Convexity and Connectivity Principles Applied for Left Ventricle Segmentation and Quantification

We propose an unsupervised method for MRI image segmentation, and global and regional shape quantification, based on pixel labeling using image analysis, connectivity constraints and near convex region requirements for the LV cavity and the epicardium. The proposed method is developed in the framework of the MICCAI Left Ventricle Full Quantification Challenge. At first the LV cavity is approximately localized based on the strong intensity contrast in the myocardium region between the two ventricles (left and right). The requirement of a near convex connected component is then applied. The image intensity statistical parameters are extracted for three classes: LV cavity, myocardium and chest space. Even if the whole background is completely inhomogeneous, the application of topological, connectivity and shape constraints permits to extract in two steps the LV cavity and the myocardium. For the later two approaches are proposed: regularization using B-spline smoothing and adaptive region growing with boundary smoothing using Fourier coefficients. On the segmented images are measured the significant clinical global and regional shape LV indices. We consider that we have obtained good results on indices related to the endocardium for both Training and Test datasets. There is place for improvements concerning the myocardium global and regional shape indices.

[1]  Wufeng Xue,et al.  Full left ventricle quantification via deep multitask relationships learning , 2018, Medical Image Anal..

[2]  Caroline Petitjean,et al.  A review of segmentation methods in short axis cardiac MR images , 2011, Medical Image Anal..

[3]  Zhiqiang Hu,et al.  Left Ventricle Full Quantification Using Deep Layer Aggregation Based Multitask Relationship Learning , 2018, STACOM@MICCAI.

[4]  Ling Shao,et al.  A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging , 2016, Magnetic Resonance Materials in Physics, Biology and Medicine.

[5]  Terry M. Peters,et al.  Regional Assessment of Cardiac Left Ventricular Myocardial Function via MRI Statistical Features , 2014, IEEE Transactions on Medical Imaging.

[6]  Xin Yang,et al.  Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved? , 2018, IEEE Transactions on Medical Imaging.

[7]  Georgios Tziritas,et al.  Fast Fully-Automatic Cardiac Segmentation in MRI Using MRF Model Optimization, Substructures Tracking and B-Spline Smoothing , 2017, STACOM@MICCAI.

[8]  Xiantong Zhen,et al.  Multi-scale deep networks and regression forests for direct bi-ventricular volume estimation , 2016, Medical Image Anal..

[9]  B. Barsky,et al.  An Introduction to Splines for Use in Computer Graphics and Geometric Modeling , 1987 .

[10]  Xiahai Zhuang,et al.  Challenges and methodologies of fully automatic whole heart segmentation: a review. , 2013, Journal of healthcare engineering.