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

• 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
• 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
• 来源: https://reporter.nih.gov/project-details/10518374
• 关键词: Address; Adopted; Adoption; Algorithms; American; Area; Atlases; Attention; Cancer Patient; Chest; Clinical; Computer Vision Systems; Computer software; Consumption; Development; Dose; Early Diagnosis; Environment; Healthcare; Heterogeneity; Hour; Human; Intraobserver Variability; Label; Malignant Neoplasms; Manuals; Medical; Medicine; Methods; Organ; Outcome; Patient-Focused Outcomes; Performance; Process; Radiation Dose Unit; Radiation therapy; Risk; Scanning; Site; Slice; Survival Rate; Time; Toxic effect; Treatment Cost; X-Ray Computed Tomography; automated segmentation; base; cancer radiation therapy; cancer therapy; cloud based; cost; deep learning; dosimetry; improved; learning strategy; millimeter; segmentation algorithm; simulation; success; tool; treatment planning; usability; validation studies

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 由于早期发现和更好的治疗提高了癌症患者的生存率， 在放射治疗期间保护正常器官引起了更多的关注，这对于减少长时间的 癌症的毒性。为了避免对危及器官（OAR）造成过高的辐射剂量，OAR需要 在放射治疗计划期间从模拟计算机断层扫描（CT）中正确分割， 得到精确的剂量分布。尽管在开发半自动或全自动汽车方面付出了巨大努力， 分割解决方案，目前的自动分割软件，主要使用基于atlas的方法， 尚未达到临床使用所需的准确性和稳健性水平。因此，在目前的实践中， OAR分割过程中仍然需要大量的手动工作。手动轮廓绘制遭受 观察者之间和观察者内部的差异，以及不同地点采用不同 轮廓图谱和标记标准，从而导致OAR分割的不准确性和可变性。当 OAR非常接近治疗目标，小至几毫米的分割误差可以具有 对剂量分布和结果的统计学显著影响。此外，还需要花费成本和时间 这是因为由于大的胸壁， 所需的轴向切片数量。总之，在治疗中分割OAR的准确和快速的过程 需要使用CT扫描进行规划，以改善患者的预后并降低放射治疗的成本 癌症。近年来，深度学习方法的快速发展彻底改变了许多 计算机视觉领域和深度学习在医疗应用中的采用已经取得了巨大的成功。 基于我们开发的基于深度学习的算法，该算法实现了优于人类的性能， 在2017年美国医学物理学家协会胸部自动分割挑战赛中排名第一 本项目将开发一个OAR自动分割产品，目标有三：1）进一步提高 OAR分割算法的性能和鲁棒性，重点是解决异构性 不同临床环境的问题; 2）进一步丰富云的功能并增强其可用性- 基于软件产品;以及3）对算法性能和软件进行临床验证研究 协作站点的可用性。有了这个产品，分割精度可以提高，导致更多 保护正常器官和改善长期患者预后的稳健治疗计划。的时间和成本 可以大大减少放射治疗计划，有助于更负担得起的癌症治疗， 减轻医疗负担。

项目成果

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

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.

• DOI: 10.1002/acm2.13556
• 发表时间: 2022-05
• 期刊: Journal of applied clinical medical physics
• 影响因子: 2.1