Supervised segmentation with domain adaptation for small sampled orbital CT images. Issue 2 (19th April 2022)

• Record Type: Journal Article
• Authors: Suh, Sungho; Cheon, Sojeong; Choi, Wonseo; Chung, Yeon Woong; Cho, Won-Kyung; Paik, Ji-Sun; Kim, Sung Eun; Chang, Dong-Jin; Lee, Yong Oh
• Is Part Of: Journal of computational design and engineering. Volume 9:Issue 2(2022)
• Page Start: 783
• Page End: 792
• Publication Date: 2022-04-19
• Subjects: deep learning -- domain adaptation -- object segmentation -- optical nerve -- orbital tumour; Engineering -- Data processing -- Periodicals; Computer-aided design -- Periodicals; Computer-aided design; Engineering -- Data processing; Electronic journals; Electronic journals; Periodicals; 620.0042
• DOI: 10.1093/jcde/qwac029
• Languages: English
• ISSNs: 2288-4300

Abstract

Abstract: Deep neural networks have been widely used for medical image analysis. However, the lack of access to a large-scale annotated dataset poses a great challenge, especially in the case of rare diseases or new domains for the research society. Transfer of pre-trained features from the relatively large dataset is a considerable solution. In this paper, we have explored supervised segmentation using domain adaptation for optic nerve and orbital tumour, when only small sampled CT images are given. Even the lung image database consortium image collection (LIDC-IDRI) is a cross-domain to orbital CT, but the proposed domain adaptation method improved the performance of attention U-Net for the segmentation in public optic nerve dataset and our clinical orbital tumour dataset by 3.7% and 13.7% in the Dice score, respectively. The code and dataset are available at https://github.com/cmcbigdata . Graphical Abstract: