Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention最新文献

{"title":"A Unified Continuous Staging Framework for Alzheimer's Disease and Lewy Body Dementia via Hierarchical Anatomical Features.","authors":"Tong Chen, Minheng Chen, Jing Zhang, Yan Zhuang, Chao Cao, Xiaowei Yu, Yanjun Lyu, Lu Zhang, Li Su, Tianming Liu, Dajiang Zhu","doi":"10.1007/978-3-032-04947-6_2","DOIUrl":"10.1007/978-3-032-04947-6_2","url":null,"abstract":"<p><p>Alzheimer's Disease (AD) and Lewy Body Dementia (LBD) often exhibit overlapping pathologies, leading to common symptoms that make diagnosis challenging and protracted in clinical settings. While many studies achieve promising accuracy in identifying AD and LBD at earlier stages, they often focus on discrete classification rather than capturing the gradual nature of disease progression. Since dementia develops progressively, understanding the continuous trajectory of dementia is crucial, as it allows us to uncover hidden patterns in cognitive decline and provides critical insights into the underlying mechanisms of disease progression. To address this gap, we propose a novel multi-scale learning framework that leverages hierarchical anatomical features to model the continuous relationships across various neurodegenerative conditions, including Mild Cognitive Impairment, AD, and LBD. Our approach employs the proposed hierarchical graph embedding fusion technique, integrating anatomical features, cortical folding patterns, and structural connectivity at multiple scales. This integration captures both fine-grained and coarse anatomical details, enabling the identification of subtle patterns that enhance differentiation between dementia types. Additionally, our framework projects each subject onto continuous tree structures, providing intuitive visualizations of disease trajectories and offering a more interpretable way to track cognitive decline. To validate our approach, we conduct extensive experiments on our in-house dataset of 308 subjects spanning multiple groups. Our results demonstrate that the proposed tree-based model effectively represents dementia progression, achieves promising performance in intricate classification task of AD and LBD, and highlights discriminative brain regions that contribute to the differentiation between dementia types. Our code is available at https://github.com/tongchen2010/haff.</p>","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15962 ","pages":"13-23"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12714422/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145807071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning Explainable Imaging-Genetics Associations Related to a Neurological Disorder.","authors":"Jueqi Wang, Zachary Jacokes, John Darrell Van Horn, Michael C Schatz, Kevin A Pelphrey, Archana Venkataraman","doi":"10.1007/978-3-032-05141-7_34","DOIUrl":"10.1007/978-3-032-05141-7_34","url":null,"abstract":"<p><p>While imaging-genetics holds great promise for unraveling the complex interplay between brain structure and genetic variation in neurological disorders, traditional methods are limited to simplistic linear models or to black-box techniques that lack interpretability. In this paper, we present NeuroPathX, an explainable deep learning framework that uses an early fusion strategy powered by cross-attention mechanisms to capture meaningful interactions between structural variations in the brain derived from MRI and established biological pathways derived from genetics data. To enhance interpretability and robustness, we introduce two loss functions over the attention matrix - a sparsity loss that focuses on the most salient interactions and a pathway similarity loss that enforces consistent representations across the cohort. We validate NeuroPathX on both autism spectrum disorder and Alzheimer's disease. Our results demonstrate that NeuroPathX outperforms competing baseline approaches and reveals biologically plausible associations linked to the disorder. These findings underscore the potential of NeuroPathX to advance our understanding of complex brain disorders.</p>","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15970 ","pages":"347-357"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13078111/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147694708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GPU Accelerated Modeling of Cortical Radial and Tangential Connectivity Changes in Neurodegeneration.","authors":"Hongbo Zhang, Xinyu Nie, Jiaxin Yue, Yuan Li, John Ringman, Yonggang Shi","doi":"10.1007/978-3-032-05162-2_33","DOIUrl":"10.1007/978-3-032-05162-2_33","url":null,"abstract":"<p><p>The diffusion MRI signals in the human cerebral cortex are strongly associated with neurodegenerative diseases. Although models like NODDI have been extensively used to characterize cortical microstructure degeneration, they