Learning multi-level structural information for small organ segmentation

Deep neural networks have achieved great success in medical image segmentation problems such as liver, kidney, the accuracy of which already exceeds the human level. However, small organ segmentation (e.g., pancreas) is still a challenging task. To tackle such problems, extracting and aggregating multi-scale robust features become essentially important. In this paper, we develop a multi-level structural loss by integrating the region, boundary, and pixel-wise information to supervise feature fusion and precise segmentation. The novel pixel-wise term can provide information complementary to the region and boundary loss, which helps to discover more local information from the image. We further develop a multi-branch network with a saliency guidance module to better aggregate the three levels of features. The coarse-to-fine segmentation architecture is adopted to use the prediction on the coarse stage to obtain the bounding box for the fine stage. Comprehensive evaluations are performed on three benchmark datasets, i.e., the NIH pancreas, ISICDM pancreas, and MSD spleen dataset, showing that our models can achieve significant increases in segmentation accuracy compared to several state-of-the-art pancreas and spleen segmentation methods. Furthermore, the ablation study demonstrates the multi-level structural features help both the training stability and the convergence of the coarse-to-fine approach.