Welcome to Automatic Structure Segmentation for Radiotherapy Planning Challenge 2019!  This competition is part of the MICCAI 2019¶

Challenge.

Important Dates¶

• Release of training data: Jun. 15th, 2019
• The training data release time extends to Jun. 22nd, 2019
• Submission deadline for results: Sept. 15th, 2019 
• Submission start time: Sep. 19th, 2019 11:59 UTC-8 Beijing Time
• Submission deadline: Sep. 26th, 2019 11:59 UTC-8 Beijing Time
• ~~Announcement of final results: Oct. 1st, 2019 ~~
• results site (under repair): http://www.structseg-challenge.org/
• Presentation and award ceremony: Oct. 13th, 2019
• Public submission: Dec. 26th, 2019 13:00 UTC-8 Beijing Time

Overview¶

The goal of the challenge is to set up tasks for evaluating automatic algorithms on segmentation of organs-at-risk (OAR) and gross target volume (GTV) of tumors of two types of cancers, nasopharynx cancer and lung cancer, for radiation therapy planning. There are four tasks for evaluating the performance of the algorithms. Participants can choose to join one or all tasks according to their interests.  - Task 1: Organ-at-risk segmentation from head & neck CT scans. 

• Task 2: Gross Target Volume segmentation of nasopharynx cancer. 

• Task 3: Organ-at-risk segmentation from chest CT scans. 

• Task 4: Gross Target Volume segmentation of lung cancer.            

Special Issue¶

Top-ranking teams on each task will be invited to submit full papers on describing their methods to the special issue on Deep learning for Medical Image Computing of¶

Neurocomputing (IF= 4.072). The guest editors are

• Hongsheng Li (SenseTime Research & CUHK) 
• Shaoting Zhang (SenseTime Research) 
• Dimitris N. Metaxas (Rutgers University)

Background¶

Radiation therapy is one type of important cancer treatment 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 receiving enough radiation and to prevent normal cells in organs-at-risk (OAR) from being damaged too much. Organs-at-risk are usually the organs that are sensitive to radiation. 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 radiotherapy treatment planning is therefore to delineate the boundaries of tens of OARs and GTV in every slice of a patient's CT scans, which is tedious and occupies much of oncologists' time. Automatic OAR and GTV delineation would substantially reduce the treatment planning time and therefore reduce the overall cost for radiotherapy.

Dataset¶

In the challenge, participants will be provided with four datasets: 

1. Head & neck CT scans for Organ-at-risk segmentation. 22 OARs of 50 nasopharynx cancer patients will be annotated and released to public as the training data. Another 10 patients’ CT scans will be used as the test data. 
2. Gross Target Volume segmentation for nasopharynx cancer. The 50 GTV annotations of the same 50 nasopharynx cancer patients’ CT scans will be provided as the training data. Another 10 patients’ GTV will be used as the test data. 
3. Chest CT scans for Organ-at-risk segmentation. 6 OARs of 50 lung cancer patients will be annotated and released to public as the training data. Another 10 patients’ CT scans will be used as the test data. 
4. Gross Target Volume segmentation of lung cancer. The 50 GTV annotations of the same 50 lung cancer patients’ CT scans will be provided as the training data. Another 10 patients’ GTV will be used as the test data.

The data and annotations are provided by Zhejiang Cancer Hospital. (See the Detailed Dataset Description).

If you have any questions or comments, please mail to support@structseg-challenge.org

This challenge is origanized by the following institutions