Automatically Segment the Left Atrium and Scars from LGE-MRIs Using a Boundary-focused nnU-Net

• URL: http://arxiv.org/abs/2304.14071v1
• Date: Thu, 27 Apr 2023 10:05:37 GMT
• Title: Automatically Segment the Left Atrium and Scars from LGE-MRIs Using a Boundary-focused nnU-Net
• Authors: Yuchen Zhang, Yanda Meng, Yalin Zheng
• Abstract summary: We propose to automatically segment LA cavity and quantify LA scars with late gadolinium enhancement Magnetic Resonance Imagings (LGE-MRIs) Specifically, a focus on LA boundary pixels is achieved during training, which provides a more accurate boundary prediction. Experiments on the LAScarQS 2022 dataset demonstrated our model's superior performance on the LA cavity and LA scar segmentation.
• Score: 8.27752923297381
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Atrial fibrillation (AF) is the most common cardiac arrhythmia. Accurate segmentation of the left atrial (LA) and LA scars can provide valuable information to predict treatment outcomes in AF. In this paper, we proposed to automatically segment LA cavity and quantify LA scars with late gadolinium enhancement Magnetic Resonance Imagings (LGE-MRIs). We adopted nnU-Net as the baseline model and exploited the importance of LA boundary characteristics with the TopK loss as the loss function. Specifically, a focus on LA boundary pixels is achieved during training, which provides a more accurate boundary prediction. On the other hand, a distance map transformation of the predicted LA boundary is regarded as an additional input for the LA scar prediction, which provides marginal constraint on scar locations. We further designed a novel uncertainty-aware module (UAM) to produce better results for predictions with high uncertainty. Experiments on the LAScarQS 2022 dataset demonstrated our model's superior performance on the LA cavity and LA scar segmentation. Specifically, we achieved 88.98\% and 64.08\% Dice coefficient for LA cavity and scar segmentation, respectively. We will make our implementation code public available at https://github.com/level6626/Boundary-focused-nnU-Net.

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