fall short in capturing detailed, orientation-specific connectivity changes within the cortex. In this study, we introduce a method to decompose cortical tissue diffusion signal to radial and tangential components. Our approach uses data from multi-shell diffusion imaging and combines it with anatomical information from brain surfaces. By applying a GPU accelerated probabilistic optimization framework, we can accurately and efficiently estimate these diffusion components while keeping the results smooth and consistent with the cortical anatomy. We test our method on data from HCP subjects and a clinical dataset of patients with autosomal dominant Alzheimer's Disease (ADAD) subjects. Our results demonstrate that the proposed method can more effectively reveal cortical gray matter connectivity changes related to tau pathology than metrics from the NODDI model. Our codebase is publicly available at https://github.com/Haibaobob/FOD-ctx-decomp.</p>","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15971 ","pages":"342-351"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12952719/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147349975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Surface-based Multi-Axis Longitudinal Disentanglement Using Contrastive Learning for Alzheimer's Disease.","authors":"Jianwei Zhang, Yonggang Shi","doi":"10.1007/978-3-032-05182-0_57","DOIUrl":"10.1007/978-3-032-05182-0_57","url":null,"abstract":"<p><p>Accurate modeling of disease progression is essential for comprehending the heterogeneous neuropathologies such as Alzheimer's Disease (AD). Traditional neuroimaging analysis often confound disease effects with normal aging, complicating the differential diagnosis. Recent advancements in deep learning have catalyzed the development of disentanglement techniques in Autoencoder networks, aiming to segregate longitudinal changes attributable to aging from those due to disease-specific alterations within the latent space. However, existing longitudinal disentanglement methods usually model disease as a single axis factor which ignores the complexity and heterogeneity of Alzheimer's Disease. In response to this issue, we propose a novel Surface-based Multi-axis Disentanglement framework.This framework posits multiple disease axes within the latent space, enhancing the model's capacity to encapsulate the multifaceted nature of AD, which includes various disease trajectories. To assign axes to data trajectories without explicit ground truth labels, we implement a longitudinal contrastive loss leveraging self-supervision, thereby refining the separation of disease trajectories. Evaluated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset ( <math><mi>N</mi> <mo>=</mo> <mn>1321</mn></math> ), our model demonstrates superior performance in delineating between cognitively normal (CN), mild cognitive impairment (MCI), and AD subjects,classification of stable MCI vs converting MCI and Amyloid status, compared to the single-axis model. This is further substantiated through an ablation study on the contrastive loss, underscoring the utility of our multi-axis approach in capturing the complex progression patterns of AD. The code is available at: https://github.com/jianweizhang17/MultiAxisDisentanglement.git.</p>","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15974 ","pages":"585-594"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12671042/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145673295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Oblique Genomics Mixture of Experts: Prediction of Brain Disorder With Aging-Related Changes of Brain's Structural Connectivity Under Genomic Influences.","authors":"Yanjun Lyu, Jing Zhang, Lu Zhang, Wei Ruan, Tianming Liu, Dajiang Zhu","doi":"10.1007/978-3-032-04965-0_33","DOIUrl":"10.1007/978-3-032-04965-0_33","url":null,"abstract":"<p><p>During the process of brain aging, the changes of white matter structural connectivity are closely correlated with the cognitive traits and brain function. Genes have strong controls over this transition of structural connectivity-altering, which influences brain health and may lead to severe dementia disease, e.g., Alzheimer's disease. In this work, we introduce a novel deep-learning diagram, an oblique genomics mixture of experts(OG-MoE), designed to address the prediction of brain disease diagnosis, with awareness of the structural connectivity changes over time, and coupled with the genomics influences. By integrating genomics features into the dynamic gating router system of MoE layers, the model specializes in representing the structural connectivity components in separate parameter spaces. We pretrained the model on the self-regression task of brain connectivity predictions and then implemented multi-task supervised learning on brain disorder predictions and brain aging prediction. Compared to traditional associations analysis, this work provided a new way of discovering the soft but intricate inter-play between brain connectome phenotypes and genomic traits. It revealed the significant divergence of this correlation between the normal brain aging process and neurodegeneration.