Supervised Segmentation with Domain Adaptation for Small Sampled Orbital CT Images
This addresses the challenge of limited annotated data for rare diseases or new domains in medical imaging, though it is incremental as it adapts existing methods to a specific application.
The paper tackles the problem of medical image segmentation when only small annotated datasets are available, by applying domain adaptation from lung CT images to orbital CT images, achieving improved segmentation performance on optic nerve and orbital tumor datasets.
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.