Supervised Segmentation with Domain Adaptation for Small Sampled Orbital CT Images

• URL: http://arxiv.org/abs/2107.00418v1
• Date: Thu, 1 Jul 2021 13:00:33 GMT
• Title: Supervised Segmentation with Domain Adaptation for Small Sampled Orbital CT Images
• Authors: Sungho Suh, Sojeong Cheon, Wonseo Choi, Yeon Woong Chung, Won-Kyung Cho, Ji-Sun Paik, Sung Eun Kim, Dong-Jin Chang, Yong Oh Lee
• Abstract summary: Deep neural networks (DNNs) have been widely used for medical image analysis. The lack of access to a to large-scale annotated dataset poses a great challenge. In this paper, we have explored supervised segmentation using domain adaptation for optic nerve and orbital tumor.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep neural networks (DNNs) have been widely used for medical image analysis. However, the lack of access a to 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 tumor, 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 tumor dataset. The code and dataset are available at https://github.com/cmcbigdata.

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