Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention最新文献_第3页

Generating Novel Brain Morphology by Deforming Learned Templates. 通过变形学习模板生成新的脑形态。

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Pub Date : 2026-01-01 Epub Date: 2025-09-20 DOI: 10.1007/978-3-032-04937-7_20

Alan Q Wang, Fangrui Huang, Bailey Trang, Wei Peng, Mohammad Abbasi, Kilian M Pohl, Mert R Sabuncu, Ehsan Adeli

{"title":"Generating Novel Brain Morphology by Deforming Learned Templates.","authors":"Alan Q Wang, Fangrui Huang, Bailey Trang, Wei Peng, Mohammad Abbasi, Kilian M Pohl, Mert R Sabuncu, Ehsan Adeli","doi":"10.1007/978-3-032-04937-7_20","DOIUrl":"10.1007/978-3-032-04937-7_20","url":null,"abstract":"Designing generative models for 3D structural brain MRI that synthesize morphologically-plausible and attribute-specific (e.g., age, sex, disease state) samples is an active area of research. Existing approaches based on frameworks like GANs or diffusion models synthesize the image directly, which may limit their ability to capture intricate morphological details. In this work, we propose a 3D brain MRI generation method based on state-of-the-art latent diffusion models (LDMs), called MorphLDM, that generates novel images by applying synthesized deformation fields to a learned template. Instead of using a reconstruction-based autoencoder (as in a typical LDM), our encoder outputs a latent embedding derived from both an image and a learned template that is itself the output of a template decoder; this latent is passed to a deformation field decoder, whose output is applied to the learned template. A registration loss is minimized between the original image and the deformed template with respect to the encoder and both decoders. Empirically, our approach outperforms generative baselines on metrics spanning image diversity, adherence with respect to input conditions, and voxel-based morphometry. Our code is available at https://github.com/alanqrwang/morphldm.","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":"207-217"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12945308/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147328910","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}

引用次数: 0

Accelerated Free-Breathing 5D Multi-Echo Respiratory Motion-Resolved R2*, PDFF, and QSM Using Novel Composite Total Variation. 加速自由呼吸5D多回波呼吸运动分解R2*， PDFF和QSM使用新的复合总变化。

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Pub Date : 2026-01-01 Epub Date: 2025-09-21 DOI: 10.1007/978-3-032-04947-6_3

MungSoo Kang, Or Alus, Youngwook Kee

{"title":"Accelerated Free-Breathing 5D Multi-Echo Respiratory Motion-Resolved R2*, PDFF, and QSM Using Novel Composite Total Variation.","authors":"MungSoo Kang, Or Alus, Youngwook Kee","doi":"10.1007/978-3-032-04947-6_3","DOIUrl":"10.1007/978-3-032-04947-6_3","url":null,"abstract":"We introduce a novel composite total variation (TV) and its solution algorithm with their application to multi-echo, respiratory motion-resolved 5D (3D space + 1D respiratory motion + 1D echo signal evolution) compressed sensing (CS) abdominal MR image reconstruction. The proposed formalism ensures a sparse representation between multi-echo images with varying contrast-a vital feature that needs to be preserved-making it highly suitable for applications in multi-dimensional computational/quantitative imaging. The key idea of the proposed composite TV and its formal definition were inspired by the observation that the spatial gradient of difference images in multi-echo MRI appears sparse. Throughout extensive experiments on a small number of healthy volunteers, we have demonstrated improved performance of the proposed method in 5D motion-resolved CS reconstruction of multi-echo MRI data compared to the state-of-the-art method. We have also demonstrated improved performance of the proposed method in quantitative tissue parameter mapping (such as R2*, proton density fat fraction, and quantitative susceptibility mapping) across a wide range of undersampling factors. In conclusion, the proposed method enables vastly accelerated motion-resolved multi-echo CS-MRI minimally impacting the quantification of downstream tissue parameters.","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":"24-34"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13041726/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147610927","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}

引用次数: 0

Unsupervised OCT Image Interpolation Using Deformable Registration and generative models. 使用可变形配准和生成模型的无监督OCT图像插值。

