Abstract
Left atrial (LA) and atrial scar segmentation from late gadolinium-enhanced magnetic resonance imaging (LGE MRI) is an important task in clinical practice. Late gadolinium enhancement magnetic resonance imaging (LGE MRI) is commonly used to visualize and quantify left atrial (LA) scars. The position and extent of LA scars provide important information on the pathophysiology and progression of atrial fibrillation (AF). LAScarQS 2022: Left Atrial and Scar Quantification & Segmentation Challenge provided the dataset to evaluate the segmentation model to segment the LA and scars. In this paper, we have developed a semi-supervised segmentation approach using the pseudo labeling approach. We have trained two different models for LA segmentation. In the first model, we have trained 3DResUnet with deep supervision techniques to get the pseudo label using training and validation datasets and in the second model, we have trained the nnUNet model that uses the pseudo segmentation labels of the first model with true labels for LA segmentation. The proposed solution provides optimal performance for the LA segmentation task and achieved a 0.88 Dice score on the validation dataset. The source code will be publicly available at https://github.com/RespectKnowledge/Semi-supervised_Segmentation_LAS-carQS-2022-Challenge.
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Acknowledgment
The authors of this paper declare that the segmentation method they implemented for participation in the LAScarQS 2022 challenge has not used any pre-trained models or additional datasets other than those provided by the organizers. We thank the LAScarQS 2022 challenge organizer teams who provided the dataset and platform to validate our proposed solution.
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Mazher, M., Qayyum, A., Abdel-Nasser, M., Puig, D. (2023). Automatic Semi-supervised Left Atrial Segmentation Using Deep-Supervision 3DResUnet with Pseudo Labeling Approach for LAScarQS 2022 Challenge. In: Zhuang, X., Li, L., Wang, S., Wu, F. (eds) Left Atrial and Scar Quantification and Segmentation. LAScarQS 2022. Lecture Notes in Computer Science, vol 13586. Springer, Cham. https://doi.org/10.1007/978-3-031-31778-1_15
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