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: 10221655
• 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 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10221655
• 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.

摘要