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Stacked BCDU-Net with Semantic CMR Synthesis: Application to Myocardial Pathology Segmentation Challenge

# metadata.dc.description.abstract[].value
Accurate segmentation of pathological tissue, such as scar tissue and edema, from cardiac magnetic resonance images (CMR) is fundamental to the assessment of the severity of myocardial infarction and myocardial viability. There are many accurate solutions for auto- matic segmentation of cardiac structures from CMR. On the contrary, a solution has not as yet been found for the automatic segmentation of my- ocardial pathological regions due to their challenging nature. As part of the Myocardial Pathology Segmentation combining multi-sequence CMR (MyoPS) challenge, we propose a fully automatic pipeline for segment- ing pathological tissue using registered multi-sequence CMR images se- quences (LGE, bSSFP and T2). The proposed approach involves a two- staged process. First, in order to reduce task complexity, a two-stacked BCDU-net is proposed to a) detect a small ROI based on accurate my- ocardium segmentation and b) perform inside-ROI multi-modal patho- logical region segmentation. Second, in order to regularize the proposed stacked architecture and deal with the under-represented data prob- lem, we propose a synthetic data augmentation pipeline that generates anatomically meaningful samples. The outputs of the proposed stacked BCDU-NET with semantic CMR synthesis are post-processed based on anatomical constrains to re ne output segmentation masks. Results from 25 di erent patients demonstrate that the proposed model improves 1- stage equivalent architectures and bene ts from the addition of synthetic anatomically meaningful samples. A  nal ensemble of 15 trained models show a challenge Dice test score of 0.665 0.143 and 0.698 0.128 for scar and scar+edema, respectively.

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Lecture Notes in Computer Science, 2020, vol. 12554, p. 1-16

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Articles publicats en revistes (Matemàtiques i Informàtica)

# metadata.dc.title[].value
Stacked BCDU-Net with Semantic CMR Synthesis: Application to Myocardial Pathology Segmentation Challenge