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Pub Date : 2026-01-01 Epub Date: 2025-09-19 DOI: 10.1007/978-3-032-04965-0_62

Shuwen Wei, Samuel W Remedios, Zhangxing Bian, Shimeng Wang, Junyu Chen, Yihao Liu, Bruno Jedynak, Tin Y A Liu, Shiv Saidha, Peter A Calabresi, Jerry L Prince, Aaron Carass

{"title":"Unsupervised OCT Image Interpolation Using Deformable Registration and generative models.","authors":"Shuwen Wei, Samuel W Remedios, Zhangxing Bian, Shimeng Wang, Junyu Chen, Yihao Liu, Bruno Jedynak, Tin Y A Liu, Shiv Saidha, Peter A Calabresi, Jerry L Prince, Aaron Carass","doi":"10.1007/978-3-032-04965-0_62","DOIUrl":"10.1007/978-3-032-04965-0_62","url":null,"abstract":"Optical coherence tomography (OCT) images are often acquired as highly anisotropic volumes, where the scanning step is dense along the fast axis but sparse along the slow axis. This affects image analysis, such as image registration for longitudinal alignment. To create more isotropic volumes, bicubic interpolation can be used along the slow axis, but it generally produces blurry features. Registration-based interpolation can reduce blurriness, but often fails to generate realistic OCT images. Deep generative models can sample realistic images, but lack the structural consistency constraints required for interpolation. In this paper, we propose an unsupervised image interpolation method that combines registration-based interpolation with a deep generative model to overcome their individual limitations and improve the structural accuracy and realism of interpolated OCT images. We compare the proposed method with both bicubic and registration-based interpolation on real OCT datasets, and show that it achieves the best interpolation performance.","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":"661-671"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13352931/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148427954","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}

引用次数: 0

Reliable and Interpretable Visual Field Progression Prediction with Diffusion Models and Conformal Risk Control. 用扩散模型和保形风险控制进行可靠和可解释的视野进展预测。

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Pub Date : 2026-01-01 Epub Date: 2025-09-18 DOI: 10.1007/978-3-032-05182-0_53

Wenwen Si, Vivian Lin, Bo Sun, Kuk Jin Jang, Rubo Xing, Almiqdad Saeed, Rina Nagatani, Oleg Sokolsky, Insup Lee, Lama Al-Aswad

{"title":"Reliable and Interpretable Visual Field Progression Prediction with Diffusion Models and Conformal Risk Control.","authors":"Wenwen Si, Vivian Lin, Bo Sun, Kuk Jin Jang, Rubo Xing, Almiqdad Saeed, Rina Nagatani, Oleg Sokolsky, Insup Lee, Lama Al-Aswad","doi":"10.1007/978-3-032-05182-0_53","DOIUrl":"10.1007/978-3-032-05182-0_53","url":null,"abstract":"Accurately predicting visual field progression is critical for early intervention and personalized treatment of glaucoma. However, existing methods struggle with both predictive accuracy and reliable uncertainty quantification. This paper introduces a framework that leverages diffusion models and conformal risk control to generate robust and interpretable forecasts of visual field deterioration. We first train a diffusion model to predict future visual fields based on a patient's past examinations. To ensure trustworthy predictions, we design a novel archetypal-based conformal risk control method, which provides finite-sample coverage guarantees on intervals of archetypal contributions. This framework captures the underlying structures within uncertainty, enabling clinicians to interpret a range of potential progression patterns rather than a single deterministic outcome. Experimental results illustrate that our method achieves the target archetypal contribution coverage while providing tighter prediction intervals than baselines. Visualizations show how archetypal visual field patterns contribute to prediction uncertainty, offering interpretable insights into disease progression. By combining diffusion models with conformal methods, our framework enhances the reliability of AI-assisted visual field forecasting, ultimately supporting improved clinical decision-making. Our code is available at: https://github.com/averysi224/abci.git.","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":"543-552"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13348073/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148427941","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}

引用次数: 0

MOSCARD - Multimodal Opportunistic Screening for Cardiovascular Adverse events with Causal Reasoning and De-confounding. MOSCARD -基于因果推理和去混杂的心血管不良事件的多模式机会性筛查。

