Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention最新文献_第8页

Bidirectional Mapping with Contrastive Learning on Multimodal Neuroimaging Data. 多模态神经成像数据的双向映射与对比学习

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Pub Date : 2023-10-01 DOI: 10.1007/978-3-031-43898-1_14

Kai Ye, Haoteng Tang, Siyuan Dai, Lei Guo, Johnny Yuehan Liu, Yalin Wang, Alex Leow, Paul M Thompson, Heng Huang, Liang Zhan

{"title":"Bidirectional Mapping with Contrastive Learning on Multimodal Neuroimaging Data.","authors":"Kai Ye, Haoteng Tang, Siyuan Dai, Lei Guo, Johnny Yuehan Liu, Yalin Wang, Alex Leow, Paul M Thompson, Heng Huang, Liang Zhan","doi":"10.1007/978-3-031-43898-1_14","DOIUrl":"10.1007/978-3-031-43898-1_14","url":null,"abstract":"The modeling of the interaction between brain structure and function using deep learning techniques has yielded remarkable success in identifying potential biomarkers for different clinical phenotypes and brain diseases. However, most existing studies focus on one-way mapping, either projecting brain function to brain structure or inversely. This type of unidirectional mapping approach is limited by the fact that it treats the mapping as a one-way task and neglects the intrinsic unity between these two modalities. Moreover, when dealing with the same biological brain, mapping from structure to function and from function to structure yields dissimilar outcomes, highlighting the likelihood of bias in one-way mapping. To address this issue, we propose a novel bidirectional mapping model, named Bidirectional Mapping with Contrastive Learning (BMCL), to reduce the bias between these two unidirectional mappings via ROI-level contrastive learning. We evaluate our framework on clinical phenotype and neurodegenerative disease predictions using two publicly available datasets (HCP and OASIS). Our results demonstrate the superiority of BMCL compared to several state-of-the-art methods.","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"14222 ","pages":"138-148"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11245326/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141617890","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

ACTION++: Improving Semi-supervised Medical Image Segmentation with Adaptive Anatomical Contrast. ACTION++：利用自适应解剖对比度改进半监督医学图像分割。

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Pub Date : 2023-10-01 DOI: 10.1007/978-3-031-43901-8_19

Chenyu You, Weicheng Dai, Yifei Min, Lawrence Staib, Jas Sekhon, James S Duncan

{"title":"ACTION++: Improving Semi-supervised Medical Image Segmentation with Adaptive Anatomical Contrast.","authors":"Chenyu You, Weicheng Dai, Yifei Min, Lawrence Staib, Jas Sekhon, James S Duncan","doi":"10.1007/978-3-031-43901-8_19","DOIUrl":"10.1007/978-3-031-43901-8_19","url":null,"abstract":"Medical data often exhibits long-tail distributions with heavy class imbalance, which naturally leads to difficulty in classifying the minority classes (i.e., boundary regions or rare objects). Recent work has significantly improved semi-supervised medical image segmentation in long-tailed scenarios by equipping them with unsupervised contrastive criteria. However, it remains unclear how well they will perform in the labeled portion of data where class distribution is also highly imbalanced. In this work, we present ACTION++, an improved contrastive learning framework with adaptive anatomical contrast for semi-supervised medical segmentation. Specifically, we propose an adaptive supervised contrastive loss, where we first compute the optimal locations of class centers uniformly distributed on the embedding space (i.e., off-line), and then perform online contrastive matching training by encouraging different class features to adaptively match these distinct and uniformly distributed class centers. Moreover, we argue that blindly adopting a constant temperature <math><mi>τ</mi></math> in the contrastive loss on long-tailed medical data is not optimal, and propose to use a dynamic <math><mi>τ</mi></math> via a simple cosine schedule to yield better separation between majority and minority classes. Empirically, we evaluate ACTION++ on ACDC and LA benchmarks and show that it achieves state-of-the-art across two semi-supervised settings. Theoretically, we analyze the performance of adaptive anatomical contrast and confirm its superiority in label efficiency.","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"14223 ","pages":"194-205"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11136572/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141177034","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

Speech Audio Synthesis from Tagged MRI and Non-Negative Matrix Factorization via Plastic Transformer. 通过塑料变压器从标记磁共振成像和非负矩阵因式分解合成语音音频

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Pub Date : 2023-10-01 DOI: 10.1007/978-3-031-43990-2_41

Xiaofeng Liu, Fangxu Xing, Maureen Stone, Jiachen Zhuo, Sidney Fels, Jerry L Prince, Georges El Fakhri, Jonghye Woo

