null,"abstract":"Accurate detection of nuclei is a crucial step in advancing pathology image analysis for disease diagnosis and treatment. However, significant domain discrepancies exist among pathology images, which severely degrade the performance of detection models. Despite promising results from existing domain adaptation approaches, they may overlook the detrimental impact of intra-domain variation (IDV) at both cell and image scales. The IDV issue typically manifests as dramatic differences in nuclei composition, morphology, and spatial arrangement, which occurs not only across different pathology images but also within a single image. This inherent heterogeneity produces highly complex feature distributions, ultimately making cross-domain alignment significantly more arduous. Moreover, the presence of IDV further injects noise into pseudo-labels, reducing the signal-to-noise ratio in the feature space and complicating the alignment process. To tackle these challenges, we propose a novel variation-robust graph-level feature alignment (VGFA) framework for unsupervised domain adaptive nuclei detection. Specifically, our method first incorporates a prior-based nuclei graph pruning scheme that harnesses nuclei spatial contextual priors and dynamically eliminates unreliable nodes from the nuclei graph. Then, a local-global nuclei encoding network is designed to learn nuclei graph representations that holistically encapsulate the consistent traits among various nuclei, thereby mitigating challenges posed by cell-scale IDV. Moreover, VGFA leverages a nuclei graph discrepancy loss that is resilient to image-scale IDV, achieving effective feature alignment in cross-domain graph feature space. Extensive experiments across different adaptation scenarios demonstrate that our VGFA framework achieves state-of-the-art performance, outperforming existing feature alignment methods in domain adaptive nuclei detection.","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"29 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145777344","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":"A General Framework for Efficient Medical Image Analysis via Shared Attention Vision Transformer","authors":"Yihang Liu, Ying Wen, Longzhen Yang, Lianghua He, Mengchu Zhou","doi":"10.1109/tmi.2025.3644949","DOIUrl":"https://doi.org/10.1109/tmi.2025.3644949","url":null,"abstract":"","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"53 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145770724","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}
Pub Date : 2025-12-17DOI: 10.1109/tmi.2025.3644811
Anwai Archit, Luca Freckmann, Constantin Pape
{"title":"MedicoSAM: Robust Improvement of SAM for Medical Imaging","authors":"Anwai Archit, Luca Freckmann, Constantin Pape","doi":"10.1109/tmi.2025.3644811","DOIUrl":"https://doi.org/10.1109/tmi.2025.3644811","url":null,"abstract":"","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"155 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145770725","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}
Pub Date : 2025-12-11DOI: 10.1109/tmi.2025.3642294
Lan Yang,Yao Li,Chen Qiao
The accurate diagnosis of early mild cognitive impairment is crucial for timely intervention and treatment of dementia. But it is challenging to distinguish from normal aging due to its complex pathology and mild symptoms. Recently, effective hyper-connectivity identified through directed hypergraph can be considered as an effective analysis approach for early detection of mild cognitive impairment and exploration of its underlying neural mechanisms, because it captures directional higher-order interactions across multiple brain regions. However, current methods face limitations, including inefficiency in high-dimensional spaces, sensitivity to noise, reliance on manually defined structures, lack of global structural information, and static learning mechanisms. To address these issues, we integrate robust dictionary learning with directed hypergraph structure learning within a unified framework. This approach jointly estimates low-dimensional sparse representations and the directed hypergraph. The integration allows both processes to dynamically reinforce each other, leading to the refinement of the directed hypergraph, which improves the estimation of low-dimensional sparse representations and, in turn, enhances the quality of the directed hypergraph estimation. Experimental analyses on simulated data confirm the positive interplay between these processes, demonstrating the effectiveness of the proposed collaborative learning strategy. Furthermore, results on real-world brain signal data show that the proposed method is highly competitive in early detection of mild cognitive impairment, highlighting its ability to identify effective hyper-connectivity networks with significant differences.
