Statistical atlases and computational models of the heart. STACOM (Workshop)最新文献_第2页

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

Semi-automated Image Segmentation of the Midsystolic Left Ventricular Mitral Valve Complex in Ischemic Mitral Regurgitation 缺血性二尖瓣反流中左心室收缩中期二尖瓣复合体的半自动图像分割

Statistical atlases and computational models of the heart. STACOM (Workshop) Pub Date : 2018-09-16 DOI: 10.1007/978-3-030-12029-0_16

A. H. Aly, A. H. Aly, Mahmoud Elrakhawy, Kirlos Haroun, Luis Prieto-Riascos, R. Gorman, N. Yushkevich, Yoshiaki Saito, J. Gorman, R. Gorman, Paul Yushkevich, A. Pouch

引用次数: 1

Left Ventricular Diastolic and Systolic Material Property Estimation from Image Data: LV Mechanics Challenge. 从图像数据估计左心室舒张和收缩材料特性:左室力学挑战。

Statistical atlases and computational models of the heart. STACOM (Workshop) Pub Date : 2015-01-01 DOI: 10.1007/978-3-319-14678-2_7

Adarsh Krishnamurthy, Christopher Villongco, Amanda Beck, Jeffrey Omens, Andrew McCulloch

{"title":"Left Ventricular Diastolic and Systolic Material Property Estimation from Image Data: LV Mechanics Challenge.","authors":"Adarsh Krishnamurthy, Christopher Villongco, Amanda Beck, Jeffrey Omens, Andrew McCulloch","doi":"10.1007/978-3-319-14678-2_7","DOIUrl":"https://doi.org/10.1007/978-3-319-14678-2_7","url":null,"abstract":"Cardiovascular simulations using patient-specific geometries can help researchers understand the mechanical behavior of the heart under different loading or disease conditions. However, to replicate the regional mechanics of the heart accurately, both the nonlinear passive and active material properties must be estimated reliably. In this paper, automated methods were used to determine passive material properties while simultaneously computing the unloaded reference geometry of the ventricles for stress analysis. Two different approaches were used to model systole. In the first, a physiologically-based active contraction model [1] coupled to a hemodynamic three-element Windkessel model of the circulation was used to simulate ventricular ejection. In the second, developed active tension was directly adjusted to match ventricular volumes at end-systole while prescribing the known end-systolic pressure. These methods were tested in four normal dogs using the data provided for the LV mechanics challenge [2]. The resulting end-diastolic and end-systolic geometry from the simulation were compared with measured image data.","PeriodicalId":74866,"journal":{"name":"Statistical atlases and computational models of the heart. STACOM (Workshop)","volume":"8896 ","pages":"63-73"},"PeriodicalIF":0.0,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/978-3-319-14678-2_7","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33094826","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: 7

Segmentation of the Aortic Valve Apparatus in 3D Echocardiographic Images: Deformable Modeling of a Branching Medial Structure. 三维超声心动图图像中主动脉瓣装置的分割:分支内侧结构的可变形建模。

Statistical atlases and computational models of the heart. STACOM (Workshop) Pub Date : 2015-01-01 DOI: 10.1007/978-3-319-14678-2_20

Alison M Pouch, Sijie Tian, Manabu Takabe, Hongzhi Wang, Jiefu Yuan, Albert T Cheung, Benjamin M Jackson, Joseph H Gorman, Robert C Gorman, Paul A Yushkevich

{"title":"Segmentation of the Aortic Valve Apparatus in 3D Echocardiographic Images: Deformable Modeling of a Branching Medial Structure.","authors":"Alison M Pouch, Sijie Tian, Manabu Takabe, Hongzhi Wang, Jiefu Yuan, Albert T Cheung, Benjamin M Jackson, Joseph H Gorman, Robert C Gorman, Paul A Yushkevich","doi":"10.1007/978-3-319-14678-2_20","DOIUrl":"https://doi.org/10.1007/978-3-319-14678-2_20","url":null,"abstract":"3D echocardiographic (3DE) imaging is a useful tool for assessing the complex geometry of the aortic valve apparatus. Segmentation of this structure in 3DE images is a challenging task that benefits from shape-guided deformable modeling methods, which enable inter-subject statistical shape comparison. Prior work demonstrates the efficacy of using continuous medial representation (cm-rep) as a shape descriptor for valve leaflets. However, its application to the entire aortic valve apparatus is limited since the structure has a branching medial geometry that cannot be explicitly parameterized in the original cm-rep framework. In this work, we show that the aortic valve apparatus can be accurately segmented using a new branching medial modeling paradigm. The segmentation method achieves a mean boundary displacement of 0.6 ± 0.1 mm (approximately one voxel) relative to manual segmentation on 11 3DE images of normal open aortic valves. This study demonstrates a promising approach for quantitative 3DE analysis of aortic valve morphology.","PeriodicalId":74866,"journal":{"name":"Statistical atlases and computational models of the heart. STACOM (Workshop)","volume":" ","pages":"196-203"},"PeriodicalIF":0.0,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/978-3-319-14678-2_20","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39977948","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: 15

Robust Atlas-Based Segmentation of Highly Variable Anatomy: Left Atrium Segmentation. 高度可变解剖结构的稳健阿特拉斯分割:左心房分割。

Statistical atlases and computational models of the heart. STACOM (Workshop) Pub Date : 2010-01-01 DOI: 10.1007/978-3-642-15835-3_9

Michal Depa, Mert R Sabuncu, Godtfred Holmvang, Reza Nezafat, Ehud J Schmidt, Polina Golland

引用次数: 50