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Automatic Organ Segmentation Tool for Radiation Treatment Planning of Cancers

用于癌症放射治疗计划的自动器官分割工具

基本信息

批准号：
10518374
负责人：
Xue Feng
金额：
$ 4.95万
依托单位：
CARINA MEDICAL, LLC
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-02-01 至 2023-07-31
项目状态：
已结题

项目摘要

ABSTRACT As early detection and better treatment have increased cancer patient survival rates, the importance of protecting normal organs during radiation treatment is drawing more attention, which is critical in reducing long term toxicity of cancers. To avoid excessively high radiation doses to organs-at-risk (OARs), OARs need to be correctly segmented from simulation computed tomography (CT) scans during radiation treatment planning to get an accurate dose distribution. Despite tremendous effort in developing semi- or fully-automatic segmentation solutions, current automated segmentation software, mostly using the atlas-based methods, has not yet reached the level of accuracy and robustness required for clinical usage. Therefore, in current practice, significant manual efforts are still required in the OAR segmentation process. Manual contouring suffers from inter- and intra-observer variability, as well as institutional variability where different sites adopt distinct contouring atlases and labeling criteria, thus leading to inaccuracy and variability in OAR segmentation. When OARs are very close to the treatment target, segmentation errors as small as a few millimeters can have a statistically significant impact on dosimetry distribution and outcome. In addition, it is also costly and time consuming as it can take 1-2 hours of a clinicians’ time to segment major thoracic organs due to the large number of axial slices required. In summary, an accurate and fast process for segmenting OARs in treatment planning using CT scans is needed for improving patient outcomes and reducing the cost of radiation therapy of cancers. In recent years, the rapid development of deep learning methods has revolutionized many computer-vision areas and the adoption of deep learning in medical applications has shown great success. Based on a deep-learning-based algorithm we developed that achieved better-than-human performance and ranked 1st in 2017 American Association of Physicist in Medicine Thoracic Auto-segmentation Challenge, an automatic OAR segmentation product will be developed in this project with the three aims: 1) further improve the performance and robustness of OAR segmentation algorithms, focusing on addressing the heterogeneity issue of different clinical environments; 2) further enrich the functionalities and enhance usability of the cloud- based software product; and 3) perform clinical validation study on the algorithm performance and software usability at collaborating sites. With this product, the segmentation accuracy can be improved, leading to more robust treatment plans in protecting normal organs and improved long term patient outcome. The time and cost of radiation treatment planning can be greatly reduced, contributing to a more affordable cancer treatment and reduced healthcare burden.

摘要

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Improving accuracy and robustness of deep convolutional neural network based thoracic OAR segmentation.

DOI：
10.1088/1361-6560/ab7877
发表时间：
2020-03-31
期刊：
Physics in medicine and biology
影响因子：
3.5
作者：
Feng X;Bernard ME;Hunter T;Chen Q
通讯作者：
Chen Q

Head and neck tumor segmentation convolutional neural network robust to missing PET/CT modalities using channel dropout.

DOI：
10.1088/1361-6560/accac9
发表时间：
2023-04-25
期刊：
Physics in medicine and biology
影响因子：
3.5
作者：
通讯作者：

Convolutional neural network enhancement of fast-scan low-dose cone-beam CT images for head and neck radiotherapy.

DOI：
10.1088/1361-6560/ab6240
发表时间：
2020-01-27
期刊：
Physics in medicine and biology
影响因子：
3.5
作者：
Yuan N;Dyer B;Rao S;Chen Q;Benedict S;Shang L;Kang Y;Qi J;Rong Y
通讯作者：
Rong Y

Contouring quality assurance methodology based on multiple geometric features against deep learning auto-segmentation.

基于多个几何特征和深度学习自动分割的轮廓质量保证方法。

DOI：
10.1002/mp.16299
发表时间：
2023
期刊：
Medical physics
影响因子：
3.8
作者：
Duan,Jingwei;Bernard,MarkE;Castle,JamesR;Feng,Xue;Wang,Chi;Kenamond,MarkC;Chen,Quan
通讯作者：
Chen,Quan

Development of a virtual source model for Monte Carlo-based independent dose calculation for varian linac.