$S^{3}$ Net: Trained on a Small Sample Segmentation Network for Biomedical Image Analysis
Mengdi Yan, Hansheng Li, Baosheng Kang, Jun Hong Feng, Yuxin Kang, Tao Zhang, Lin Yang, Lei Cui · 2019
Fully convolutional networks (FCNs) are powerful methods to extract hierarchies of features that have achieved remarkable success in various biomedical image analysis tasks. However, the successful training of FCN requires more than hundreds of pixel-level annotated training samples, which poses a challenge for biomedical image processing tasks. In this paper, we present S3Net, a network that makes more efficient use of available annotated samples on biomedical image segmentation. S3Net is essentially a deeply-supervised encoder-decoder network where the decoder has been redesigned to efficiently restore multi-level encoded feature resolution in a single step. We have conducted extensive experiments on the 2015 MICCAI Gland Challenge dataset. Compared with other methods, S3Net achieves a 0.781 dice-score using 11 images for training, higher than U-Net++ 18.6% and U-Net 12.9%, which verifies the performance of S3Net trained on a small sample. Further, with training on 85 images, S3Net achieves a 0.910 dice-score by using resnet50 as the encoder, which is higher than state-of-the-art semantic segmentation results by 4%.