PRIME@MICCAI最新文献

Video-Based Hand Pose Estimation for Remote Assessment of Bradykinesia in Parkinson's Disease 基于视频的手部姿势评估用于帕金森病运动迟缓的远程评估

PRIME@MICCAI Pub Date : 2023-08-28 DOI: 10.48550/arXiv.2308.14679

Gabriela T. Acevedo Trebbau, A. Bandini, D. Guarin

{"title":"Video-Based Hand Pose Estimation for Remote Assessment of Bradykinesia in Parkinson's Disease","authors":"Gabriela T. Acevedo Trebbau, A. Bandini, D. Guarin","doi":"10.48550/arXiv.2308.14679","DOIUrl":"https://doi.org/10.48550/arXiv.2308.14679","url":null,"abstract":"There is a growing interest in using pose estimation algorithms for video-based assessment of Bradykinesia in Parkinson's Disease (PD) to facilitate remote disease assessment and monitoring. However, the accuracy of pose estimation algorithms in videos from video streaming services during Telehealth appointments has not been studied. In this study, we used seven off-the-shelf hand pose estimation models to estimate the movement of the thumb and index fingers in videos of the finger-tapping (FT) test recorded from Healthy Controls (HC) and participants with PD and under two different conditions: streaming (videos recorded during a live Zoom meeting) and on-device (videos recorded locally with high-quality cameras). The accuracy and reliability of the models were estimated by comparing the models' output with manual results. Three of the seven models demonstrated good accuracy for on-device recordings, and the accuracy decreased significantly for streaming recordings. We observed a negative correlation between movement speed and the model's accuracy for the streaming recordings. Additionally, we evaluated the reliability of ten movement features related to bradykinesia extracted from video recordings of PD patients performing the FT test. While most of the features demonstrated excellent reliability for on-device recordings, most of the features demonstrated poor to moderate reliability for streaming recordings. Our findings highlight the limitations of pose estimation algorithms when applied to video recordings obtained during Telehealth visits, and demonstrate that on-device recordings can be used for automatic video-assessment of bradykinesia in PD.","PeriodicalId":344481,"journal":{"name":"PRIME@MICCAI","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123894913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Pose2Gait: Extracting Gait Features from Monocular Video of Individuals with Dementia pose2步态:从痴呆患者的单目视频中提取步态特征

PRIME@MICCAI Pub Date : 2023-08-22 DOI: 10.48550/arXiv.2308.11484

Caroline Malin-Mayor, Vida Adeli, Andrea Sabo, S. Noritsyn, C. Gorodetsky, A. Fasano, A. Iaboni, B. Taati

{"title":"Pose2Gait: Extracting Gait Features from Monocular Video of Individuals with Dementia","authors":"Caroline Malin-Mayor, Vida Adeli, Andrea Sabo, S. Noritsyn, C. Gorodetsky, A. Fasano, A. Iaboni, B. Taati","doi":"10.48550/arXiv.2308.11484","DOIUrl":"https://doi.org/10.48550/arXiv.2308.11484","url":null,"abstract":"Video-based ambient monitoring of gait for older adults with dementia has the potential to detect negative changes in health and allow clinicians and caregivers to intervene early to prevent falls or hospitalizations. Computer vision-based pose tracking models can process video data automatically and extract joint locations; however, publicly available models are not optimized for gait analysis on older adults or clinical populations. In this work we train a deep neural network to map from a two dimensional pose sequence, extracted from a video of an individual walking down a hallway toward a wall-mounted camera, to a set of three-dimensional spatiotemporal gait features averaged over the walking sequence. The data of individuals with dementia used in this work was captured at two sites using a wall-mounted system to collect the video and depth information used to train and evaluate our model. Our Pose2Gait model is able to extract velocity and step length values from the video that are correlated with the features from the depth camera, with Spearman's correlation coefficients of .83 and .60 respectively, showing that three dimensional spatiotemporal features can be predicted from monocular video. Future work remains to improve the accuracy of other features, such as step time and step width, and test the utility of the predicted values for detecting meaningful changes in gait during longitudinal ambient monitoring.","PeriodicalId":344481,"journal":{"name":"PRIME@MICCAI","volume":"61 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129167032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Imputing Brain Measurements Across Data Sets via Graph Neural Networks 通过图神经网络跨数据集计算大脑测量值

