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Deep learning for head and neck semi-supervised semantic segmentation
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DOI：10.1088/1361-6560/ad25c2.png)
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摘要
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Objective. Radiation therapy (RT) represents a prevalent therapeutic modality for head and neck (H&N) cancer. A crucial phase in RT planning involves the precise delineation of organs-at-risks (OARs), employing computed tomography (CT) scans. Nevertheless, the manual delineation of OARs is a labor-intensive process, necessitating individual scrutiny of each CT image slice, not to mention that a standard CT scan comprises hundreds of such slices. Furthermore, there is a significant domain shift between different institutions' H&N data, which makes traditional semi-supervised learning strategies susceptible to confirmation bias. Therefore, effectively using unlabeled datasets to support annotated datasets for model training has become a critical issue for preventing domain shift and confirmation bias. Approach. In this work, we proposed an innovative cross-domain orthogon-based-perspective consistency (CD-OPC) strategy within a two-branch collaborative training framework, which compels the two sub-networks to acquire valuable features from unrelated perspectives. More specifically, a novel generative pretext task cross-domain prediction (CDP) was designed for learning inherent properties of CT images. Then this prior knowledge was utilized to promote the independent learning of distinct features by the two sub-networks from identical inputs, thereby enhancing the perceptual capabilities of the sub-networks through orthogon-based pseudo-labeling knowledge transfer. Main results. Our CD-OPC model was trained on H&N datasets from nine different institutions, and validated on the four local intuitions' H&N datasets. Among all datasets CD-OPC achieved more advanced performance than other semi-supervised semantic segmentation algorithms. Significance. The CD-OPC method successfully mitigates domain shift and prevents network collapse. In addition, it enhances the network's perceptual abilities, and generates more reliable predictions, thereby further addressing the confirmation bias issue.
Keyword:
radiation therapy
semi-supervised semantic segmentation
domain shift
confirmation bias
deep learning
期刊
IF:
3.4
论文数:
1.4W
被引数:
3.1W
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