</p>","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15963 ","pages":"348-358"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12714490/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145807046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the Feasibility of Zero-Shot Super-Resolution in Preclinical Imaging.","authors":"Omar A M Gharib, Samuel W Remedios, Blake E Dewey, Jerry L Prince, Aaron Carass","doi":"10.1007/978-3-032-04937-7_18","DOIUrl":"10.1007/978-3-032-04937-7_18","url":null,"abstract":"<p><p>Preclinical imaging studies are vital to the research, development, and evaluation of new medical therapies. Images acquired during these studies often have high in-plane resolution but low through-plane resolution, resulting in highly anisotropic volumes that hamper downstream volumetric analysis. Additionally, since there are no image acquisition standards across studies, training data for conventional supervised super-resolution (SR) methods is limited. In this work, we compare two SR methods that do not require additional training data. The first is ECLARE, a self-SR approach that creates its own training data from in-plane patches drawn from the anisotropic volume. The second is Biplanar Denoising diffusion null space model (DDNM) Averaging (BiDA), a proposed method leveraging two independently pre-trained denoising diffusion probabilistic models and the DDNM posterior sampling technique. We evaluate both methods first on rat data at two scale factors (2.5× and .5×) and compare signal recovery and downstream task performance. We further evaluate these methods on a different species (mice) to measure their generalizability. Both methods experimentally resulted in good signal recovery performance, but only the images super-resolved by BiDA were accurately skullstripped downstream. Although both methods performed well on the in-domain rat data, BiDA did not fully generalize to the out-of-domain mouse data.</p>","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15961 ","pages":"186-196"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12803491/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145992387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Domain-Adaptive Diagnosis of Lewy Body Disease with Transferability Aware Transformer.","authors":"Xiaowei Yu, Jing Zhang, Chao Cao, Tong Chen, Yan Zhuang, Minheng Chen, Yanjun Lyu, Lu Zhang, Li Su, Tianming Liu, Dajiang Zhu","doi":"10.1007/978-3-032-04981-0_18","DOIUrl":"10.1007/978-3-032-04981-0_18","url":null,"abstract":"<p><p>Lewy Body Disease (LBD) is a common but understudied dementia that poses a significant public health burden. It shares similar clinical signs with Alzheimer's disease (AD), with both conditions progressing through stages of normal cognition, mild cognitive impairment, and dementia. A major obstacle in LBD diagnosis is data scarcity, which limits the effectiveness of deep learning models. In contrast, AD datasets are more abundant, offering a potential avenue for knowledge transfer. However, LBD and AD data are typically collected from different sites using varied machines and protocols, resulting in a distinct domain shift. To effectively leverage AD data while mitigating domain shift, we propose a Transferability Aware Transformer (TAT) that adapts knowledge from AD to enhance LBD diagnosis. Our method utilizes structural connectivity (SC) derived from structural MRI as training data. Built on the attention mechanism, TAT assigns high weights to disease-transferable features while suppressing domain-specific ones, effectively reducing domain shift and improving diagnostic accuracy on limited LBD data. The experimental results demonstrate the effectiveness of TAT. Our work serves as the first to explore domain adaptation from AD to LBD study under data scarcity and domain shift scenarios, providing a promising framework for domain-adaptive diagnosis of rare diseases.