Automatic Segmentation of Organs in Computed Tomography for Radiation Therapy Pla

放射治疗平面计算机断层扫描中器官的自动分割

• 批准号: 8123632
• 负责人: Mark McKenzie Roden
• 金额: $ 25.99万
• 依托单位: MEDICAL VISION SYSTEMS
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-02 至 2012-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8123632
• 关键词: Abdomen; Address; Affect; Algorithms; Anatomic structures; Anatomy; Area; Arthritis; Atlases; Automation; Bladder; Clinical; Collimator; Computer software; Conformal Radiotherapy; Data Collection; Databases; Dictionary; Dose; Ensure; Equipment; Femur; Foundations; Future; Goals; Head; Image; Intensity-Modulated Radiotherapy; Learning; Left; Literature; Magnetic Resonance Imaging; Malignant neoplasm of prostate; Manuals; Maps; Masks; Measures; Medical; Medical Imaging; Methods; Modeling; Motion; Organ; Outcome; Patients; Pelvis; Phase; Predictive Value; Problem Solving; Prostate; Protocols documentation; Quality of life; Radiation; Radiation therapy; Rectum; Reporting; Research Personnel; Scanning; Seminal Vesicles; Site; Small Business Innovation Research Grant; Solutions; Speed; Structure; System; Techniques; Technology; Testing; Therapeutic; Time; Tissues; Toxic effect; Validation; Variant; Vision; Work; X-Ray Computed Tomography; base; cancer therapy; cost; image registration; imaging Segmentation; improved; innovation; interest; meetings; novel; rectal; simulation; tool; treatment center; treatment planning; urinary

DESCRIPTION (provided by applicant): Medical Vision Systems has developed a new image segmentation technology that improves the accuracy and robustness of automated medical image segmentation. Our commercial goal is to offer a product that produces accurate, fast, fully automatic segmentations at low cost to aid the Intensity Modulated Radiation Therapy (IMRT) treatment of prostate cancer. The use of Intensity-Modulated Radiation Therapy (IMRT) has become the preferred method for treating prostate cancer through radiation doses in excess of 70 Gy [1]. This technology has been developed to ensure that therapeutic doses of radiation are delivered only to the target organs, the prostate and the seminal vesicles, without affecting nearby non- involved structures, such as the bladder and the rectum. This technique relies on dose planning software and dynamic multileaf collimators to shield these sensitive organs. Without preventative measures, 15% to 35% of patients developed grade 2 or worse rectal toxicity [2, 19, 20, 22, 31, 32] and, with less statistical certainty, increased late urinary complications [5, 12, 30]. IMRT Treatment Planning software requires accurate segmentations of the organs in the abdomen and pelvis. Segmentations of the prostate that include neighboring tissues can irradiate those tissues unnecessarily, while segmentations of organs such as the bladder or rectum that include too much surrounding tissue can interfere with complete delivery of radiation dose. Thus, an inaccurate segmentation has a real effect on the quality of life of the patient. Furthermore, the time required to produce a manual segmentation of the organs of interest significantly limits the number of radiation therapy treatments that can be undertaken. In addition, if image-guided radiation therapy (IGRT) supplants IMRT in the same way that IMRT has supplanted conformal therapy, then manual segmentation will become an even more limiting bottleneck in contemporary radiation therapy. Preliminary work has begun through partnerships with three cancer treatment centers. The investigators have built upon previous work that shows the Auto Context Model (ACM) with AdaBoost is effective at subcortical segmentation in magnetic resonance (MR) imaging [15, 14, 16, 17, 18]. We propose to build upon this foundation to develop a new learning-based image segmentation system capable of accurately and automatically segmenting organs of interest in abdominal x-ray computed tomography (CT) scans taken at different imaging facilities. Accurate segmentation of organs in abdominal CT images is complicated by large variations in abdominal anatomy, motion of abdominal tissues, the limited contrast between anatomic structures in x-ray computed tomography, and variations in imaging equipment, protocols, and techniques. In this Phase I SBIR proposal, we will address the limitations of existing segmentation tools to achieve the accuracy and automation required for IMRT treatment planning. We propose to develop an innovative learning-based segmentation system using both bottom-up and top-down approaches. We will construct a novel feature dictionary, implicitly incorporate atlas-based segmentation methods through the use of image registration techniques, and build a modest "ground truth" database using images acquired and segmented by our collaborators. A Phase II proposal would demonstrate the clinical feasibility of these techniques, addressing both the stability of the system across multiple imaging sites and determining the impact of the proposed system on patient outcomes through increased segmentation accuracy. PUBLIC HEALTH RELEVANCE: Medical Vision Systems has developed a new learning-based image segmentation technology that can simultaneously improve the accuracy and robustness of automated medical image segmentation. Our commercial goal is to offer a product that produces accurate, fast, and fully automatic segmentations at low cost to aid in Intensity Modulated Radiation Therapy (IMRT) and, in the future, Image Guided Radiation Therapy (IGRT) for prostate cancer.