{"title":"Constructing Effective Hyper-Connectivity Networks through Adaptive Directed Hypergraph Embedded Dictionary Learning: Application to Early Mild Cognitive Impairment Detection.","authors":"Lan Yang,Yao Li,Chen Qiao","doi":"10.1109/tmi.2025.3642294","DOIUrl":"https://doi.org/10.1109/tmi.2025.3642294","url":null,"abstract":"The accurate diagnosis of early mild cognitive impairment is crucial for timely intervention and treatment of dementia. But it is challenging to distinguish from normal aging due to its complex pathology and mild symptoms. Recently, effective hyper-connectivity identified through directed hypergraph can be considered as an effective analysis approach for early detection of mild cognitive impairment and exploration of its underlying neural mechanisms, because it captures directional higher-order interactions across multiple brain regions. However, current methods face limitations, including inefficiency in high-dimensional spaces, sensitivity to noise, reliance on manually defined structures, lack of global structural information, and static learning mechanisms. To address these issues, we integrate robust dictionary learning with directed hypergraph structure learning within a unified framework. This approach jointly estimates low-dimensional sparse representations and the directed hypergraph. The integration allows both processes to dynamically reinforce each other, leading to the refinement of the directed hypergraph, which improves the estimation of low-dimensional sparse representations and, in turn, enhances the quality of the directed hypergraph estimation. Experimental analyses on simulated data confirm the positive interplay between these processes, demonstrating the effectiveness of the proposed collaborative learning strategy. Furthermore, results on real-world brain signal data show that the proposed method is highly competitive in early detection of mild cognitive impairment, highlighting its ability to identify effective hyper-connectivity networks with significant differences.","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"29 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145728473","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}
Pub Date : 2025-12-11DOI: 10.1109/tmi.2025.3642381
Jiaqi Zhang,Xiuzhe Wu,Jiahui Liu,Chunyu Zou,Fengze Nie,Zicheng Sun,Xiaojuan Qi,Jiang Liu
High-fidelity reconstruction of the Posterior Eyeball Shape (PES) is crucial for early diagnosis and timely intervention of sight-threatening diseases such as high myopia, diabetic retinopathy, and glaucoma. However, existing magnetic resonance imaging (MRI)- and optical coherence tomography (OCT)-based methods either provide only coarse scleral geometry or suffer from suboptimal PES representations due to limited field of view (FOV) and detail loss, hindering accurate assessment of intact retinal pigment epithelium (RPE) abnormalities. In this study, we propose the Polar Subarea-Aware Fusion Net (PSAFNet), a novel end-to-end framework that reconstructs complete and high-fidelity PES directly from a single local OCT scan, even under clinically common settings with only 6.25% FOV. To avoid information loss, we reformulate PES reconstruction as a 2D dense regression task and introduce the Ocular Shape Map (OSM), an innovative lossless 2D representation that encodes 3D coordinate attributes into corresponding image channels. PSAFNet then leverages three dedicated modules-Subarea Feature Embedding Module (SFEM), Channel- and Patch-wise Fusion Blocks (CFB/PFB), and Reassemble and Up-sample Module (RUM)-to enhance positional awareness, integrate local-global features, and achieve high-resolution OSM prediction. Furthermore, we construct two large-scale datasets, POSDiag and PESGen, comprising 794 ultra-widefield OCT scans from diverse health conditions and imaging devices, providing a comprehensive benchmark for PES reconstruction. Extensive experiments demonstrate that PSAFNet consistently outperforms existing methods (e.g., EMD=5.58, AAL=97.3%) and exhibits strong clinical relevance, validated by superior performance in downstream disease classification and ophthalmologist evaluations (Expert-Score=82.78%). The source code of the proposed PSAFNet is released at https://github.com/HKUZJ77/PSAFNet.
{"title":"Polar Subarea-Aware Fusion Net for Posterior Eyeball Shape Reconstruction.","authors":"Jiaqi Zhang,Xiuzhe Wu,Jiahui Liu,Chunyu Zou,Fengze Nie,Zicheng Sun,Xiaojuan Qi,Jiang Liu","doi":"10.1109/tmi.2025.3642381","DOIUrl":"https://doi.org/10.1109/tmi.2025.3642381","url":null,"abstract":"High-fidelity reconstruction of the Posterior Eyeball Shape (PES) is crucial for early diagnosis and timely intervention of sight-threatening diseases such as high myopia, diabetic retinopathy, and glaucoma. However, existing magnetic resonance imaging (MRI)- and optical coherence tomography (OCT)-based methods either provide only coarse scleral geometry or suffer from suboptimal PES representations due to limited field of view (FOV) and detail loss, hindering accurate assessment of intact retinal pigment epithelium (RPE) abnormalities. In this study, we propose the Polar Subarea-Aware Fusion Net (PSAFNet), a novel end-to-end framework that reconstructs complete and high-fidelity PES directly from a single local OCT scan, even under clinically common settings with only 6.25% FOV. To avoid information loss, we reformulate PES reconstruction as a 2D dense regression task and introduce the Ocular Shape Map (OSM), an innovative lossless 2D representation that encodes 3D coordinate attributes into corresponding image channels. PSAFNet then leverages three dedicated modules-Subarea Feature Embedding Module (SFEM), Channel- and Patch-wise Fusion Blocks (CFB/PFB), and Reassemble and Up-sample Module (RUM)-to enhance positional awareness, integrate local-global features, and achieve high-resolution OSM prediction. Furthermore, we construct two large-scale datasets, POSDiag and PESGen, comprising 794 ultra-widefield OCT scans from diverse health conditions and imaging devices, providing a comprehensive benchmark for PES reconstruction. Extensive experiments demonstrate that PSAFNet consistently outperforms existing methods (e.g., EMD=5.58, AAL=97.3%) and exhibits strong clinical relevance, validated by superior performance in downstream disease classification and ophthalmologist evaluations (Expert-Score=82.78%). The source code of the proposed PSAFNet is released at https://github.com/HKUZJ77/PSAFNet.","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"38 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145728474","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}
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