</p>","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15966 ","pages":"184-193"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12715848/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145807033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BiSCoT: Behavior-Informed Subgroup-Consistent Connectome Template for Interpretable Brain Network Analysis.","authors":"Zijian Chen, Stefen Beeler-Duden, Sophie Lawson, Zachary Jacokes, John Darrell Van Horn, Kevin A Pelphrey, Archana Venkataraman","doi":"10.1007/978-3-032-05162-2_11","DOIUrl":"10.1007/978-3-032-05162-2_11","url":null,"abstract":"<p><p>We propose a graph information compression framework, called <b>B</b>ehavior-<b>I</b>nformed <b>S</b>ubgroup-consistent <b>Co</b>nnectome <b>T</b>emplate (BISCoT), that learns interpretable functional subnetworks from resting-state fMRI (rs-fMRI) connectivity, which simultaneously capture the heterogeneity of a diverse patient cohort. BISCoT uses multidimensional behavioral profiles to guide the decomposition of a rs-fMRI connectivity matrices into sparse yet representative subnetworks that are consistent within behavioral sub-groups. In particular, our framework adopts a graph convolution network to capture local connectivity features and applies a subgroup-informed pooling process to extract the final subnetworks. We evaluate BISCoT on an in-house dataset of individuals with autism spectrum disorder and demonstrate that the learned subnetworks <i>improve the performance of multiple downstream prediction tasks</i>. In addition, BISCoT extracts consistent connectivity \"templates\" at the subgroup level, which allows for interpretable biomarker identification.</p>","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15971 ","pages":"109-119"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13078116/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147694615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LLM-Powered Cross-Modal Alignment for Explainable Seizure Detection from EEG.","authors":"Maryam Riazi, Deeksha M Shama, Archana Venkataraman","doi":"10.1007/978-3-032-05185-1_33","DOIUrl":"10.1007/978-3-032-05185-1_33","url":null,"abstract":"<p><p>While artificial intelligence (AI) has revolutionized the field of epileptic seizure detection from electroencephalography (EEG), its clinical adoption remains limited, largely due to the lack of transparency in AI models and their inability to explain the underlying seizure etiology. This paper introduces SzXAI, a novel framework to enhance the reasoning abilities of AI models for EEG-based seizure detection. SzXAI employs a contrastive training mechanism, which uses cross-modality similarity layers to align the EEG encodings with textual concept embeddings derived from clinical notes using LLMs. Along with the alignment, SzXAI leverages an attention-weighted pooling mechanism to detect underlying seizure and baseline etiologies. We validate SzXAI via 10-fold cross validation on the publicly available Temple University Hospital dataset. Our results demonstrate that the alignment-powered training mechanism of SzXAI vastly outperforms direct etiology prediction, thus improving the reliability of the predicted seizure etiologies. Furthermore, structured sentence generation using the model output provided insights in a human-readable format. Thus, SzXAI provides an effective platform to boost clinical trust and AI usability in epilepsy management.</p>","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15973 ","pages":"337-347"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13078112/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147694618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning-based Alignment Measurement in Knee Radiographs.","authors":"Zhisen Hu, Dominic Cullen, Peter Thompson, David Johnson, Chang Bian, Aleksei Tiulpin, Timothy Cootes, Claudia Lindner","doi":"10.1007/978-3-032-04965-0_12","DOIUrl":"10.1007/978-3-032-04965-0_12","url":null,"abstract":"<p><p>Radiographic knee alignment (KA) measurement is important for predicting joint health and surgical outcomes after total knee replacement. Traditional methods for KA measurements are manual, time-consuming and require long-leg radiographs. This study proposes a deep learning-based method to measure KA in anteroposterior knee radiographs via automatically localized knee anatomical landmarks. Our method builds on hourglass networks and incorporates an attention gate structure to enhance robustness and focus on key anatomical features. To our knowledge, this is the first deep learning-based method to localize over 100 knee anatomical landmarks to fully outline the knee shape while integrating KA measurements on both pre-operative and post-operative images. It provides highly accurate and reliable anatomical varus/valgus KA measurements using the anatomical tibiofemoral angle, achieving mean absolute differences ~1° when compared to clinical ground truth measurements. Agreement between automated and clinical measurements was excellent pre-operatively (intra-class correlation coefficient (ICC) = 0.97) and good post-operatively (ICC = 0.86). Our findings demonstrate that KA assessment can be automated with high accuracy, creating opportunities for digitally enhanced clinical workflows.</p>","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":" ","pages":"121-130"},"PeriodicalIF":0.0,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7618655/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146021026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}