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Pub Date : 2026-01-01 Epub Date: 2025-09-20 DOI: 10.1007/978-3-032-04984-1_32

Jialu Pi, Juan Maria Farina, Rimita Lahiri, Jiwoong Jeong, Archana Gurudu, Hyung-Bok Park, Chieh-Ju Chao, Chadi Ayoub, Reza Arsanjani, Imon Banerjee

{"title":"MOSCARD - Multimodal Opportunistic Screening for Cardiovascular Adverse events with Causal Reasoning and De-confounding.","authors":"Jialu Pi, Juan Maria Farina, Rimita Lahiri, Jiwoong Jeong, Archana Gurudu, Hyung-Bok Park, Chieh-Ju Chao, Chadi Ayoub, Reza Arsanjani, Imon Banerjee","doi":"10.1007/978-3-032-04984-1_32","DOIUrl":"10.1007/978-3-032-04984-1_32","url":null,"abstract":"Major Adverse Cardiovascular Events (MACE) remain the leading cause of mortality globally, as reported in the Global Disease Burden Study 2021. Opportunistic screening leverages data collected from routine health check-ups and multimodal data can play a key role to identify at-risk individuals. Chest X-rays (CXR) provide insights into chronic conditions contributing to major adverse cardiovascular events (MACE), while 12-lead electrocardiogram (ECG) directly assesses cardiac electrical activity and structural abnormalities. Integrating CXR and ECG could offer a more comprehensive risk assessment than conventional models, which rely on clinical scores, computed tomography (CT) measurements, or biomarkers, which may be limited by sampling bias and single modality constraints. We propose a novel predictive modeling framework - MOSCARD, multimodal causal reasoning with co-attention to align two distinct modalities and simultaneously mitigate bias and confounders in opportunistic risk estimation. Primary technical contributions are - (i) multimodal alignment of CXR with ECG guidance; (ii) integration of causal reasoning; (iii) dual back-propagation graph for deconfounding. Evaluated on internal, shift data from emergency department (ED) and external MIMIC datasets, our model outperformed single (ED) and external MIMIC datasets, our model outperformed single modality and state-of-the-art foundational models - AUC: 0.75, 0.83, 0.71 respectively. Proposed cost-effective opportunistic screening enables early intervention, improving patient outcomes and reducing disparities.","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15967 ","pages":"331-341"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12685155/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145716931","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}

引用次数: 0

ImPartial: Multi-channel Whole-Cell Segmentation using Partial Annotations. 公正：多通道全细胞分割使用部分注释。

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Pub Date : 2026-01-01 Epub Date: 2026-09-25 DOI: 10.1007/978-3-032-38224-5_56

Gunjan Shrivastava, Saad Nadeem

{"title":"ImPartial: Multi-channel Whole-Cell Segmentation using Partial Annotations.","authors":"Gunjan Shrivastava, Saad Nadeem","doi":"10.1007/978-3-032-38224-5_56","DOIUrl":"https://doi.org/10.1007/978-3-032-38224-5_56","url":null,"abstract":"Accurate cell segmentation in pathology images typically requires dense pixel-wise annotations, which are costly and time-consuming to obtain. This challenge is especially important for emerging biological imaging modalities and multiplexed datasets with variable channel configurations, where expert-labeled data are scarce. In this work, we introduce ImPartial, a deep learning framework designed to achieve state-of-the-art segmentation performance in low-annotation regimes using sparse scribbles and limited supervision. ImPartial augments the segmentation objective via self-supervised multi-channel quantized imputation. This approach leverages the observation that perfect pixel-wise reconstruction or denoising of the image is not needed for accurate segmentation, and thus, introduces a self-supervised classification objective that better aligns with the overall segmentation goal. We demonstrate that ImPartial achieves performance at par with fully supervised models while requiring substantially fewer annotations. Extensive experiments on benchmark multiplexed cellular imaging and single-plex clinical brightfield immunohistochemistry datasets show consistent improvements over strong baselines with only partial annotations. All benchmark datasets and code are available via our Github: https://github.com/nadeemlab/ImPartial.","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"16891 ","pages":"594-602"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13637462/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149121227","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}

