Statistical atlases and computational models of the heart. STACOM (Workshop)最新文献

Statistical shape analysis of the tricuspid valve in hypoplastic left heart sydrome. 发育不全左心综合征三尖瓣的统计形状分析。

Statistical atlases and computational models of the heart. STACOM (Workshop) Pub Date : 2022-09-01 Epub Date: 2022-01-01 DOI: 10.1007/978-3-030-93722-5_15

Jared Vicory, Christian Herz, David Allemang, Hannah H Nam, Alana Cianciulli, Chad Vigil, Ye Han, Andras Lasso, Matthew A Jolley, Beatriz Paniagua

{"title":"Statistical shape analysis of the tricuspid valve in hypoplastic left heart sydrome.","authors":"Jared Vicory, Christian Herz, David Allemang, Hannah H Nam, Alana Cianciulli, Chad Vigil, Ye Han, Andras Lasso, Matthew A Jolley, Beatriz Paniagua","doi":"10.1007/978-3-030-93722-5_15","DOIUrl":"10.1007/978-3-030-93722-5_15","url":null,"abstract":"Hypoplastic left heart syndrome (HLHS) is a congenital heart disease characterized by incomplete development of the left heart. Children with HLHS undergo a series of operations which result in the tricuspid valve (TV) becoming the only functional atrioventricular valve. Some of those patients develop tricuspid regurgitation which is associated with heart failure and death and necessitates further surgical intervention. Repair of the regurgitant TV, and understanding the connections between structure and function of this valve remains extremely challenging. Adult cardiac populations have used 3D echocardiography (3DE) combined with computational modeling to better understand cardiac conditions affecting the TV. However, these structure-function analyses rely on simplistic point-based techniques that do not capture the leaflet surface in detail, nor do they allow robust comparison of shapes across groups. We propose using statistical shape modeling and analysis of the TV using Spherical Harmonic Representation Point Distribution Models (SPHARM-PDM) in order to generate a reproducible representation, which in turn enables high dimensional low sample size statistical analysis techniques such as principal component analysis and distance weighted discrimination. Our initial results suggest that visualization of the differences in regurgitant vs. non-regurgitant valves can precisely locate populational structural differences as well as how an individual regurgitant valve differs from the mean shape of functional valves. We believe that these results will support the creation of modern image-based modeling tools, and ultimately increase the understanding of the relationship between valve structure and function needed to inform and improve surgical planning in HLHS.","PeriodicalId":74866,"journal":{"name":"Statistical atlases and computational models of the heart. STACOM (Workshop)","volume":"13131 ","pages":"132-140"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8788948/pdf/nihms-1759249.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39866544","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

Statistical Shape Modeling of Biventricular Anatomy with Shared Boundaries 具有共享边界的双心室解剖统计形状建模

Statistical atlases and computational models of the heart. STACOM (Workshop) Pub Date : 2022-09-01 DOI: 10.48550/arXiv.2209.02706

Krithika S. Iyer, A. Morris, B. Zenger, Karthik Karnath, Benjamin A Orkild, O. Korshak, Shireen Elhabian

{"title":"Statistical Shape Modeling of Biventricular Anatomy with Shared Boundaries","authors":"Krithika S. Iyer, A. Morris, B. Zenger, Karthik Karnath, Benjamin A Orkild, O. Korshak, Shireen Elhabian","doi":"10.48550/arXiv.2209.02706","DOIUrl":"https://doi.org/10.48550/arXiv.2209.02706","url":null,"abstract":"Statistical shape modeling (SSM) is a valuable and powerful tool to generate a detailed representation of complex anatomy that enables quantitative analysis and the comparison of shapes and their variations. SSM applies mathematics, statistics, and computing to parse the shape into a quantitative representation (such as correspondence points or landmarks) that will help answer various questions about the anatomical variations across the population. Complex anatomical structures have many diverse parts with varying interactions or intricate architecture. For example, the heart is a four-chambered anatomy with several shared boundaries between chambers. Coordinated and efficient contraction of the chambers of the heart is necessary to adequately perfuse end organs throughout the body. Subtle shape changes within these shared boundaries of the heart can indicate potential pathological changes that lead to uncoordinated contraction and poor end-organ perfusion. Early detection and robust quantification could provide insight into ideal treatment techniques and intervention timing. However, existing SSM approaches fall short of explicitly modeling the statistics of shared boundaries. In this paper, we present a general and flexible data-driven approach for building statistical shape models of multi-organ anatomies with shared boundaries that captures morphological and alignment changes of individual anatomies and their shared boundary surfaces throughout the population. We demonstrate the effectiveness of the proposed methods using a biventricular heart dataset by developing shape models that consistently parameterize the cardiac biventricular structure and the interventricular septum (shared boundary surface) across the population data.","PeriodicalId":74866,"journal":{"name":"Statistical atlases and computational models of the heart. STACOM (Workshop)","volume":"52 1","pages":"302-316"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73727083","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: 1

