Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
Phys Med. 2020 May;73:190-196. doi: 10.1016/j.ejmp.2020.04.011. Epub 2020 May 1.
An open-source library of implementations for deep-learning-based image segmentation and outcomes models based on radiotherapy and radiomics is presented. As oncology treatment planning becomes increasingly driven by automation, such a library of model implementations is crucial to (i) validate existing models on datasets collected at different institutions, (ii) automate segmentation, (iii) create ensembles for improving performance and (iv) incorporate validated models in the clinical workflow. Inclusion of deep-learning-based image segmentation and outcomes models in the same library provides a fully automated and reproduceable pipeline to estimate prognosis. The library was developed with the Computational Environment for Radiological Research (CERR) software platform. Centralizing model implementations in CERR builds upon its rich set of radiotherapy and radiomics tools and caters to the world-wide user base. CERR provides well-validated feature extraction pipelines for radiotherapy dosimetry and radiomics with fine control over the calculation settings, allowing users to select appropriate parameters used in model derivation. Models for automatic image segmentation are distributed via containers, allowing them to be deployed with a variety of scientific computing architectures. The library includes implementations of popular DVH-based models outlined in the Quantitative Analysis of Normal Tissue Effects in the Clinic effort and recently published literature. Radiomics models include features from the Image Biomarker Standardization Initiative and application-specific features found to be relevant across multiple sites and image modalities. The library is distributed as a module within CERR at https://www.github.com/cerr/CERR under the GNU-GPL copyleft with additional restrictions on clinical and commercial use and provision to dual license in future.
本文提出了一个基于放射治疗和放射组学的深度学习图像分割和结果模型的开源实现库。随着肿瘤治疗计划越来越自动化，这样的模型实现库对于（i）在不同机构收集的数据集上验证现有模型，（ii）自动化分割，（iii）创建集成以提高性能，以及（iv）将经过验证的模型纳入临床工作流程至关重要。将基于深度学习的图像分割和结果模型纳入同一个库中，提供了一种完全自动化和可重复的预后估计管道。该库是使用计算放射学研究环境（CERR）软件平台开发的。在 CERR 中集中模型实现，建立在其丰富的放射治疗和放射组学工具集之上，并迎合全球用户基础。CERR 提供了经过良好验证的放射治疗剂量学和放射组学特征提取管道，可对计算设置进行精细控制，允许用户选择在模型推导中使用的适当参数。自动图像分割模型通过容器分发，允许在各种科学计算架构中部署。该库包括在临床定量分析正常组织效应（Quantitative Analysis of Normal Tissue Effects in the Clinic，QUANTEC）工作中概述的流行基于剂量体积直方图（DVH）模型的实现，以及最近发表的文献。放射组学模型包括来自图像生物标志物标准化倡议（Image Biomarker Standardization Initiative）的特征，以及在多个站点和图像模态中发现的与应用相关的特定特征。该库作为 CERR 中的一个模块分发，位于 https://www.github.com/cerr/CERR，采用 GNU-GPL 版权协议，并对临床和商业用途施加额外限制，并规定将来可双重许可。