{"title":"Speech Audio Synthesis from Tagged MRI and Non-Negative Matrix Factorization via Plastic Transformer.","authors":"Xiaofeng Liu, Fangxu Xing, Maureen Stone, Jiachen Zhuo, Sidney Fels, Jerry L Prince, Georges El Fakhri, Jonghye Woo","doi":"10.1007/978-3-031-43990-2_41","DOIUrl":"https://doi.org/10.1007/978-3-031-43990-2_41","url":null,"abstract":"The tongue's intricate 3D structure, comprising localized functional units, plays a crucial role in the production of speech. When measured using tagged MRI, these functional units exhibit cohesive displacements and derived quantities that facilitate the complex process of speech production. Non-negative matrix factorization-based approaches have been shown to estimate the functional units through motion features, yielding a set of building blocks and a corresponding weighting map. Investigating the link between weighting maps and speech acoustics can offer significant insights into the intricate process of speech production. To this end, in this work, we utilize two-dimensional spectrograms as a proxy representation, and develop an end-to-end deep learning framework for translating weighting maps to their corresponding audio waveforms. Our proposed plastic light transformer (PLT) framework is based on directional product relative position bias and single-level spatial pyramid pooling, thus enabling flexible processing of weighting maps with variable size to fixed-size spectrograms, without input information loss or dimension expansion. Additionally, our PLT framework efficiently models the global correlation of wide matrix input. To improve the realism of our generated spectrograms with relatively limited training samples, we apply pair-wise utterance consistency with Maximum Mean Discrepancy constraint and adversarial training. Experimental results on a dataset of 29 subjects speaking two utterances demonstrated that our framework is able to synthesize speech audio waveforms from weighting maps, outperforming conventional convolution and transformer models.","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"14226 ","pages":"435-445"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11034915/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140863045","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

Mesh2SSM: From Surface Meshes to Statistical Shape Models of Anatomy. Mesh2SSM：从表面网格到解剖学统计形状模型。

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Pub Date : 2023-10-01 DOI: 10.1007/978-3-031-43907-0_59

Krithika Iyer, Shireen Elhabian

{"title":"Mesh2SSM: From Surface Meshes to Statistical Shape Models of Anatomy.","authors":"Krithika Iyer, Shireen Elhabian","doi":"10.1007/978-3-031-43907-0_59","DOIUrl":"10.1007/978-3-031-43907-0_59","url":null,"abstract":"Statistical shape modeling is the computational process of discovering significant shape parameters from segmented anatomies captured by medical images (such as MRI and CT scans), which can fully describe subject-specific anatomy in the context of a population. The presence of substantial non-linear variability in human anatomy often makes the traditional shape modeling process challenging. Deep learning techniques can learn complex non-linear representations of shapes and generate statistical shape models that are more faithful to the underlying population-level variability. However, existing deep learning models still have limitations and require established/optimized shape models for training. We propose Mesh2SSM, a new approach that leverages unsupervised, permutation-invariant representation learning to estimate how to deform a template point cloud to subject-specific meshes, forming a correspondence-based shape model. Mesh2SSM can also learn a population-specific template, reducing any bias due to template selection. The proposed method operates directly on meshes and is computationally efficient, making it an attractive alternative to traditional and deep learning-based SSM approaches.","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"14220 ","pages":"615-625"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11036176/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140862102","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

Uncovering Heterogeneity in Alzheimer's Disease from Graphical Modeling of the Tau Spatiotemporal Topography. 从 Tau 时空地形图的图形建模中揭示阿尔茨海默病的异质性

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Pub Date : 2023-10-01 DOI: 10.1007/978-3-031-43904-9_26

Jiaxin Yue, Yonggang Shi

{"title":"Uncovering Heterogeneity in Alzheimer's Disease from Graphical Modeling of the Tau Spatiotemporal Topography.","authors":"Jiaxin Yue, Yonggang Shi","doi":"10.1007/978-3-031-43904-9_26","DOIUrl":"10.1007/978-3-031-43904-9_26","url":null,"abstract":"Growing evidence from post-mortem and in vivo studies have demonstrated the substantial variability of tau pathology spreading patterns in Alzheimer's disease(AD). Automated tools for characterizing the heterogeneity of tau pathology will enable a more accurate understanding of the disease and help the development of targeted treatment. In this paper, we propose a Reeb graph representation of tau pathology topography on cortical surfaces using tau PET imaging data. By comparing the spatial and temporal coherence of the Reeb graph representation across subjects, we can build a directed graph to represent the distribution of tau topography over a population, which naturally facilitates the discovery of spatiotemporal subtypes of tau pathology with graph-based clustering. In our experiments, we conducted extensive comparisons with state-of-the-art event-based model on synthetic and large-scale tau PET imaging data from ADNI3 and A4 studies. We demonstrated that our proposed method can more robustly achieve the subtyping of tau pathology with clear clinical significance and demonstrated superior generalization performance than event-based model.","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"14224 ","pages":"262-271"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10951551/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140178532","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

