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Left atrial and scar quantification and segmentationfirst challenge, LAScarQS 2022, held in conjunction with MICCAI 2022, Singapore, September 18, 2022 : proceedings /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Left atrial and scar quantification and segmentationedited by Xiahai Zhuang ... [et al.].
Reminder of title:	first challenge, LAScarQS 2022, held in conjunction with MICCAI 2022, Singapore, September 18, 2022 : proceedings /
remainder title:	LAScarQS 2022
other author:	Zhuang, Xiahai.
corporate name:
Published:	Cham :Springer Nature Switzerland :2023.
Description:	x, 164 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Heart atriumCongresses.Imaging
Online resource:	https://doi.org/10.1007/978-3-031-31778-1
ISBN:	9783031317781$q(electronic bk.)

Left atrial and scar quantification and segmentationfirst challenge, LAScarQS 2022, held in conjunction with MICCAI 2022, Singapore, September 18, 2022 : proceedings /
Left atrial and scar quantification and segmentationfirst challenge, LAScarQS 2022, held in conjunction with MICCAI 2022, Singapore, September 18, 2022 : proceedings /[electronic resource] :LAScarQS 2022edited by Xiahai Zhuang ... [et al.]. - Cham :Springer Nature Switzerland :2023. - x, 164 p. :ill., digital ;24 cm. - Lecture notes in computer science,135860302-9743 ;. - Lecture notes in computer science ;4891..

LASSNet: A four steps deep neural network for Left Atrial Segmentation and Scar Quantification -- Multi-Depth Boundary-Aware Left Atrial Scar Segmentation Network -- Self Pre-training with Single-scale Adapter for Left Atrial Segmentation -- UGformer for Robust Left Atrium and Scar Segmentation Across Scanners -- Automatically Segmenting the Left Atrium and Scars from LGE-MRIs Using a boundary-focused nnU-Net -- Two Stage of Histogram Matching Augmentation for Domain Generalization : Application to Left Atrial Segmentation -- Sequential Segmentation of the Left Atrium and Atrial Scars Using a Multi-scale Weight Sharing Network and Boundary-based Processing -- LA-HRNet: High-resolution network for automatic left atrial segmentation in multi-center LEG MRI -- Edge-enhanced Features Guided Joint Segmentation and Quantification of Left Atrium and Scars in LGE MRI Images -- TESSLA: Two-Stage Ensemble Scar Segmentation for the Left Atrium -- Deep U-Net architecture with curriculum learning for left atrial segmentation -- Cross-domain Segmentation of Left Atrium Based on Multi-scale Decision Level Fusion -- Using Polynomial Loss and Uncertainty Information for Robust Left Atrial and Scar Quantification and Segmentation -- Automated segmentation of the left atrium and scar using deep convolutional neural networks -- Automatic Semi-Supervised Left Atrial Segmentation using Deep-Supervision 3DResUnet with Pseudo Labeling Approach for LAScarQS 2022 Challenge.

This book constitutes the First Left Atrial and Scar Quantification and Segmentation Challenge, LAScarQS 2022, which was held in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, in Singapore, in September 2022. The 15 papers presented in this volume were carefully reviewed and selected form numerous submissions. The aim of the challenge is not only benchmarking various LA scar segmentation algorithms, but also covering the topic of general cardiac image segmentation, quantification, joint optimization, and model generalization, and raising discussions for further technical development and clinical deployment.

ISBN: 9783031317781$q(electronic bk.)

Standard No.: 10.1007/978-3-031-31778-1doiSubjects--Topical Terms:

949988
Heart atrium
--Imaging--Congresses.

LC Class. No.: RC683.5.I42

Dewey Class. No.: 616.120754

Left atrial and scar quantification and segmentationfirst challenge, LAScarQS 2022, held in conjunction with MICCAI 2022, Singapore, September 18, 2022 : proceedings /
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505 0 $a LASSNet: A four steps deep neural network for Left Atrial Segmentation and Scar Quantification -- Multi-Depth Boundary-Aware Left Atrial Scar Segmentation Network -- Self Pre-training with Single-scale Adapter for Left Atrial Segmentation -- UGformer for Robust Left Atrium and Scar Segmentation Across Scanners -- Automatically Segmenting the Left Atrium and Scars from LGE-MRIs Using a boundary-focused nnU-Net -- Two Stage of Histogram Matching Augmentation for Domain Generalization : Application to Left Atrial Segmentation -- Sequential Segmentation of the Left Atrium and Atrial Scars Using a Multi-scale Weight Sharing Network and Boundary-based Processing -- LA-HRNet: High-resolution network for automatic left atrial segmentation in multi-center LEG MRI -- Edge-enhanced Features Guided Joint Segmentation and Quantification of Left Atrium and Scars in LGE MRI Images -- TESSLA: Two-Stage Ensemble Scar Segmentation for the Left Atrium -- Deep U-Net architecture with curriculum learning for left atrial segmentation -- Cross-domain Segmentation of Left Atrium Based on Multi-scale Decision Level Fusion -- Using Polynomial Loss and Uncertainty Information for Robust Left Atrial and Scar Quantification and Segmentation -- Automated segmentation of the left atrium and scar using deep convolutional neural networks -- Automatic Semi-Supervised Left Atrial Segmentation using Deep-Supervision 3DResUnet with Pseudo Labeling Approach for LAScarQS 2022 Challenge.
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