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245 1 0 _aLeft Atrial and Scar Quantification and Segmentation
_h[electronic resource] :
_bFirst Challenge, LAScarQS 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings /
_cedited by Xiahai Zhuang, Lei Li, Sihan Wang, Fuping Wu.
250 _a1st ed. 2023.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2023.
300 _aX, 164 p. 83 illus., 72 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
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338 _aonline resource
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347 _atext file
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490 1 _aLecture Notes in Computer Science,
_x1611-3349 ;
_v13586
505 0 _aLASSNet: 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.
520 _aThis 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.
650 0 _aImage processing
_xDigital techniques.
650 0 _aComputer vision.
650 1 4 _aComputer Imaging, Vision, Pattern Recognition and Graphics.
700 1 _aZhuang, Xiahai.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aLi, Lei.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aWang, Sihan.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aWu, Fuping.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031317774
776 0 8 _iPrinted edition:
_z9783031317798
830 0 _aLecture Notes in Computer Science,
_x1611-3349 ;
_v13586
856 4 0 _uhttps://doi.org/10.1007/978-3-031-31778-1
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