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Semi-Supervised Intracranial Aneurysm Segmentation from CTA Images via weight-Perceptual Self-Ensembling Model

Journal of Computer Science & Technology 2023年第3期38卷 674-685页

作者：李才子刘瑞强钟焕新范峻铭司伟鑫张猛王平安 Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China University of Chinese Academy of Sciences Beijing 100049China Shantou University Shantou 515063China Shenzhen Second People′s Hospital Shenzhen 518035China Department of Computer Science and Engineering The Chinese University of Hong KongHong Kong 999077China

Segmentation of intracranial aneurysm(IA)from computed tomography angiography(CTA)images is of significant importance for quantitative assessment of IA and further surgical *** segmentation of IA is a labor-intensive,time-consuming job and suffers from inter-and intra-observer *** deep neural networks usually requires a large amount of labeled data,while annotating data is very time-consuming for the IA segmentation *** paper presents a novel weight-perceptual self-ensembling model for semi-supervised IA segmentation,which employs unlabeled data by encouraging the predictions of given perturbed input samples to be *** that the quality of consistency targets is not comparable to each other,we introduce a novel sample weight perception module to quantify the quality of different consistency *** proposed module can be used to evaluate the contributions of unlabeled samples during training to force the network to focus on those well-predicted *** have conducted both horizontal and vertical comparisons on the clinical intracranial aneurysm CTA image *** results show that our proposed method can improve at least 3%Dice coefficient over the fully-supervised baseline,and at least 1.7%over other state-of-the-art semi-supervised methods.

关键词： intracranial aneurysm(IA)segmentation sample weight perception self-ensembling model semi-supervised learning

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