[2109.14172] Semi-Supervised Segmentation of Radiation-Induced Pulmonary Fibrosis from Lung CT Scans with Multi-Scale Guided Dense Attention

Computed Tomography (CT) plays an important role in monitoring radiation-induced Pulmonary Fibrosis (PF), where accurate segmentation of the PF lesions is highly desired for diagnosis and treatment follow-up. However, the task is challenged by ambiguous boundary, irregular shape, various position and size of the lesions, as well as the difficulty in acquiring a large set of annotated volumetric images for training. To overcome these problems, we propose a novel convolutional neural network called PF-Net and incorporate it into a semi-supervised learning framework based on Iterative Confidence-based Refinement And Weighting of pseudo Labels (I-CRAWL). Our PF-Net combines 2D and 3D convolutions to deal with CT volumes with large inter-slice spacing, and uses multi-scale guided dense attention to segment complex PF lesions. For semi-supervised learning, our I-CRAWL employs pixel-level uncertainty-based confidence-aware refinement to improve the accuracy of pseudo labels of unannotated ima

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@Maxwell_110
@Maxwell_110 肺の放射線治療による誘発障害部の CT segmentation 📝 t.co/1NItc8SeJF CT は軸毎に分解能が異なるため,2D Conv で低分解能に統一後,3D Conv を適用する 2.5D CNN を採用 半教師学習を採用しており,psuedo label と data weight を予測の不確実性(Dropout で推計)に基づき逐次更新 t.co/RLxsl6T441

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