04187nam 22005415 450 99653446730331620230504072653.03-031-31778-510.1007/978-3-031-31778-1(MiAaPQ)EBC7246023(Au-PeEL)EBL7246023(DE-He213)978-3-031-31778-1(OCoLC)1378743247(PPN)270612122(EXLCZ)992658185570004120230504d2023 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierLeft Atrial and Scar Quantification and Segmentation[electronic resource] First Challenge, LAScarQS 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings /edited by Xiahai Zhuang, Lei Li, Sihan Wang, Fuping Wu1st ed. 2023.Cham :Springer Nature Switzerland :Imprint: Springer,2023.1 online resource (174 pages)Lecture Notes in Computer Science,1611-3349 ;13586Print version: Zhuang, Xiahai Left Atrial and Scar Quantification and Segmentation Cham : Springer,c2023 9783031317774 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.Lecture Notes in Computer Science,1611-3349 ;13586Image processing—Digital techniquesComputer visionComputer Imaging, Vision, Pattern Recognition and GraphicsImage processing—Digital techniques.Computer vision.Computer Imaging, Vision, Pattern Recognition and Graphics.006Zhuang Xiahai1355673Li Lei1255050Wang Sihan1355674Wu Fuping1355675MiAaPQMiAaPQMiAaPQBOOK996534467303316Left Atrial and Scar Quantification and Segmentation3359797UNISA