PRIME@MICCAI Pub Date : 2023-08-19 DOI: 10.48550/arXiv.2308.09907

Yixin Wang, Wei Peng, S. Tapert, Qingyu Zhao, K. Pohl

{"title":"Imputing Brain Measurements Across Data Sets via Graph Neural Networks","authors":"Yixin Wang, Wei Peng, S. Tapert, Qingyu Zhao, K. Pohl","doi":"10.48550/arXiv.2308.09907","DOIUrl":"https://doi.org/10.48550/arXiv.2308.09907","url":null,"abstract":"Publicly available data sets of structural MRIs might not contain specific measurements of brain Regions of Interests (ROIs) that are important for training machine learning models. For example, the curvature scores computed by Freesurfer are not released by the Adolescent Brain Cognitive Development (ABCD) Study. One can address this issue by simply reapplying Freesurfer to the data set. However, this approach is generally computationally and labor intensive (e.g., requiring quality control). An alternative is to impute the missing measurements via a deep learning approach. However, the state-of-the-art is designed to estimate randomly missing values rather than entire measurements. We therefore propose to re-frame the imputation problem as a prediction task on another (public) data set that contains the missing measurements and shares some ROI measurements with the data sets of interest. A deep learning model is then trained to predict the missing measurements from the shared ones and afterwards is applied to the other data sets. Our proposed algorithm models the dependencies between ROI measurements via a graph neural network (GNN) and accounts for demographic differences in brain measurements (e.g. sex) by feeding the graph encoding into a parallel architecture. The architecture simultaneously optimizes a graph decoder to impute values and a classifier in predicting demographic factors. We test the approach, called Demographic Aware Graph-based Imputation (DAGI), on imputing those missing Freesurfer measurements of ABCD (N=3760) by training the predictor on those publicly released by the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA, N=540)...","PeriodicalId":344481,"journal":{"name":"PRIME@MICCAI","volume":"398 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132217901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Self-supervised Landmark Learning with Deformation Reconstruction and Cross-subject Consistency Objectives 具有变形重构和跨学科一致性目标的自监督里程碑学习

PRIME@MICCAI Pub Date : 2023-08-09 DOI: 10.48550/arXiv.2308.04987

Chun-Hung Chao, M. Niethammer

{"title":"Self-supervised Landmark Learning with Deformation Reconstruction and Cross-subject Consistency Objectives","authors":"Chun-Hung Chao, M. Niethammer","doi":"10.48550/arXiv.2308.04987","DOIUrl":"https://doi.org/10.48550/arXiv.2308.04987","url":null,"abstract":"A Point Distribution Model (PDM) is the basis of a Statistical Shape Model (SSM) that relies on a set of landmark points to represent a shape and characterize the shape variation. In this work, we present a self-supervised approach to extract landmark points from a given registration model for the PDMs. Based on the assumption that the landmarks are the points that have the most influence on registration, existing works learn a point-based registration model with a small number of points to estimate the landmark points that influence the deformation the most. However, such approaches assume that the deformation can be captured by point-based registration and quality landmarks can be learned solely with the deformation capturing objective. We argue that data with complicated deformations can not easily be modeled with point-based registration when only a limited number of points is used to extract influential landmark points. Further, landmark consistency is not assured in existing approaches In contrast, we propose to extract landmarks based on a given registration model, which is tailored for the target data, so we can obtain more accurate correspondences. Secondly, to establish the anatomical consistency of the predicted landmarks, we introduce a landmark discovery loss to explicitly encourage the model to predict the landmarks that are anatomically consistent across subjects. We conduct experiments on an osteoarthritis progression prediction task and show our method outperforms existing image-based and point-based approaches.","PeriodicalId":344481,"journal":{"name":"PRIME@MICCAI","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128111307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Self-supervised Few-shot Learning for Semantic Segmentation: An Annotation-free Approach 语义分割的自监督少镜头学习:一种无标注的方法