Spatiotemporal Cardiac Statistical Shape Modeling: A Data-Driven Approach 时空心脏统计形状建模:数据驱动的方法

Statistical atlases and computational models of the heart. STACOM (Workshop) Pub Date : 2022-09-01 DOI: 10.48550/arXiv.2209.02736

Jadie Adams, N. Khan, A. Morris, Shireen Elhabian

{"title":"Spatiotemporal Cardiac Statistical Shape Modeling: A Data-Driven Approach","authors":"Jadie Adams, N. Khan, A. Morris, Shireen Elhabian","doi":"10.48550/arXiv.2209.02736","DOIUrl":"https://doi.org/10.48550/arXiv.2209.02736","url":null,"abstract":"Clinical investigations of anatomy's structural changes over time could greatly benefit from population-level quantification of shape, or spatiotemporal statistic shape modeling (SSM). Such a tool enables characterizing patient organ cycles or disease progression in relation to a cohort of interest. Constructing shape models requires establishing a quantitative shape representation (e.g., corresponding landmarks). Particle-based shape modeling (PSM) is a data-driven SSM approach that captures population-level shape variations by optimizing landmark placement. However, it assumes cross-sectional study designs and hence has limited statistical power in representing shape changes over time. Existing methods for modeling spatiotemporal or longitudinal shape changes require predefined shape atlases and pre-built shape models that are typically constructed cross-sectionally. This paper proposes a data-driven approach inspired by the PSM method to learn population-level spatiotemporal shape changes directly from shape data. We introduce a novel SSM optimization scheme that produces landmarks that are in correspondence both across the population (inter-subject) and across time-series (intra-subject). We apply the proposed method to 4D cardiac data from atrial-fibrillation patients and demonstrate its efficacy in representing the dynamic change of the left atrium. Furthermore, we show that our method outperforms an image-based approach for spatiotemporal SSM with respect to a generative time-series model, the Linear Dynamical System (LDS). LDS fit using a spatiotemporal shape model optimized via our approach provides better generalization and specificity, indicating it accurately captures the underlying time-dependency.","PeriodicalId":74866,"journal":{"name":"Statistical atlases and computational models of the heart. STACOM (Workshop)","volume":"58 1","pages":"143-156"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76043133","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: 3

Statistical Shape Modeling of Biventricular Anatomy with Shared Boundaries. 具有共享边界的双心室解剖统计形状建模

Statistical atlases and computational models of the heart. STACOM (Workshop) Pub Date : 2022-09-01 Epub Date: 2023-01-28 DOI: 10.1007/978-3-031-23443-9_28

Krithika Iyer, Alan Morris, Brian Zenger, Karthik Karanth, Benjamin A Orkild, Oleksandre Korshak, Shireen Elhabian