引用次数: 0

A Large-scale Neural Model Inversion Framework for Effective Connectivity Estimation. 一种有效连通性估计的大规模神经模型反演框架。

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Pub Date : 2026-01-01 Epub Date: 2025-09-20 DOI: 10.1007/978-3-032-04937-7_1

Guoshi Li, Pew-Thian Yap

{"title":"A Large-scale Neural Model Inversion Framework for Effective Connectivity Estimation.","authors":"Guoshi Li, Pew-Thian Yap","doi":"10.1007/978-3-032-04937-7_1","DOIUrl":"10.1007/978-3-032-04937-7_1","url":null,"abstract":"The development of a computational framework that can infer large-scale brain-wide effective connectivity (EC) based on resting-state functional MRI (rs-fMRI) represents a grand challenge to computational neuroimaging. Towards the goal of estimating full-scale, whole-brain EC, we developed a new computational framework termed Large-scale nEural Model Inversion (LEMI) by utilizing a linear neural mass model with an efficient Kalman-filter based gradient descent algorithm. Key advantages of LEMI include fast estimation of both intra-regional and inter-regional connection strengths for large-scale networks, allowing exploration of both intrinsic and external mechanisms in neuroscience problems. Using ground-truth simulations, we demonstrated that LEMI can accurately and efficiently recover model parameters in a large network (100 regions) within 90 minutes. We then applied the LEMI model to an empirical rs-fMRI dataset from the ADNI database and identified widespread reduced excitation-inhibition (E-I) ratio in patients with Alzheimer's disease (AD). Overall, LEMI provides an efficient and accurate computational framework to estimate large-scale EC and whole-brain E-I balance based on non-invasive neuroimaging data.","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":"3-12"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13004605/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147501313","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}

引用次数: 0

Pretraining on Chronic Lung Inflammatory Disease Datasets to Enhance Indeterminant Lung Cancer Classification using Masked Autoencoders. 慢性肺部炎症疾病数据集的预训练，以增强不确定肺癌分类使用掩膜自编码器。

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Pub Date : 2026-01-01 Epub Date: 2025-09-20 DOI: 10.1007/978-3-032-05169-1_47

Axel H P Masquelin, Raúl San José Estépar

{"title":"Pretraining on Chronic Lung Inflammatory Disease Datasets to Enhance Indeterminant Lung Cancer Classification using Masked Autoencoders.","authors":"Axel H P Masquelin, Raúl San José Estépar","doi":"10.1007/978-3-032-05169-1_47","DOIUrl":"10.1007/978-3-032-05169-1_47","url":null,"abstract":"Lung cancer remains the leading cause of cancer-related mortality in the United States, despite the adoption of low-dose computed tomography (LDCT) and updated screening guidelines from the United States Preventive Service Task Force (USPSTF) [19]. Limited infrastructure and financial costs continue to hinder widespread LDCT adoption, while the increasing detection of indeterminate pulmonary nodules (4-20 mm) challenges accurate diagnosis and clinical decision-making. We address these limitations by pretraining masked autoencoders (MAE) on the COPDGene dataset, which captures chronic lung inflammatory disease features. Emphysema and airway disease, two distinct subtypes of COPD, are pathophysiological manifestations of chronic lung inflammation [4,15]. Incorporating these features may enhance the model's ability to distinguish between malignant and benign pulmonary nodules. By exploring multiple masking strategies, we optimize network attention on parenchymal and perinodular features, improving the extraction of relevant image biomarkers. Our results demonstrate that pretraining on the COPDGene dataset using random masking (r-masking) achieves superior classification performance, with a sensitivity of 88.79%, specificity of 86.27%, and an AUC of 0.931, when compared to self-pretraining on National Lung Cancer Screening Trial (NLST), and supervised learning on NLST. This highlights the importance of leveraging chronic disease datasets for self-supervised learning and underscores the potential of MAE-based approaches to improve nodule classification in clinical settings. Code available at https://github.com/axemasquelin/RegionalMAE.","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15972 ","pages":"485-495"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12922548/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147273594","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}