PRIME@MICCAI Pub Date : 2023-07-26 DOI: 10.48550/arXiv.2307.14446

Sanaz Karimijafarbigloo, Reza Azad, D. Merhof

{"title":"Self-supervised Few-shot Learning for Semantic Segmentation: An Annotation-free Approach","authors":"Sanaz Karimijafarbigloo, Reza Azad, D. Merhof","doi":"10.48550/arXiv.2307.14446","DOIUrl":"https://doi.org/10.48550/arXiv.2307.14446","url":null,"abstract":"Few-shot semantic segmentation (FSS) offers immense potential in the field of medical image analysis, enabling accurate object segmentation with limited training data. However, existing FSS techniques heavily rely on annotated semantic classes, rendering them unsuitable for medical images due to the scarcity of annotations. To address this challenge, multiple contributions are proposed: First, inspired by spectral decomposition methods, the problem of image decomposition is reframed as a graph partitioning task. The eigenvectors of the Laplacian matrix, derived from the feature affinity matrix of self-supervised networks, are analyzed to estimate the distribution of the objects of interest from the support images. Secondly, we propose a novel self-supervised FSS framework that does not rely on any annotation. Instead, it adaptively estimates the query mask by leveraging the eigenvectors obtained from the support images. This approach eliminates the need for manual annotation, making it particularly suitable for medical images with limited annotated data. Thirdly, to further enhance the decoding of the query image based on the information provided by the support image, we introduce a multi-scale large kernel attention module. By selectively emphasizing relevant features and details, this module improves the segmentation process and contributes to better object delineation. Evaluations on both natural and medical image datasets demonstrate the efficiency and effectiveness of our method. Moreover, the proposed approach is characterized by its generality and model-agnostic nature, allowing for seamless integration with various deep architectures. The code is publicly available at href{https://github.com/mindflow-institue/annotation_free_fewshot}{textcolor{magenta}{GitHub}}.","PeriodicalId":344481,"journal":{"name":"PRIME@MICCAI","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115274989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

TransDeepLab: Convolution-Free Transformer-based DeepLab v3+ for Medical Image Segmentation TransDeepLab:基于无卷积变换的DeepLab v3+医学图像分割

PRIME@MICCAI Pub Date : 2022-08-01 DOI: 10.48550/arXiv.2208.00713

Reza Azad, Moein Heidari, M. Shariatnia, Ehsan Khodapanah Aghdam, Sanaz Karimijafarbigloo, E. Adeli, D. Merhof

{"title":"TransDeepLab: Convolution-Free Transformer-based DeepLab v3+ for Medical Image Segmentation","authors":"Reza Azad, Moein Heidari, M. Shariatnia, Ehsan Khodapanah Aghdam, Sanaz Karimijafarbigloo, E. Adeli, D. Merhof","doi":"10.48550/arXiv.2208.00713","DOIUrl":"https://doi.org/10.48550/arXiv.2208.00713","url":null,"abstract":"Convolutional neural networks (CNNs) have been the de facto standard in a diverse set of computer vision tasks for many years. Especially, deep neural networks based on seminal architectures such as U-shaped models with skip-connections or atrous convolution with pyramid pooling have been tailored to a wide range of medical image analysis tasks. The main advantage of such architectures is that they are prone to detaining versatile local features. However, as a general consensus, CNNs fail to capture long-range dependencies and spatial correlations due to the intrinsic property of confined receptive field size of convolution operations. Alternatively, Transformer, profiting from global information modelling that stems from the self-attention mechanism, has recently attained remarkable performance in natural language processing and computer vision. Nevertheless, previous studies prove that both local and global features are critical for a deep model in dense prediction, such as segmenting complicated structures with disparate shapes and configurations. To this end, this paper proposes TransDeepLab, a novel DeepLab-like pure Transformer for medical image segmentation. Specifically, we exploit hierarchical Swin-Transformer with shifted windows to extend the DeepLabv3 and model the Atrous Spatial Pyramid Pooling (ASPP) module. A thorough search of the relevant literature yielded that we are the first to model the seminal DeepLab model with a pure Transformer-based model. Extensive experiments on various medical image segmentation tasks verify that our approach performs superior or on par with most contemporary works on an amalgamation of Vision Transformer and CNN-based methods, along with a significant reduction of model complexity. The codes and trained models are publicly available at https://github.com/rezazad68/transdeeplab","PeriodicalId":344481,"journal":{"name":"PRIME@MICCAI","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125076928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 28