{"title":"Statistical Shape Modeling of Biventricular Anatomy with Shared Boundaries.","authors":"Krithika Iyer, Alan Morris, Brian Zenger, Karthik Karanth, Benjamin A Orkild, Oleksandre Korshak, Shireen Elhabian","doi":"10.1007/978-3-031-23443-9_28","DOIUrl":"10.1007/978-3-031-23443-9_28","url":null,"abstract":"Statistical shape modeling (SSM) is a valuable and powerful tool to generate a detailed representation of complex anatomy that enables quantitative analysis and the comparison of shapes and their variations. SSM applies mathematics, statistics, and computing to parse the shape into a quantitative representation (such as correspondence points or landmarks) that will help answer various questions about the anatomical variations across the population. Complex anatomical structures have many diverse parts with varying interactions or intricate architecture. For example, the heart is a four-chambered anatomy with several shared boundaries between chambers. Coordinated and efficient contraction of the chambers of the heart is necessary to adequately perfuse end organs throughout the body. Subtle shape changes within these shared boundaries of the heart can indicate potential pathological changes that lead to uncoordinated contraction and poor end-organ perfusion. Early detection and robust quantification could provide insight into ideal treatment techniques and intervention timing. However, existing SSM approaches fall short of explicitly modeling the statistics of shared boundaries. In this paper, we present a general and flexible data-driven approach for building statistical shape models of multi-organ anatomies with shared boundaries that captures morphological and alignment changes of individual anatomies and their shared boundary surfaces throughout the population. We demonstrate the effectiveness of the proposed methods using a biventricular heart dataset by developing shape models that consistently parameterize the cardiac biventricular structure and the interventricular septum (shared boundary surface) across the population data.","PeriodicalId":74866,"journal":{"name":"Statistical atlases and computational models of the heart. STACOM (Workshop)","volume":"13593 ","pages":"302-316"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10103081/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9379519","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

Skeletal model-based analysis of the tricuspid valve in hypoplastic left heart syndrome. 基于骨骼模型的左心发育不全综合征三尖瓣分析。

Statistical atlases and computational models of the heart. STACOM (Workshop) Pub Date : 2022-01-01 DOI: 10.1007/978-3-031-23443-9_24

Jared Vicory, Christian Herz, Ye Han, David Allemang, Maura Flynn, Alana Cianciulli, Hannah H Nam, Patricia Sabin, Andras Lasso, Matthew A Jolley, Beatriz Paniagua

{"title":"Skeletal model-based analysis of the tricuspid valve in hypoplastic left heart syndrome.","authors":"Jared Vicory, Christian Herz, Ye Han, David Allemang, Maura Flynn, Alana Cianciulli, Hannah H Nam, Patricia Sabin, Andras Lasso, Matthew A Jolley, Beatriz Paniagua","doi":"10.1007/978-3-031-23443-9_24","DOIUrl":"10.1007/978-3-031-23443-9_24","url":null,"abstract":"Hypoplastic left heart syndrome (HLHS) is a congenital heart disease characterized by incomplete development of the left heart. Children with HLHS undergo a series of operations which result in the tricuspid valve (TV) becoming the only functional atrioventricular valve. Many HLHS patients develop tricuspid regurgitation and right ventricle enlargement which is associated with heart failure and death without surgical intervention on the valve. Understanding the connections between the geometry of the TV and its function remains extremely challenging and hinders TV repair planning. Traditional analysis methods rely on simple anatomical measures which do not capture information about valve geometry in detail. Recently, surface-based shape representations such as SPHARM-PDM have been shown to be useful for tasks such as discriminating between valves with normal or poor function. In this work we propose to use skeletal representations (s-reps), a more feature-rich geometric representation, for modeling the leaflets of the tricuspid valve. We propose an extension to previous s-rep fitting approaches to incorporate application-specific anatomical landmarks and population information to improve correspondence. We use several traditional statistical shape analysis techniques to evaluate the efficiency of this representation: using principal component analysis (PCA) we observe that it takes fewer modes of variation compared to boundary-based approaches to represent 90% of the population variation, while distance-weighted discrimination (DWD) shows that s-reps provide for more significant classification between valves with less regurgitation and those with more. These results show the power of using s-reps for modeling the relationship between structure and function of the tricuspid valve.","PeriodicalId":74866,"journal":{"name":"Statistical atlases and computational models of the heart. STACOM (Workshop)","volume":"13593 ","pages":"258-268"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9949511/pdf/nihms-1869944.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9155638","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

Simultaneous Segmentation and Motion Estimation of Left Ventricular Myocardium in 3D Echocardiography Using Multi-task Learning. 基于多任务学习的三维超声心动图左心室心肌同时分割和运动估计。

