CardiacNET: Segmentation of left atrium and proximal pulmonary veins from MRI using multi-view CNN

TY - CHAP

T1 - CardiacNET

T2 - 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017

AU - Mortazi, Aliasghar

AU - Karim, Rashed

AU - Rhode, Kawal

AU - Burt, Jeremy

AU - Bagci, Ulas

PY - 2017/9/4

Y1 - 2017/9/4

N2 - Anatomical and biophysical modeling of left atrium (LA) and proximal pulmonary veins (PPVs) is important for clinical management of several cardiac diseases. Magnetic resonance imaging (MRI) allows qualitative assessment of LA and PPVs through visualization. However, there is a strong need for an advanced image segmentation method to be applied to cardiac MRI for quantitative analysis of LA and PPVs. In this study, we address this unmet clinical need by exploring a new deep learning-based segmentation strategy for quantification of LA and PPVs with high accuracy and heightened efficiency. Our approach is based on a multi-view convolutional neural network (CNN) with an adaptive fusion strategy and a new loss function that allows fast and more accurate convergence of the backpropagation based optimization. After training our network from scratch by using more than 60K 2D MRI images (slices), we have evaluated our segmentation strategy to the STACOM 2013 cardiac segmentation challenge benchmark. Qualitative and quantitative evaluations, obtained from the segmentation challenge, indicate that the proposed method achieved the state-of-the-art sensitivity (90%), specificity (99%), precision (94%), and efficiency levels (10s in GPU, and 7.5 min in CPU).

AB - Anatomical and biophysical modeling of left atrium (LA) and proximal pulmonary veins (PPVs) is important for clinical management of several cardiac diseases. Magnetic resonance imaging (MRI) allows qualitative assessment of LA and PPVs through visualization. However, there is a strong need for an advanced image segmentation method to be applied to cardiac MRI for quantitative analysis of LA and PPVs. In this study, we address this unmet clinical need by exploring a new deep learning-based segmentation strategy for quantification of LA and PPVs with high accuracy and heightened efficiency. Our approach is based on a multi-view convolutional neural network (CNN) with an adaptive fusion strategy and a new loss function that allows fast and more accurate convergence of the backpropagation based optimization. After training our network from scratch by using more than 60K 2D MRI images (slices), we have evaluated our segmentation strategy to the STACOM 2013 cardiac segmentation challenge benchmark. Qualitative and quantitative evaluations, obtained from the segmentation challenge, indicate that the proposed method achieved the state-of-the-art sensitivity (90%), specificity (99%), precision (94%), and efficiency levels (10s in GPU, and 7.5 min in CPU).

KW - Cardiac magnetic resonance

KW - CardiacNET

KW - Deep learning

KW - Image segmentation

KW - Left atrium

KW - MRI

KW - Pulmonary veins

UR - http://www.scopus.com/inward/record.url?scp=85029536604&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-66185-8_43

DO - 10.1007/978-3-319-66185-8_43

M3 - Other chapter contribution

AN - SCOPUS:85029536604

SN - 9783319661841

VL - 10434 LNCS

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

SP - 377

EP - 385

BT - Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings

PB - Springer Verlag

Y2 - 11 September 2017 through 13 September 2017

ER -