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

TY - CHAP

T1 - SMOD - Data Augmentation Based on Statistical Models of Deformation to Enhance Segmentation in 2D Cine Cardiac MRI

AU - Corral Acero, Jorge

AU - Zacur, Ernesto

AU - Xu, Hao

AU - Ariga, Rina

AU - Bueno-Orovio, Alfonso

AU - Lamata, Pablo

AU - Grau, Vicente

PY - 2019/5/30

Y1 - 2019/5/30

N2 - 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.

AB - 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.

KW - Cardiac MRI

KW - Data augmentation

KW - Deep learning

KW - Models of deformation

KW - Segmentation

UR - http://www.scopus.com/inward/record.url?scp=85067176547&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-21949-9_39

DO - 10.1007/978-3-030-21949-9_39

M3 - Conference paper

AN - SCOPUS:85067176547

SN - 9783030219482

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 361

EP - 369

BT - Functional Imaging and Modeling of the Heart - 10th International Conference, FIMH 2019, Proceedings

A2 - Coudière, Yves

A2 - Zemzemi, Nejib

A2 - Ozenne, Valéry

A2 - Vigmond, Edward

PB - Springer Verlag

T2 - 10th International Conference on Functional Imaging and Modeling of the Heart, FIMH 2019

Y2 - 6 June 2019 through 8 June 2019

ER -