Automated Object Contouring Methods & Software for Radiotherapy Planning

自动对象轮廓方法

• 批准号: 9761481
• 负责人: Steve Owens
• 金额: $ 87.34万
• 依托单位: QUANTITATIVE RADIOLOGY SOLUTIONS, LLC
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-06-10 至 2021-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9761481
• 关键词: Adoption; Algorithms; Anatomy; Automation; Back; Blinded; Body Regions; Chest; Clinical; Collaborations; Computer software; Country; Data Quality; Data Set; Development; Digital Imaging and Communications in Medicine; Evaluation; Evaluation Studies; Grant; Head and neck structure; Health Personnel; Image; Imagery; Industry; Inferior; Investigation; Investments; Learning; Location; Malignant Neoplasms; Malignant neoplasm of thorax; Manuals; Medical Imaging; Medical center; Methods; Modeling; Morphologic artifacts; Names; Object Attachment; Organ; Outcome; Output; Pathology; Patients; Phase; Population Heterogeneity; Process; Protocols documentation; Radiation Oncologist; Radiation therapy; Reader; Research; Risk; Scanning; Service delivery model; Services; Slice; Specific qualifier value; System; Techniques; Testing; Three-Dimensional Image; Time; Work; base; clinical efficacy; cloud based; deep learning; image processing; improved; interest; learning network; learning strategy; neural network; novel strategies; object recognition; prototype; research clinical testing; software as a service; software development; software systems; treatment center; treatment planning; virtual

Abstract In 2015, 1,658,370 new cancer cases are estimated to occur in the US, where nearly two-thirds will have radiation therapy (RT). Given that there are over 2,300 RT centers in the US, and current systems for contouring organs at risk (OARs) rely mostly on manual methods, there is a strong commercial opportunity for producing a software system that can contour OARs in medical images at a high degree of automation and for impacting current practice of RT planning. Encouraged by our strong Phase I results in thoracic and head and neck (H&N) body regions compared to current industry systems, we seek the accuracy, efficiency, and clinical acceptance of the contours output by our software product to significantly exceed those of existing systems. Our overall aim for Phase II is to advance the algorithms and prototype software developed in Phase I into a leading commercial software product, and demonstrate its efficacy in multiple medical centers across the country with diverse populations. Phase II specific aims are three-fold: (1) Further advance the automatic anatomy recognition algorithms from Phase I using advanced deep learning techniques. (2) Develop a cloud-based software auto contouring service. (3) Perform clinical evaluation of the new software on H&N and thoracic cases. Aim 1 will be accomplished in three stages: (a) Automating the process of defining the body region on given patient CT studies, which is currently done manually in our system, via a new concept of virtual landmarks using deep learning techniques. (b) Improving object recognition/ localization accuracy from the current 2 voxels for “good” quality data sets to 1 voxel and from 4-5 voxels for “poor” quality data sets to 2-3 voxels by using virtual landmarks to learn object relationships. (c) Improving object delineation by combining object localization methods with deep learning techniques applied to the vicinity of the localized objects to bring boundary distance accuracy within 1 voxel. Aim 2 will be achieved by developing a cloud-based Software-as-a-Service model to implement the software that incorporates the algorithms. To accomplish Aim 3, an evaluation study involving four academic RT centers will be undertaken to assess the efficiency, accuracy, and acceptability of the contours output by the new software. To assess efficiency, contouring time taken by the current clinical process will be compared to the time taken by the new software method plus any manual adjustment needed. Accuracy will be assessed by comparing software output to carefully prepared ground truth contours. Acceptability will be determined by conducting a blinded reader study, where an acceptability score (1-5) is given by radiation oncologists to software produced contours, ground truth contours, and contours produced by the normal clinical process, and comparing these scores. Expected clinical outcomes are significantly improved clinical efficiency/ acceptability of contouring compared to current practice.

摘要