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Left Atrial and Scar Quantification and Segmentation de Xiahai Zhuang

Left Atrial and Scar Quantification and Segmentation: Lecture Notes in Computer Science, cartea 13586

Editat de Xiahai Zhuang, Lei Li, Sihan Wang, Fuping Wu
en Limba Engleză Paperback – 5 mai 2023
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.
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Specificații

ISBN-13: 9783031317774
ISBN-10: 3031317777
Pagini: 176
Ilustrații: X, 164 p. 83 illus., 72 illus. in color.
Dimensiuni: 155 x 235 x 10 mm
Greutate: 0.28 kg
Ediția:1st edition 2023
Editura: Springer
Colecția Lecture Notes in Computer Science
Seria Lecture Notes in Computer Science

Locul publicării:Cham, Switzerland

Cuprins

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 leftatrial 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.