Statistical atlases and computational models of the heart. STACOM (Workshop) Pub Date : 2022-01-01 Epub Date: 2022-01-14 DOI: 10.1007/978-3-030-93722-5_14

Kevinminh Ta, Shawn S Ahn, John C Stendahl, Jonathan Langdon, Albert J Sinusas, James S Duncan

{"title":"Simultaneous Segmentation and Motion Estimation of Left Ventricular Myocardium in 3D Echocardiography Using Multi-task Learning.","authors":"Kevinminh Ta, Shawn S Ahn, John C Stendahl, Jonathan Langdon, Albert J Sinusas, James S Duncan","doi":"10.1007/978-3-030-93722-5_14","DOIUrl":"https://doi.org/10.1007/978-3-030-93722-5_14","url":null,"abstract":"Motion estimation and segmentation are both critical steps in identifying and assessing myocardial dysfunction, but are traditionally treated as unique tasks and solved as separate steps. However, many motion estimation techniques rely on accurate segmentations. It has been demonstrated in the computer vision and medical image analysis literature that both these tasks may be mutually beneficial when solved simultaneously. In this work, we propose a multi-task learning network that can concurrently predict volumetric segmentations of the left ventricle and estimate motion between 3D echocardiographic image pairs. The model exploits complementary latent features between the two tasks using a shared feature encoder with task-specific decoding branches. Anatomically inspired constraints are incorporated to enforce realistic motion patterns. We evaluate our proposed model on an in vivo 3D echocardiographic canine dataset. Results suggest that coupling these two tasks in a learning framework performs favorably when compared against single task learning and other alternative methods.","PeriodicalId":74866,"journal":{"name":"Statistical atlases and computational models of the heart. STACOM (Workshop)","volume":" ","pages":"123-131"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9221412/pdf/nihms-1816353.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40403664","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: 1

A Persistent Homology-Based Topological Loss Function for Multi-class CNN Segmentation of Cardiac MRI. 基于持续同源的拓扑损失函数在心脏MRI多类CNN分割中的应用。

Statistical atlases and computational models of the heart. STACOM (Workshop) Pub Date : 2020-01-01 Epub Date: 2021-01-29 DOI: 10.1007/978-3-030-68107-4_1

Nick Byrne, James R Clough, Giovanni Montana, Andrew P King

{"title":"A Persistent Homology-Based Topological Loss Function for Multi-class CNN Segmentation of Cardiac MRI.","authors":"Nick Byrne, James R Clough, Giovanni Montana, Andrew P King","doi":"10.1007/978-3-030-68107-4_1","DOIUrl":"10.1007/978-3-030-68107-4_1","url":null,"abstract":"With respect to spatial overlap, CNN-based segmentation of short axis cardiovascular magnetic resonance (CMR) images has achieved a level of performance consistent with inter observer variation. However, conventional training procedures frequently depend on pixel-wise loss functions, limiting optimisation with respect to extended or global features. As a result, inferred segmentations can lack spatial coherence, including spurious connected components or holes. Such results are implausible, violating the anticipated topology of image segments, which is frequently known a priori. Addressing this challenge, published work has employed persistent homology, constructing topological loss functions for the evaluation of image segments against an explicit prior. Building a richer description of segmentation topology by considering all possible labels and label pairs, we extend these losses to the task of multi-class segmentation. These topological priors allow us to resolve all topological errors in a subset of 150 examples from the ACDC short axis CMR training data set, without sacrificing overlap performance.","PeriodicalId":74866,"journal":{"name":"Statistical atlases and computational models of the heart. STACOM (Workshop)","volume":"2020 ","pages":"3-13"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7610940/pdf/EMS124653.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39080292","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

Quality-aware semi-supervised learning for CMR segmentation. 用于 CMR 分割的质量感知半监督学习。

Statistical atlases and computational models of the heart. STACOM (Workshop) Pub Date : 2020-01-01 Epub Date: 2021-01-29 DOI: 10.1007/978-3-030-68107-4_10

Bram Ruijsink, Esther Puyol-Antón, Ye Li, Wenja Bai, Eric Kerfoot, Reza Razavi, Andrew P King