描述（由申请人提供）：Medical Vision Systems开发了一种新的图像分割技术，可提高自动医学图像分割的准确性和鲁棒性。我们的商业目标是提供一种产品，以低成本产生准确，快速，全自动分割，以帮助前列腺癌的调强放射治疗（IMRT）治疗。 调强放射治疗（IMRT）的使用已成为通过超过70戈伊的辐射剂量治疗前列腺癌的首选方法[1]。开发该技术是为了确保治疗剂量的辐射仅输送到靶器官、前列腺和精囊，而不影响附近的非受累结构，例如膀胱和直肠。该技术依赖于剂量规划软件和动态多叶准直器来屏蔽这些敏感器官。在没有预防措施的情况下，15%-35%的患者发生2级或更严重的直肠毒性[2，19，20，22，31，32]，并且在统计学确定性较低的情况下，增加了晚期泌尿系统并发症[5，12，30]。 调强放射治疗计划软件需要准确分割腹部和骨盆中的器官。包括相邻组织的前列腺的分割可能不必要地照射那些组织，而包括太多周围组织的器官（诸如膀胱或直肠）的分割可能干扰辐射剂量的完全递送。因此，不准确的分割对患者的生活质量具有真实的影响。此外，产生感兴趣器官的手动分割所需的时间显著地限制了可以进行的放射治疗处置的数量。此外，如果图像引导放射治疗（IGRT）以IMRT取代适形治疗的方式取代IMRT，则手动分割将成为当代放射治疗中更具限制性的瓶颈。 通过与三个癌症治疗中心的合作，初步工作已经开始。研究人员基于先前的工作，该工作表明使用AdaBoost的自动上下文模型（ACM）在磁共振（MR）成像中的皮层下分割中是有效的[15，14，16，17，18]。我们建议在此基础上开发一种新的基于学习的图像分割系统，该系统能够在不同成像设施进行的腹部X射线计算机断层扫描（CT）扫描中准确自动分割感兴趣的器官。腹部CT图像中器官的准确分割由于腹部解剖结构的大变化、腹部组织的运动、X射线计算机断层摄影中解剖结构之间的有限对比度以及成像设备、协议和技术的变化而变得复杂。 在第一阶段SBIR提案中，我们将解决现有分割工具的局限性，以实现IMRT治疗计划所需的准确性和自动化。我们建议开发一个创新的基于学习的分割系统，使用自下而上和自上而下的方法。我们将构建一个新的特征字典，通过使用图像配准技术隐式地结合基于图集的分割方法，并使用我们的合作者获取和分割的图像构建一个适度的“地面实况”数据库。II期提案将证明这些技术的临床可行性，解决系统在多个成像部位的稳定性，并通过提高分割精度确定拟议系统对患者结局的影响。 公共卫生相关性：Medical Vision Systems开发了一种新的基于学习的图像分割技术，可以同时提高自动医学图像分割的准确性和鲁棒性。我们的商业目标是提供一种能够以低成本产生准确、快速和全自动分割的产品，以帮助强度调制放射治疗（IMRT）以及未来针对前列腺癌的图像引导放射治疗（IGRT）。