LAScarQS@MICCAI最新文献

Automatically Segment the Left Atrium and Scars from LGE-MRIs Using a Boundary-focused nnU-Net 利用边界聚焦nnU-Net自动分割大磁共振左心房和疤痕

LAScarQS@MICCAI Pub Date : 2023-04-27 DOI: 10.48550/arXiv.2304.14071

Yuchen Zhang, Y. Meng, Yalin Zheng

{"title":"Automatically Segment the Left Atrium and Scars from LGE-MRIs Using a Boundary-focused nnU-Net","authors":"Yuchen Zhang, Y. Meng, Yalin Zheng","doi":"10.48550/arXiv.2304.14071","DOIUrl":"https://doi.org/10.48550/arXiv.2304.14071","url":null,"abstract":"Atrial fibrillation (AF) is the most common cardiac arrhythmia. Accurate segmentation of the left atrial (LA) and LA scars can provide valuable information to predict treatment outcomes in AF. In this paper, we proposed to automatically segment LA cavity and quantify LA scars with late gadolinium enhancement Magnetic Resonance Imagings (LGE-MRIs). We adopted nnU-Net as the baseline model and exploited the importance of LA boundary characteristics with the TopK loss as the loss function. Specifically, a focus on LA boundary pixels is achieved during training, which provides a more accurate boundary prediction. On the other hand, a distance map transformation of the predicted LA boundary is regarded as an additional input for the LA scar prediction, which provides marginal constraint on scar locations. We further designed a novel uncertainty-aware module (UAM) to produce better results for predictions with high uncertainty. Experiments on the LAScarQS 2022 dataset demonstrated our model's superior performance on the LA cavity and LA scar segmentation. Specifically, we achieved 88.98% and 64.08% Dice coefficient for LA cavity and scar segmentation, respectively. We will make our implementation code public available at https://github.com/level6626/Boundary-focused-nnU-Net.","PeriodicalId":138957,"journal":{"name":"LAScarQS@MICCAI","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115094108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

UGformer for Robust Left Atrium and Scar Segmentation Across Scanners 鲁棒左心房和跨扫描仪疤痕分割的UGformer

LAScarQS@MICCAI Pub Date : 2022-10-11 DOI: 10.48550/arXiv.2210.05151

Tianyi Liu, Size Hou, Jiayu Zhu, Zilong Zhao, Haochuan Jiang

{"title":"UGformer for Robust Left Atrium and Scar Segmentation Across Scanners","authors":"Tianyi Liu, Size Hou, Jiayu Zhu, Zilong Zhao, Haochuan Jiang","doi":"10.48550/arXiv.2210.05151","DOIUrl":"https://doi.org/10.48550/arXiv.2210.05151","url":null,"abstract":"Thanks to the capacity for long-range dependencies and robustness to irregular shapes, vision transformers and deformable convolutions are emerging as powerful vision techniques of segmentation.Meanwhile, Graph Convolution Networks (GCN) optimize local features based on global topological relationship modeling. Particularly, they have been proved to be effective in addressing issues in medical imaging segmentation tasks including multi-domain generalization for low-quality images. In this paper, we present a novel, effective, and robust framework for medical image segmentation, namely, UGformer. It unifies novel transformer blocks, GCN bridges, and convolution decoders originating from U-Net to predict left atriums (LAs) and LA scars. We have identified two appealing findings of the proposed UGformer: 1). an enhanced transformer module with deformable convolutions to improve the blending of the transformer information with convolutional information and help predict irregular LAs and scar shapes. 2). Using a bridge incorporating GCN to further overcome the difficulty of capturing condition inconsistency across different Magnetic Resonance Images scanners with various inconsistent domain information. The proposed UGformer model exhibits outstanding ability to segment the left atrium and scar on the LAScarQS 2022 dataset, outperforming several recent state-of-the-arts.","PeriodicalId":138957,"journal":{"name":"LAScarQS@MICCAI","volume":"272 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133852040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Multi-Depth Boundary-Aware Left Atrial Scar Segmentation Network 多深度边界感知左心房疤痕分割网络

LAScarQS@MICCAI Pub Date : 2022-08-08 DOI: 10.48550/arXiv.2208.04940

Meng-Yun Wu, Wangbin Ding, Mingjing Yang, Liqin Huang

引用次数: 2

Automatic Semi-supervised Left Atrial Segmentation Using Deep-Supervision 3DResUnet with Pseudo Labeling Approach for LAScarQS 2022 Challenge LAScarQS 2022挑战赛中基于深度监督3DResUnet和伪标记方法的自动半监督左心房分割

LAScarQS@MICCAI Pub Date : 1900-01-01 DOI: 10.1007/978-3-031-31778-1_15

Moona Mazher, Abdul Qayyum, M. Abdel-Nasser, D. Puig

引用次数: 1

TESSLA: Two-Stage Ensemble Scar Segmentation for the Left Atrium TESSLA:左心房两阶段整体疤痕分割

LAScarQS@MICCAI Pub Date : 1900-01-01 DOI: 10.1007/978-3-031-31778-1_10

S. Ogbomo-Harmitt, Jakub Grzelak, A. Qureshi, A. King, O. Aslanidi