Personalized Patch-based Normality Assessment of Brain Atrophy in Alzheimer's Disease. 基于贴片的阿尔茨海默病脑萎缩正常性个性化评估

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Pub Date : 2023-10-01 DOI: 10.1007/978-3-031-43904-9_6

Jianwei Zhang, Yonggang Shi

{"title":"Personalized Patch-based Normality Assessment of Brain Atrophy in Alzheimer's Disease.","authors":"Jianwei Zhang, Yonggang Shi","doi":"10.1007/978-3-031-43904-9_6","DOIUrl":"10.1007/978-3-031-43904-9_6","url":null,"abstract":"Cortical thickness is an important biomarker associated with gray matter atrophy in neurodegenerative diseases. In order to conduct meaningful comparisons of cortical thickness between different subjects, it is imperative to establish correspondence among surface meshes. Conventional methods achieve this by projecting surface onto canonical domains such as the unit sphere or averaging feature values in anatomical regions of interest (ROIs). However, due to the natural variability in cortical topography, perfect anatomically meaningful one-to-one mapping can be hardly achieved and the practice of averaging leads to the loss of detailed information. For example, two subjects may have different number of gyral structures in the same region, and thus mapping can result in gyral/sulcal mismatch which introduces noise and averaging in detailed local information loss. Therefore, it is necessary to develop new method that can overcome these intrinsic problems to construct more meaningful comparison for atrophy detection. To address these limitations, we propose a novel personalized patch-based method to improve cortical thickness comparison across subjects. Our model segments the brain surface into patches based on gyral and sulcal structures to reduce mismatches in mapping method while still preserving detailed topological information which is potentially discarded in averaging. Moreover,the personalized templates for each patch account for the variability of folding patterns, as not all subjects are comparable. Finally, through normality assessment experiments, we demonstrate that our model performs better than standard spherical registration in detecting atrophy in patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD).","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"14224 ","pages":"55-62"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10948101/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140159783","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

Utilizing Longitudinal Chest X-Rays and Reports to Pre-fill Radiology Reports. 利用纵向胸部 X 光片和报告预填放射科报告。

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Pub Date : 2023-10-01 DOI: 10.1007/978-3-031-43904-9_19

Qingqing Zhu, Tejas Sudharshan Mathai, Pritam Mukherjee, Yifan Peng, Ronald M Summers, Zhiyong Lu

{"title":"Utilizing Longitudinal Chest X-Rays and Reports to Pre-fill Radiology Reports.","authors":"Qingqing Zhu, Tejas Sudharshan Mathai, Pritam Mukherjee, Yifan Peng, Ronald M Summers, Zhiyong Lu","doi":"10.1007/978-3-031-43904-9_19","DOIUrl":"10.1007/978-3-031-43904-9_19","url":null,"abstract":"Despite the reduction in turn-around times in radiology reporting with the use of speech recognition software, persistent communication errors can significantly impact the interpretation of radiology reports. Pre-filling a radiology report holds promise in mitigating reporting errors, and despite multiple efforts in literature to generate comprehensive medical reports, there lacks approaches that exploit the longitudinal nature of patient visit records in the MIMIC-CXR dataset. To address this gap, we propose to use longitudinal multi-modal data, i.e., previous patient visit CXR, current visit CXR, and the previous visit report, to pre-fill the \"findings\" section of the patient's current visit. We first gathered the longitudinal visit information for 26,625 patients from the MIMIC-CXR dataset, and created a new dataset called Longitudinal-MIMIC. With this new dataset, a transformer-based model was trained to capture the multi-modal longitudinal information from patient visit records (CXR images + reports) via a cross-attention-based multi-modal fusion module and a hierarchical memory-driven decoder. In contrast to previous works that only uses current visit data as input to train a model, our work exploits the longitudinal information available to pre-fill the \"findings\" section of radiology reports. Experiments show that our approach outperforms several recent approaches by ≥3% on F1 score, and ≥2% for BLEU-4, METEOR and ROUGE-L respectively. Code will be published at https://github.com/CelestialShine/Longitudinal-Chest-X-Ray.","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"14224 ","pages":"189-198"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10947431/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140159784","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

Geometry-invariant abnormality detection. 几何不变异常检测

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Pub Date : 2023-01-10 DOI: 10.1007/978-3-031-43907-0_29

Ashay Patel, Petru-Daniel Tudosiu, Walter Hugo Lopez Pinaya, Olusola Adeleke, Gary Cook, Vicky Goh, Sebastien Ourselin, M Jorge Cardoso

