Segmentation of Organs-at-Risk and Gross Tumor Volume for Radiotherapy Planning of Nasopharyngeal Carcinoma Challenge 2023
Creators: name: Xiangde Luo
name: Wenjun Liao
name: Zhou, Mu
name: Fu, Jia
name: Shichuan Zhang
name: Guotai Wang
name: Shaoting Zhang
Publisher: name: Zenodo
Year: 2023
Subjects: Organ-at-risk
Gross Target Volume
segmentation
radiotherapy
MICCAI Challenges

description: Radiotherapy is one of the most important cancer treatments for killing cancer cells with external beam radiation. Treatment planning is vital for the treatment, which sets up the radiation dose distribution for tumor and ordinary organs. The goal of planning is to ensure the cancer cells receive enough radiation and to prevent normal cells in organs-at-risk (OARs) from being damaged too much. For instance, optical nerves and chiasma in the head cannot receive too much radiation, otherwise the patient risks losing his/her vision. Gross Target Volume (GTV) is the position and extent of gross tumor imaged by CT scans, i.e., what can be seen. One important step in radiation treatment planning is to delineate the boundaries of GTV and tens of OARs. However, manual delineation slice-by-slice in the CT scans is tedious and time-consuming for radiation oncologists. Automatic delineation of GTV and OARs would substantially reduce the treatment planning time and therefore improve the efficiency of radiotherapy. We propose to hold the challenge of Segmentation of Organs-at-Risk and Gross Tumor Volume for Radiotherapy Planning of Nasopharyngeal Carcinoma Challenge 2023 in conjunction with MICCAI 2023. We will provide a dataset of CT scans of patients with nasopharyngeal-carcinoma, where the segmentation targets include OARs, Gross Target Volume of nasopharynx (GTVnx) and Gross Target Volume of lymph node (GTVnd). The dataset consists of CT scans from 200 patients where each patient has two CT scans (no-contrast CT and contrast-enhanced CT) with pixel-level annotations of 45 OARs, GTVnx and GTVnd. This is a comprehensive automatic OAR and GTV delineation challenge with much training data on GTVnx and OARs segmentation provided to the research community. It was also similar to StructSeg 2019, which was hosted in MICCAI2019 (718 teams registered, and 34 teams submitted their final models for evaluation, and the challenge talks and ceremony attracted a large group of audience to learn about the top-ranking methods during MICCAI 2019). However, the dataset in StructSeg 2019 was relatively small and consisted of non-contrast CT scans from 60 patients with 22 OARs and GTVnx annotations (50 and 10 cases for training and testing, respectively). In our proposed SegRap2023, we provide an extensive and comprehensive dataset with 200 patients, 45 OARs, GTVnd and a new imaging modality (contrast-enhanced CT). Note that all images of the SegRap2023 dataset were collected and annotated by M.D. W. Liao and S. Zhang and their team in Sichuan Cancer Hospital &amp; Institute, Sichuan Cancer Center, so there are not any overlapped patients between StructSeg2019 and SegRap2023. Based on this extensive and comprehensive dataset, two sub-tasks will be held in SegRap2023: Task01: 45 OARs segmentation from no-contrast and contrast-enhanced CT scans and<br> Task02: GTVnx and GTVnd segmentation from no-contrast and contrast-enhanced CT scans. SegRap2023 will provide an online evaluation server (grand-challenge.com), and it is expected to continually attract much attention from the research community and advance the research on OAR and GTV segmentation significantly