Carlos Martin Isla won the MyoPS 2020 Best Paper Award at MICCAI2020.

“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.”

Our PhD student, Carlos Martín Isla won the MyoPS 2020 Best Paper Award at MICCAI2020. 

If you are curious to learn more about his work, here is a short summary and you can read the full paper here soon.

As part of the Myocardial Pathology Segmentation combining multi-sequence CMR (MyoPS) challenge, we propose a fully automatic pipeline for segmenting pathological tissue using registered multi-sequence CMR images sequences (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 myocardium segmentation and b) perform inside-ROI multi-modal pathological region segmentation. Second, in order to regularize the proposed stacked architecture and deal with the under-represented data problem, 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 rene output segmentation masks. Results from 25 different patients demonstrate that the proposed model improves 1-stage equivalent architectures and benefits from the addition of synthetic anatomically meaningful samples. A nal ensemble of 15 trained models show a challenge Dice test score of 0.6650.143 and 0.6980.128 for scar and scar+edema, respectively.

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