{"title":"Geometry-invariant abnormality detection.","authors":"Ashay Patel, Petru-Daniel Tudosiu, Walter Hugo Lopez Pinaya, Olusola Adeleke, Gary Cook, Vicky Goh, Sebastien Ourselin, M Jorge Cardoso","doi":"10.1007/978-3-031-43907-0_29","DOIUrl":"10.1007/978-3-031-43907-0_29","url":null,"abstract":"Cancer is a highly heterogeneous condition best visualised in positron emission tomography. Due to this heterogeneity, a general-purpose cancer detection model can be built using unsupervised learning anomaly detection models. While prior work in this field has showcased the efficacy of abnormality detection methods (e.g. Transformer-based), these have shown significant vulnerabilities to differences in data geometry. Changes in image resolution or observed field of view can result in inaccurate predictions, even with significant data pre-processing and augmentation. We propose a new spatial conditioning mechanism that enables models to adapt and learn from varying data geometries, and apply it to a state-of-the-art Vector-Quantized Variational Autoencoder + Transformer abnormality detection model. We showcase that this spatial conditioning mechanism statistically-significantly improves model performance on whole-body data compared to the same model without conditioning, while allowing the model to perform inference at varying data geometries.","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"2023 ","pages":"300-309"},"PeriodicalIF":0.0,"publicationDate":"2023-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7616404/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142116728","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

Adversarial Consistency for Single Domain Generalization in Medical Image Segmentation. 医学图像分割中单域泛化的对抗一致性。

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Pub Date : 2022-09-01 Epub Date: 2022-09-17 DOI: 10.1007/978-3-031-16449-1_64

Yanwu Xu, Shaoan Xie, Maxwell Reynolds, Matthew Ragoza, Mingming Gong, Kayhan Batmanghelich

{"title":"Adversarial Consistency for Single Domain Generalization in Medical Image Segmentation.","authors":"Yanwu Xu, Shaoan Xie, Maxwell Reynolds, Matthew Ragoza, Mingming Gong, Kayhan Batmanghelich","doi":"10.1007/978-3-031-16449-1_64","DOIUrl":"10.1007/978-3-031-16449-1_64","url":null,"abstract":"An organ segmentation method that can generalize to unseen contrasts and scanner settings can significantly reduce the need for retraining of deep learning models. Domain Generalization (DG) aims to achieve this goal. However, most DG methods for segmentation require training data from multiple domains during training. We propose a novel adversarial domain generalization method for organ segmentation trained on data from a single domain. We synthesize the new domains via learning an adversarial domain synthesizer (ADS) and presume that the synthetic domains cover a large enough area of plausible distributions so that unseen domains can be interpolated from synthetic domains. We propose a mutual information regularizer to enforce the semantic consistency between images from the synthetic domains, which can be estimated by patch-level contrastive learning. We evaluate our method for various organ segmentation for unseen modalities, scanning protocols, and scanner sites.","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"13437 ","pages":"671-681"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11164048/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141302362","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

Unified Embeddings of Structural and Functional Connectome via a Function-Constrained Structural Graph Variational Auto-Encoder. 通过函数约束结构图变异自动编码器统一嵌入结构和功能连接组

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Pub Date : 2022-09-01 Epub Date: 2022-09-15 DOI: 10.1007/978-3-031-16431-6_39

Carlo Amodeo, Igor Fortel, Olusola Ajilore, Liang Zhan, Alex Leow, Theja Tulabandhula

{"title":"Unified Embeddings of Structural and Functional Connectome via a Function-Constrained Structural Graph Variational Auto-Encoder.","authors":"Carlo Amodeo, Igor Fortel, Olusola Ajilore, Liang Zhan, Alex Leow, Theja Tulabandhula","doi":"10.1007/978-3-031-16431-6_39","DOIUrl":"10.1007/978-3-031-16431-6_39","url":null,"abstract":"Graph theoretical analyses have become standard tools in modeling functional and anatomical connectivity in the brain. With the advent of connectomics, the primary graphs or networks of interest are structural connectome (derived from DTI tractography) and functional connectome (derived from resting-state fMRI). However, most published connectome studies have focused on either structural or functional connectome, yet complementary information between them, when available in the same dataset, can be jointly leveraged to improve our understanding of the brain. To this end, we propose a function-constrained structural graph variational autoencoder (FCS-GVAE) capable of incorporating information from both functional and structural connectome in an unsupervised fashion. This leads to a joint low-dimensional embedding that establishes a unified spatial coordinate system for comparing across different subjects. We evaluate our approach using the publicly available OASIS-3 Alzheimer's disease (AD) dataset and show that a variational formulation is necessary to optimally encode functional brain dynamics. Further, the proposed joint embedding approach can more accurately distinguish different patient sub-populations than approaches that do not use complementary connectome information.","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"13431 ","pages":"406-415"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11246745/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141617891","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