Left Ventricle Quantification with Cardiac MRI: Deep Learning Meets Statistical Models of Deformation

Jorge Corral Acero*, Hao Xu, Ernesto Zacur, Jurgen E. Schneider, Pablo Lamata, Alfonso Bueno-Orovio, Vicente Grau

*Corresponding author for this work

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

5 Citations (Scopus)

Abstract

Deep learning has been widely applied for left ventricle (LV) analysis, obtaining state of the art results in quantification through image segmentation. When the training datasets are limited, data augmentation becomes critical, but standard augmentation methods do not usually incorporate the natural variation of anatomy. In this paper we propose a pipeline for LV quantification applying our data augmentation methodology based on statistical models of deformations (SMOD) to quantify LV based on segmentation of cardiac MR (CMR) images, and present an in-depth analysis of the effects of deformation parameters in SMOD performance. We trained and evaluated our pipeline on the MICCAI 2019 Left Ventricle Full Quantification Challenge dataset, and achieved average mean absolute error (MAE) for areas, dimensions, regional wall thickness and phase of 106 mm2, 1.52 mm, 1.01 mm and 8.0% respectively in a 3-fold cross-validation experiment.

Original languageEnglish
Title of host publicationStatistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges - 10th International Workshop, STACOM 2019, Held in Conjunction with MICCAI 2019, Revised Selected Papers
EditorsMihaela Pop, Maxime Sermesant, Oscar Camara, Xiahai Zhuang, Shuo Li, Alistair Young, Tommaso Mansi, Avan Suinesiaputra
PublisherSPRINGER
Pages384-394
Number of pages11
ISBN (Print)9783030390730
DOIs
Publication statusPublished - 1 Jan 2020
Event10th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: 13 Oct 201913 Oct 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12009 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019
Country/TerritoryChina
CityShenzhen
Period13/10/201913/10/2019

Keywords

  • Data augmentation
  • Deep learning
  • LV quantification

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