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SAUN: Stack attention U‐Net for left ventricle segmentation from cardiac cine magnetic resonance imaging
Author(s) -
Sun Xiaowu,
Garg Pankaj,
Plein Sven,
Geest Rob J.
Publication year - 2021
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.14752
Subject(s) - segmentation , artificial intelligence , computer science , hausdorff distance , dice , convolutional neural network , pattern recognition (psychology) , ejection fraction , image segmentation , computer vision , stack (abstract data type) , deep learning , mathematics , medicine , heart failure , geometry , programming language
Purpose Quantification of left ventricular (LV) volume, ejection fraction and myocardial mass from multi‐slice multi‐phase cine MRI requires accurate segmentation of the LV in many images. We propose a stack attention‐based convolutional neural network (CNN) approach for fully automatic segmentation from short‐axis cine MR images. Methods To extract the relevant spatiotemporal image features, we introduce two kinds of stack methods, spatial stack model and temporal stack model, combining the target image with its neighboring images as the input of a CNN. A stack attention mechanism is proposed to weigh neighboring image slices in order to extract the relevant features using the target image as a guide. Based on stack attention and standard U‐Net, a novel Stack Attention U‐Net (SAUN) is proposed and trained to perform the semantic segmentation task. A loss function combining cross‐entropy and Dice is used to train SAUN. The performance of the proposed method was evaluated on an internal and a public dataset using technical metrics including Dice, Hausdorff distance (HD), and mean contour distance (MCD), as well as clinical parameters, including left ventricular ejection fraction (LVEF) and myocardial mass (LVM). In addition, the results of SAUN were compared to previously presented CNN methods, including U‐Net and SegNet. Results The spatial stack attention model resulted in better segmentation results than the temporal stack model. On the internal dataset comprising of 167 post‐myocardial infarction patients and 57 healthy volunteers, our method achieved a mean Dice of 0.91, HD of 3.37 mm, and MCD of 1.08 mm. Evaluation on the publicly available ACDC dataset demonstrated good generalization performance, yielding a Dice of 0.92, HD of 9.4 mm, and MCD of 0.74 mm on end‐diastolic images, and a Dice of 0.89, HD of 7.1 mm and MCD of 1.03 mm on end‐systolic images. The Pearson correlation coefficient of LVEF and LVM between automatically and manually derived results were higher than 0.98 in both datasets. Conclusion We developed a CNN with a stack attention mechanism to automatically segment the LV chamber and myocardium from the multi‐slice short‐axis cine MRI. The experimental results demonstrate that the proposed approach exceeds existing state‐of‐the‐art segmentation methods and verify its potential clinical applicability.