Organ at Risk Segmentation in Head and Neck CT Images by Using a Two-Stage Segmentation Framework Based on 3D U-Net

TL;DR

This paper introduces a two-stage 3D U-Net based framework for accurate organ at risk segmentation in head and neck CT images, improving performance over state-of-the-art methods.

Contribution

The novel two-stage segmentation approach decomposes the task into locating and segmenting each organ, enhancing accuracy and efficiency.

Findings

Ranked first in boundary-based metric 95HD for 8 of 9 OARs.

Achieved top performance in DSC metric for 6 of 9 OARs.

Outperformed existing methods on MICCAI 2015 dataset.

Abstract

Accurate segmentation of organ at risk (OAR) play a critical role in the treatment planning of image guided radiation treatment of head and neck cancer. This segmentation task is challenging for both human and automatic algorithms because of the relatively large number of OARs to be segmented, the large variability of the size and morphology across different OARs, and the low contrast of between some OARs and the background. In this paper, we proposed a two-stage segmentation framework based on 3D U-Net. In this framework, the segmentation of each OAR is decomposed into two sub-tasks: locating a bounding box of the OAR and segment it from a small volume within the bounding box, and each sub-tasks is fulfilled by a dedicated 3D U-Net. The decomposition makes each of the two sub-tasks much easier, so that they can be better completed. We evaluated the proposed method and compared it to state-of-the-art methods by using the MICCAI 2015 Challenge dataset. In terms of the boundary-based metric 95HD, the proposed method ranked first in eight of all nine OARs and ranked second in the other OAR. In terms of the area-based metric DSC, the proposed method ranked first in six of the nine OARs and ranked second in the other three OARs with small difference with the first one.