论文标题
一个统一的3D器官框架，处于风险本地化和放射治疗计划的细分
A unified 3D framework for Organs at Risk Localization and Segmentation for Radiation Therapy Planning
论文作者
论文摘要
CT中的器官 - 危险风险（OAR）的自动定位和分割是医学图像分析任务（例如放射治疗计划）中必不可少的预处理步骤。例如，围绕肿瘤的桨叶分割使辐射最大化对肿瘤区域，而不会损害健康组织。但是，当前的医疗工作流程需要手动描述桨，这很容易出现错误，并且依赖于注释者。在这项工作中，我们旨在引入一条统一的3D管道，以进行OAR定位细分，而不是新的定位或分割体系结构。据我们所知，我们提出的框架充分实现了对医学成像中固有的3D上下文信息的开发。在第一步中，一个3D多变量回归网络预测了器官的质心和边界框。其次，将3D器官特定的分割网络杠杆化以生成多器官分割图。我们的方法在内脏数据集上，总体骰子得分为0.9260 \ pm 0.18 \％$，其中包含具有不同视野和多个器官的CT扫描。
Automatic localization and segmentation of organs-at-risk (OAR) in CT are essential pre-processing steps in medical image analysis tasks, such as radiation therapy planning. For instance, the segmentation of OAR surrounding tumors enables the maximization of radiation to the tumor area without compromising the healthy tissues. However, the current medical workflow requires manual delineation of OAR, which is prone to errors and is annotator-dependent. In this work, we aim to introduce a unified 3D pipeline for OAR localization-segmentation rather than novel localization or segmentation architectures. To the best of our knowledge, our proposed framework fully enables the exploitation of 3D context information inherent in medical imaging. In the first step, a 3D multi-variate regression network predicts organs' centroids and bounding boxes. Secondly, 3D organ-specific segmentation networks are leveraged to generate a multi-organ segmentation map. Our method achieved an overall Dice score of $0.9260\pm 0.18 \%$ on the VISCERAL dataset containing CT scans with varying fields of view and multiple organs.
