Shape in Medical Imaging : International Workshop, ShapeMI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings最新文献

Combined Estimation of Shape and Pose for Statistical Analysis of Articulating Joints. 关节统计分析中形状和姿态的联合估计。

Shape in Medical Imaging : International Workshop, ShapeMI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings Pub Date : 2020-10-01 Epub Date: 2020-10-03 DOI: 10.1007/978-3-030-61056-2_9

Praful Agrawal, Joseph D Mozingo, Shireen Y Elhabian, Andrew E Anderson, Ross T Whitaker

{"title":"Combined Estimation of Shape and Pose for Statistical Analysis of Articulating Joints.","authors":"Praful Agrawal, Joseph D Mozingo, Shireen Y Elhabian, Andrew E Anderson, Ross T Whitaker","doi":"10.1007/978-3-030-61056-2_9","DOIUrl":"https://doi.org/10.1007/978-3-030-61056-2_9","url":null,"abstract":"Quantifying shape variations in articulated joints is of utmost interest to understand the underlying joint biomechanics and associated clinical symptoms. For joint comparisons and analysis, the relative positions of the bones can confound subsequent analysis. Clinicians design specific image acquisition protocols to neutralize the individual pose variations. However, recent studies have shown that even specific acquisition protocols fail to achieve consistent pose. The individual pose variations are largely attributed to the day-to-day functioning of the patient, such as gait during walk, as well as interactions between specific morphologies and joint alignment. This paper presents a novel two-step method to neutralize such patient-specific variations while simultaneously preserving the inherent relationship of the articulated joint. The resulting shape models are then used to discover clinically relevant shape variations in a population of hip joints.","PeriodicalId":87360,"journal":{"name":"Shape in Medical Imaging : International Workshop, ShapeMI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings","volume":"12474 ","pages":"111-121"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7962350/pdf/nihms-1676557.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25493945","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: 4

Patient Specific Classification of Dental Root Canal and Crown Shape. 患者的根管分类和牙冠形状。

Shape in Medical Imaging : International Workshop, ShapeMI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings Pub Date : 2020-10-01 Epub Date: 2020-10-03 DOI: 10.1007/978-3-030-61056-2_12

Maxime Dumont, Juan Carlos Prieto, Serge Brosset, Lucia Cevidanes, Jonas Bianchi, Antonio Ruellas, Marcela Gurgel, Camila Massaro, Aron Aliaga Del Castillo, Marcos Ioshida, Marilia Yatabe, Erika Benavides, Hector Rios, Fabiana Soki, Gisele Neiva, Juan Fernando Aristizabal, Diego Rey, Maria Antonia Alvarez, Kayvan Najarian, Jonathan Gryak, Martin Styner, Jean-Christophe Fillion-Robin, Beatriz Paniagua, Reza Soroushmehr

{"title":"Patient Specific Classification of Dental Root Canal and Crown Shape.","authors":"Maxime Dumont, Juan Carlos Prieto, Serge Brosset, Lucia Cevidanes, Jonas Bianchi, Antonio Ruellas, Marcela Gurgel, Camila Massaro, Aron Aliaga Del Castillo, Marcos Ioshida, Marilia Yatabe, Erika Benavides, Hector Rios, Fabiana Soki, Gisele Neiva, Juan Fernando Aristizabal, Diego Rey, Maria Antonia Alvarez, Kayvan Najarian, Jonathan Gryak, Martin Styner, Jean-Christophe Fillion-Robin, Beatriz Paniagua, Reza Soroushmehr","doi":"10.1007/978-3-030-61056-2_12","DOIUrl":"https://doi.org/10.1007/978-3-030-61056-2_12","url":null,"abstract":"This paper proposes machine learning approaches to support dentistry researchers in the context of integrating imaging modalities to analyze the morphology of tooth crowns and roots. One of the challenges to jointly analyze crowns and roots with precision is that two different image modalities are needed. Precision in dentistry is mainly driven by dental crown surfaces characteristics, but information on tooth root shape and position is of great value for successful root canal preparation, pulp regeneration, planning of orthodontic movement, restorative and implant dentistry. An innovative approach is to use image processing and machine learning to combine crown surfaces, obtained by intraoral scanners, with three dimensional volumetric images of the jaws and teeth root canals, obtained by cone beam computed tomography. In this paper, we propose a patient specific classification of dental root canal and crown shape analysis workflow that is widely applicable.","PeriodicalId":87360,"journal":{"name":"Shape in Medical Imaging : International Workshop, ShapeMI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings","volume":"12474 ","pages":"145-153"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7773155/pdf/nihms-1656402.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38770261","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: 6

Interpretation of Brain Morphology in Association to Alzheimer's Disease Dementia Classification Using Graph Convolutional Networks on Triangulated Meshes. 使用三角网格上的图卷积网络解读与阿尔茨海默病痴呆症分类相关的大脑形态学。