{"title":"Quality-aware semi-supervised learning for CMR segmentation.","authors":"Bram Ruijsink, Esther Puyol-Antón, Ye Li, Wenja Bai, Eric Kerfoot, Reza Razavi, Andrew P King","doi":"10.1007/978-3-030-68107-4_10","DOIUrl":"10.1007/978-3-030-68107-4_10","url":null,"abstract":"One of the challenges in developing deep learning algorithms for medical image segmentation is the scarcity of annotated training data. To overcome this limitation, data augmentation and semi-supervised learning (SSL) methods have been developed. However, these methods have limited effectiveness as they either exploit the existing data set only (data augmentation) or risk negative impact by adding poor training examples (SSL). Segmentations are rarely the final product of medical image analysis -they are typically used in downstream tasks to infer higher-order patterns to evaluate diseases. Clinicians take into account a wealth of prior knowledge on biophysics and physiology when evaluating image analysis results. We have used these clinical assessments in previous works to create robust quality-control (QC) classifiers for automated cardiac magnetic resonance (CMR) analysis. In this paper, we propose a novel scheme that uses QC of the downstream task to identify high quality outputs of CMR segmentation networks, that are subsequently utilised for further network training. In essence, this provides quality-aware augmentation of training data in a variant of SSL for segmentation networks (semiQCSeg). We evaluate our approach in two CMR segmentation tasks (aortic and short axis cardiac volume segmentation) using UK Biobank data and two commonly used network architectures (U-net and a Fully Convolutional Network) and compare against supervised and SSL strategies. We show that semiQCSeg improves training of the segmentation networks. It decreases the need for labelled data, while outperforming the other methods in terms of Dice and clinical metrics. SemiQCSeg can be an efficient approach for training segmentation networks for medical image data when labelled datasets are scarce.","PeriodicalId":74866,"journal":{"name":"Statistical atlases and computational models of the heart. STACOM (Workshop)","volume":"2020 ","pages":"97-107"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7611307/pdf/EMS124550.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39203725","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

Left Ventricle Segmentation and Quantification from Cardiac Cine MR Images via Multi-task Learning. 通过多任务学习从心脏电影MR图像中进行左心室分割和量化。

Statistical atlases and computational models of the heart. STACOM (Workshop) Pub Date : 2019-01-01 Epub Date: 2019-02-14

Shusil Dangi, Ziv Yaniv, Cristian A Linte

{"title":"Left Ventricle Segmentation and Quantification from Cardiac Cine MR Images via Multi-task Learning.","authors":"Shusil Dangi, Ziv Yaniv, Cristian A Linte","doi":"","DOIUrl":"","url":null,"abstract":"Segmentation of the left ventricle and quantification of various cardiac contractile functions is crucial for the timely diagnosis and treatment of cardiovascular diseases. Traditionally, the two tasks have been tackled independently. Here we propose a convolutional neural network based multi-task learning approach to perform both tasks simultaneously, such that, the network learns better representation of the data with improved generalization performance. Probabilistic formulation of the problem enables learning the task uncertainties during the training, which are used to automatically compute the weights for the tasks. We performed a five fold cross-validation of the myocardium segmentation obtained from the proposed multi-task network on 97 patient 4-dimensional cardiac cine-MRI datasets available through the STA-COM LV segmentation challenge against the provided gold-standard myocardium segmentation, obtaining a Dice overlap of 0.849 ± 0.036 and mean surface distance of 0.274 ± 0.083 mm, while simultaneously estimating the myocardial area with mean absolute difference error of 205 ± 198 mm2.","PeriodicalId":74866,"journal":{"name":"Statistical atlases and computational models of the heart. STACOM (Workshop)","volume":"11395 ","pages":"21-31"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6554510/pdf/nihms-1032213.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41223043","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

Left Ventricle Segmentation and Quantification from Cardiac Cine MR Images via Multi-task Learning 基于多任务学习的心脏电影MR图像左心室分割与量化

Statistical atlases and computational models of the heart. STACOM (Workshop) Pub Date : 2018-09-16 DOI: 10.1007/978-3-030-12029-0_3

Shusil Dangi, Z. Yaniv, C. Linte

引用次数: 21