  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 images, and uses image-level uncertainty for confidence-based image
weighting to suppress low-quality pseudo labels in an iterative training
process. Extensive experiments with CT scans of Rhesus Macaques with
radiation-induced PF showed that: 1) PF-Net achieved higher segmentation
accuracy than existing 2D, 3D and 2.5D neural networks, and 2) I-CRAWL
outperformed state-of-the-art semi-supervised learning methods for the PF
lesion segmentation task. Our method has a potential to improve the diagnosis
of PF and clinical assessment of side effects of radiotherapy for lung cancers.
