Organ-Specific Segmentation Versus Multi-Class Segmentation Using U-Net

Abstract

In the clinical practice of radiation treatment planning, multiple organs need to be segmented from the CT to calculate the dose distribution on each organ. Deep convolutional neural networks have shown great success in many medical image segmentation applications and U-net have been widely used in this application and demonstrated far superior performance than all traditional methods. To achieve the multi-organ segmentation task, there can be two design choices: a single network can be designed and trained with direct multi-class segmentation output or multiple organ specific networks can be trained with each one performing a binary class segmentation. This study aims to perform a comparison of these two options using the data from the 2017 AAPM Thoracic Auto-segmentation Challenge and evaluate the advantages and disadvantages of each method. The chapter finds that in thoracic organ segmentation, there are no differences between a multi-organ segmentation network and organ-specific networks in terms of performance. This is likely due to the fact that the organ segmentation is largely independent.