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: 10081752
• 负责人: Xue Feng
• 金额: $ 100万
• 依托单位: CARINA MEDICAL, LLC
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-05-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10081752
• 关键词: 3-Dimensional; Address; Adopted; Adoption; Algorithms; American; Area; Artificial Intelligence; Atlases; Attention; Body Regions; Body part; Cancer Patient; Chest; Clinical; Clinical Research; Computer Vision Systems; Computer software; Consumption; Data; Development; Digital Imaging and Communications in Medicine; Dose; Early Diagnosis; Environment; Healthcare; Heterogeneity; Hour; Human; Image; Intraobserver Variability; Label; Malignant Neoplasms; Manuals; Measures; Medical; Medicine; Methods; Modality; Modeling; Online Systems; Organ; Outcome; Patient-Focused Outcomes; Performance; Phase; Process; Protocols documentation; Radiation Dose Unit; Radiation therapy; Risk; Scanning; Site; Slice; Survival Rate; Techniques; Testing; Time; Toxic effect; Treatment Cost; Update; X-Ray Computed Tomography; algorithm development; automated segmentation; base; cancer radiation therapy; cancer therapy; clinical heterogeneity; cloud based; commercialization; convolutional neural network; cost; deep learning; deep learning algorithm; dosimetry; healthcare community; imaging modality; improved; innovation; learning strategy; life-long learning; millimeter; novel; phase 1 study; prototype; satisfaction; segmentation algorithm; simulation; software development; success; tool; treatment planning; usability; user-friendly; 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.

抽象的 由于早期检测和更好的治疗提高了癌症患者的生存率，因此 放射治疗期间保护正常器官越来越受到关注，这对于减少长时间的治疗至关重要 为了避免危及器官 (OAR) 受到过高的辐射剂量，需要对 OAR 进行长期治疗。 在放射治疗计划期间从模拟计算机断层扫描 (CT) 扫描中正确分割 尽管在开发半自动或全自动方面付出了巨大的努力，但仍能获得准确的剂量分布。 分割解决方案，目前的自动分割软件，大多使用基于图集的方法，已经 尚未达到临床使用所需的准确性和鲁棒性水平，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。 观察者之间和观察者内部的变异性，以及不同地点采用不同方法的制度变异性 轮廓图集和标记标准，从而导致 OAR 分割的不准确性和可变性。 OAR 非常接近治疗目标，小至几毫米的分割误差都会产生 对剂量测定分布和结果有统计上的显着影响此外，它也很昂贵和时间。 由于胸腔器官体积较大，分割主要胸腔器官可能需要 1-2 个小时的时间。 总之，在治疗中分割 OAR 的准确而快速的过程。 需要使用 CT 扫描进行规划，以改善患者的治疗效果并降低放射治疗的成本 近年来，深度学习方法的快速发展已经彻底改变了许多癌症。 计算机视觉领域以及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，并且 2017年美国医学物理学家协会胸部自动分割挑战赛排名第一 本项目将开发自动OAR分割产品，其三个目标是：1）进一步改进 OAR 分割算法的性能和鲁棒性，重点解决异构性 2）进一步丰富云的功能并增强可用性- 基于软件产品；3）对算法性能和软件进行临床验证研究 通过该产品，可以提高分割准确性，从而带来更多结果。 保护正常器官并改善患者长期治疗效果的强有力的治疗计划。 放射治疗计划的数量可以大大减少，从而有助于提供更实惠的癌症治疗 减轻医疗负担。