Fully Automatic Segmentation of Gross Target Volume and Organs-at-Risk for Radiotherapy Planning of Nasopharyngeal Carcinoma

TL;DR

This paper presents a fully automatic deep learning framework for segmenting organs-at-risk and tumor volumes in CT images of the head and neck, specifically for nasopharyngeal carcinoma, achieving high accuracy in a competitive challenge.

Contribution

The authors developed a novel fully automatic segmentation framework using 3D U-Net models, with preprocessing steps for intensity harmonization and volume cropping, tailored for NPC radiotherapy planning.

Findings

Achieved second place in the SegRap 2023 challenge for both tasks.

Effective preprocessing improved segmentation accuracy.

Framework is publicly available for further research.

Abstract

Target segmentation in CT images of Head&Neck (H&N) region is challenging due to low contrast between adjacent soft tissue. The SegRap 2023 challenge has been focused on benchmarking the segmentation algorithms of Nasopharyngeal Carcinoma (NPC) which would be employed as auto-contouring tools for radiation treatment planning purposes. We propose a fully-automatic framework and develop two models for a) segmentation of 45 Organs at Risk (OARs) and b) two Gross Tumor Volumes (GTVs). To this end, we preprocess the image volumes by harmonizing the intensity distributions and then automatically cropping the volumes around the target regions. The preprocessed volumes were employed to train a standard 3D U-Net model for each task, separately. Our method took second place for each of the tasks in the validation phase of the challenge. The proposed framework is available at https://github.com/Astarakee/segrap2023