3D U-JAPA-Net: Mixture of Convolutional Networks for Abdominal Multi-organ CT Segmentation

This paper introduces a new type of deep learning scheme for fully-automated abdominal multi-organ CT segmentation using transfer learning. Convolutional neural network with 3D U-net is a strong tool to achieve volumetric image segmentation. The drawback of 3D U-net is that its judgement is based only on the local volumetric data, which leads to errors in categorization. To overcome this problem we propose 3D U-JAPA-net, which uses not only the raw CT data but also the probabilistic atlas of organs to reflect the information on organ locations. In the first phase of training, a 3D U-net is trained based on the conventional method. In the second phase, expert 3D U-nets for each organ are trained intensely around the locations of the organs, where the initial weights are transferred from the 3D U-net obtained in the first phase. Segmentation in the proposed method consists of three phases. First rough locations of organs are estimated by probabilistic atlas. Second, the trained expert 3D U-nets are applied in the focused locations. Post-process to remove debris is applied in the final phase. We test the performance of the proposed method with 47 CT data and it achieves higher DICE scores than the conventional 2D U-net and 3D U-net. Also, a positive effect of transfer learning is confirmed by comparing the proposed method with that without transfer learning.