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SMOD - Data Augmentation Based on Statistical Models of Deformation to Enhance Segmentation in 2D Cine Cardiac MRI

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Jorge Corral Acero, Ernesto Zacur, Hao Xu, Rina Ariga, Alfonso Bueno-Orovio, Pablo Lamata, Vicente Grau

Original languageEnglish
Title of host publicationFunctional Imaging and Modeling of the Heart - 10th International Conference, FIMH 2019, Proceedings
EditorsYves Coudière, Nejib Zemzemi, Valéry Ozenne, Edward Vigmond
PublisherSpringer Verlag
Pages361-369
Number of pages9
ISBN (Print)9783030219482
DOIs
Accepted/In press2 Apr 2019
E-pub ahead of print30 May 2019
Event10th International Conference on Functional Imaging and Modeling of the Heart, FIMH 2019 - Bordeaux, France
Duration: 6 Jun 20198 Jun 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11504 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th International Conference on Functional Imaging and Modeling of the Heart, FIMH 2019
Country/TerritoryFrance
CityBordeaux
Period6/06/20198/06/2019

Documents

King's Authors

Abstract

Deep learning has revolutionized medical image analysis in recent years. Nevertheless, technical, ethical and financial constraints along with confidentiality issues still limit data availability, and therefore the performance of these approaches. To overcome such limitations, data augmentation has proven crucial. Here we propose SMOD, a novel augmentation methodology based on Statistical Models of Deformations, to segment 2D cine scans in cardiac MRI. In brief, the shape variability of the training set space is modelled so new images with the appearance of the original ones but unseen shapes within the space of plausible realistic shapes are generated. SMOD is compared to standard augmentation providing quantitative improvement, especially when the training data available is very limited or the structures to segment are complex and highly variable. We finally propose a state-of-art, deep learning 2D cardiac MRI segmenter for normal and hypertrophic cardiomyopathy hearts with an epicardium and endocardium mean Dice score of 0.968 in short and long axis.

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