Intervertebral Disc Labeling With Learning Shape Information, A Look Once Approach 椎间盘标记与学习形状信息，一看一次的方法

PRIME@MICCAI Pub Date : 2022-04-06 DOI: 10.48550/arXiv.2204.02943

Reza Azad, Moein Heidari, J. Cohen-Adad, E. Adeli, D. Merhof

{"title":"Intervertebral Disc Labeling With Learning Shape Information, A Look Once Approach","authors":"Reza Azad, Moein Heidari, J. Cohen-Adad, E. Adeli, D. Merhof","doi":"10.48550/arXiv.2204.02943","DOIUrl":"https://doi.org/10.48550/arXiv.2204.02943","url":null,"abstract":"Accurate and automatic segmentation of intervertebral discs from medical images is a critical task for the assessment of spine-related diseases such as osteoporosis, vertebral fractures, and intervertebral disc herniation. To date, various approaches have been developed in the literature which routinely relies on detecting the discs as the primary step. A disadvantage of many cohort studies is that the localization algorithm also yields false-positive detections. In this study, we aim to alleviate this problem by proposing a novel U-Net-based structure to predict a set of candidates for intervertebral disc locations. In our design, we integrate the image shape information (image gradients) to encourage the model to learn rich and generic geometrical information. This additional signal guides the model to selectively emphasize the contextual representation and suppress the less discriminative features. On the post-processing side, to further decrease the false positive rate, we propose a permutation invariant 'look once' model, which accelerates the candidate recovery procedure. In comparison with previous studies, our proposed approach does not need to perform the selection in an iterative fashion. The proposed method was evaluated on the spine generic public multi-center dataset and demonstrated superior performance compared to previous work. We have provided the implementation code in https://github.com/rezazad68/intervertebral-lookonce","PeriodicalId":344481,"journal":{"name":"PRIME@MICCAI","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124502619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

A Few-Shot Learning Graph Multi-trajectory Evolution Network for Forecasting Multimodal Baby Connectivity Development from a Baseline Timepoint 基于基线时间点预测多模态婴儿连通性发展的少镜头学习图多轨迹进化网络

PRIME@MICCAI Pub Date : 2021-10-06 DOI: 10.1007/978-3-030-87602-9_2

Alaa Bessadok, Ahmed Nebli, M. Mahjoub, Gang Li, Weili Lin, D. Shen, I. Rekik

引用次数: 2

One Representative-Shot Learning Using a Population-Driven Template with Application to Brain Connectivity Classification and Evolution Prediction 基于群体驱动模板的一次代表性学习在脑连接分类和进化预测中的应用

PRIME@MICCAI Pub Date : 2021-10-06 DOI: 10.1007/978-3-030-87602-9_3

Umut Guvercin, Mohammed Amine Gharsallaoui, I. Rekik

引用次数: 6

Opportunistic Screening of Osteoporosis Using Plain Film Chest X-ray 利用胸部x线平片筛查骨质疏松症

PRIME@MICCAI Pub Date : 2021-04-05 DOI: 10.1007/978-3-030-87602-9_13

Fakai Wang, K. Zheng, Yirui Wang, Xiaoyun Zhou, Le Lu, Jing Xiao, Min Wu, C. Kuo, S. Miao

引用次数: 3