引用次数: 0

Robust Topographical Representation for Longitudinal Propagation of Tau Pathology. Tau病理纵向传播的鲁棒地形表征。

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Pub Date : 2026-01-01 Epub Date: 2025-09-19 DOI: 10.1007/978-3-032-05162-2_56

Jiaxin Yue, Jianwei Zhang, Xinkai Wang, Yonggang Shi

{"title":"Robust Topographical Representation for Longitudinal Propagation of Tau Pathology.","authors":"Jiaxin Yue, Jianwei Zhang, Xinkai Wang, Yonggang Shi","doi":"10.1007/978-3-032-05162-2_56","DOIUrl":"10.1007/978-3-032-05162-2_56","url":null,"abstract":"Tau pathology is a hallmark of Alzheimer's disease (AD), and longitudinal tau positron emission tomography (PET) provides valuable insights into disease progression. However, the integration of tau PET data into computational models remains limited by challenges in encoding topographical information and ensuring longitudinal consistency. Existing biomarker-based representations often lack spatial flexibility and fail to account for covariance between brain regions. Additionally, traditional approaches often treat longitudinal scans as independent observations, neglecting temporal coherence. To address these limitations, we propose a novel Multiresolutional Reeb Graph representation that encodes the spatiotemporal propagation of tau topographical information. Our method constructs Reeb graphs to capture tau topography at a static time point and extends them into a multiresolutional framework to model disease evolution. We introduce a topology-based measurement for quantifying pathology spatial distribution similarity, and a severity interleaving distance for robust longitudinal staging. The efficiency of the proposed representation is validated in two downstream tasks: an integrated subtyping and staging system, and the longitudinal pathology prediction. The promising results compared with the current methods demonstrate the great potential of the proposed representation to enhancing the application of longitudinal tau PET data, and offering a reliable approach for studying AD progression.","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":"584-593"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12952718/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147350055","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}

引用次数: 0

Multistage Alignment and Fusion for Multimodal Multiclass Alzheimer's Disease Diagnosis. 多模式多类别阿尔茨海默病诊断的多阶段对齐与融合。

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Pub Date : 2026-01-01 Epub Date: 2025-09-18 DOI: 10.1007/978-3-032-05182-0_37

Shuo Huang, Lujia Zhong, Yonggang Shi

{"title":"Multistage Alignment and Fusion for Multimodal Multiclass Alzheimer's Disease Diagnosis.","authors":"Shuo Huang, Lujia Zhong, Yonggang Shi","doi":"10.1007/978-3-032-05182-0_37","DOIUrl":"10.1007/978-3-032-05182-0_37","url":null,"abstract":"For the early diagnosis of Alzheimer's disease (AD), it is essential that we have effective multiclass classification methods that can distinct subjects with mild cognitive impairment (MCI) from cognitively normal (CN) subjects and AD patients. However, significant overlaps of biomarker distributions among these groups make this a difficult task. In this work, we propose a novel framework for multi-modal, multiclass AD diagnosis that can integrate information from diverse and complex modalities to resolve ambiguity among the disease groups and hence enhance classification performances. More specifically, our approach integrates T1-weighted MRI, tau PET, fiber orientation distribution (FOD) from diffusion MRI (dMRI), and Montreal Cognitive Assessment (MoCA) scores to classify subjects into AD, MCI, and CN groups. We introduce a Swin-FOD model to extract order-balanced features from FOD and use contrastive learning to align MRI and PET features. These aligned features and MoCA scores are then processed with a Tabular Prior-data Fitted In-context Learning (TabPFN) method, which selects model parameters based on the alignment between input data and prior data during pre-training, eliminating the need for additional training or fine-tuning. Evaluated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset ( <math><mi>n</mi> <mspace></mspace> <mo>=</mo> <mspace></mspace> <mn>1147</mn></math> ), our model achieved a diagnosis accuracy of 73.21%, outperforming all comparison models ( <math><mi>n</mi> <mo>=</mo> <mn>10</mn></math> ). We also performed Shapley analysis and quantitatively evaluated the essential contributions of each modality.","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":"375-385"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12674853/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145679841","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}

引用次数: 0