Shape in Medical Imaging : International Workshop, ShapeMI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings Pub Date : 2020-10-01 Epub Date: 2020-10-03 DOI: 10.1007/978-3-030-61056-2_8

Emanuel Azcona, Pierre Besson, Yunan Wu, Arjun Punjabi, Adam Martersteck, Amil Dravid, Todd B Parrish, S Kathleen Bandt, Aggelos K Katsaggelos

{"title":"Interpretation of Brain Morphology in Association to Alzheimer's Disease Dementia Classification Using Graph Convolutional Networks on Triangulated Meshes.","authors":"Emanuel Azcona, Pierre Besson, Yunan Wu, Arjun Punjabi, Adam Martersteck, Amil Dravid, Todd B Parrish, S Kathleen Bandt, Aggelos K Katsaggelos","doi":"10.1007/978-3-030-61056-2_8","DOIUrl":"10.1007/978-3-030-61056-2_8","url":null,"abstract":"We propose a mesh-based technique to aid in the classification of Alzheimer's disease dementia (ADD) using mesh representations of the cortex and subcortical structures. Deep learning methods for classification tasks that utilize structural neuroimaging often require extensive learning parameters to optimize. Frequently, these approaches for automated medical diagnosis also lack visual interpretability for areas in the brain involved in making a diagnosis. This work: (a) analyzes brain shape using surface information of the cortex and subcortical structures, (b) proposes a residual learning framework for state-of-the-art graph convolutional networks which offer a significant reduction in learnable parameters, and (c) offers visual interpretability of the network via class-specific gradient information that localizes important regions of interest in our inputs. With our proposed method leveraging the use of cortical and subcortical surface information, we outperform other machine learning methods with a 96.35% testing accuracy for the ADD vs. healthy control problem. We confirm the validity of our model by observing its performance in a 25-trial Monte Carlo cross-validation. The generated visualization maps in our study show correspondences with current knowledge regarding the structural localization of pathological changes in the brain associated to dementia of the Alzheimer's type.","PeriodicalId":87360,"journal":{"name":"Shape in Medical Imaging : International Workshop, ShapeMI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings","volume":"12474 ","pages":"95-107"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7713521/pdf/nihms-1647904.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38343215","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

Uncertain-DeepSSM: From Images to Probabilistic Shape Models. Uncertain-DeepSSM：从图像到概率形状模型。

Shape in Medical Imaging : International Workshop, ShapeMI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings Pub Date : 2020-10-01 Epub Date: 2020-10-03 DOI: 10.1007/978-3-030-61056-2_5

Jadie Adams, Riddhish Bhalodia, Shireen Elhabian

{"title":"Uncertain-DeepSSM: From Images to Probabilistic Shape Models.","authors":"Jadie Adams, Riddhish Bhalodia, Shireen Elhabian","doi":"10.1007/978-3-030-61056-2_5","DOIUrl":"10.1007/978-3-030-61056-2_5","url":null,"abstract":"Statistical shape modeling (SSM) has recently taken advantage of advances in deep learning to alleviate the need for a time-consuming and expert-driven workflow of anatomy segmentation, shape registration, and the optimization of population-level shape representations. DeepSSM is an end-to-end deep learning approach that extracts statistical shape representation directly from unsegmented images with little manual overhead. It performs comparably with state-of-the-art shape modeling methods for estimating morphologies that are viable for subsequent downstream tasks. Nonetheless, DeepSSM produces an overconfident estimate of shape that cannot be blindly assumed to be accurate. Hence, conveying what DeepSSM does not know, via quantifying granular estimates of uncertainty, is critical for its direct clinical application as an on-demand diagnostic tool to determine how trustworthy the model output is. Here, we propose Uncertain-DeepSSM as a unified model that quantifies both, data-dependent aleatoric uncertainty by adapting the network to predict intrinsic input variance, and model-dependent epistemic uncertainty via a Monte Carlo dropout sampling to approximate a variational distribution over the network parameters. Experiments show an accuracy improvement over DeepSSM while maintaining the same benefits of being end-to-end with little pre-processing.","PeriodicalId":87360,"journal":{"name":"Shape in Medical Imaging : International Workshop, ShapeMI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings","volume":"12474 ","pages":"57-72"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8011333/pdf/nihms-1676365.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25576107","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

Shape in Medical Imaging: International Workshop, ShapeMI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings 医学成像中的形状:国际研讨会，ShapeMI 2020，与MICCAI 2020一起举行，秘鲁利马，2020年10月4日，会议录

Shape in Medical Imaging : International Workshop, ShapeMI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings Pub Date : 2020-01-01 DOI: 10.1007/978-3-030-61056-2

M. Reuter, C. Wachinger, H. Lombaert, B. Paniagua, O. Goksel, I. Rekik

引用次数: 0