Advances in Auto-Segmentation
Advances in Auto-Segmentation
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DOI：
10.1016/j.semradonc.2019.02.001
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发表时间：
2019-07-01
影响因子：
3.5
通讯作者：
Brock, Kristy B.
中科院分区：
文献类型：
--
作者：
Cardenas, Carlos E.;Yang, Jinzhong;Brock, Kristy B.
人工图像分割是放射治疗中常规执行的一项耗时的任务，以识别每个患者的靶点和解剖结构。放射治疗计划的有效性和安全性需要准确的分割，因为这些感兴趣的区域通常用于优化和评估计划的质量。然而，有报告表明，这一进程可能会受到观察员之间和观察员内部的重大变化。此外，放射治疗和随后的分析(即放射组学、剂量学)的质量可能取决于这些手动分割的准确性。因此，目标和正常组织的自动分割(或自动分割)是更可取的，因为它将解决这些挑战。以前，自动分割技术已经分成了三代算法，其中基于多图谱和混合技术(第三代)被认为是最先进的。然而，最近，在计算机视觉进步的推动下，医学图像分割领域出现了加速增长，特别是通过深度学习算法的应用，这表明我们已经进入了第四代自动分割算法的开发。在这篇文章中，作者回顾了传统的(非深度学习)算法，特别是与放射治疗应用相关的算法。介绍了深度学习的概念，重点介绍了卷积神经网络和全卷积网络，这两种网络通常用于分割任务。此外，作者还总结了文献中报道的深度学习自动分割放射治疗的应用。最后，对自动分割软件的临床部署(调试和质量保证)提出了考虑因素。(C)2019 Elsevier Inc.保留所有权利。
Manual image segmentation is a time-consuming task routinely performed in radiotherapy to identify each patient's targets and anatomical structures. The efficacy and safety of the radiotherapy plan requires accurate segmentations as these regions of interest are generally used to optimize and assess the quality of the plan. However, reports have shown that this process can be subject to significant inter- and intraobserver variability. Furthermore, the quality of the radiotherapy treatment, and subsequent analyses (ie, radiomics, dosimetric), can be subject to the accuracy of these manual segmentations. Automatic segmentation (or auto-segmentation) of targets and normal tissues is, therefore, preferable as it would address these challenges. Previously, auto-segmentation techniques have been clustered into 3 generations of algorithms, with multiatlas based and hybrid techniques (third generation) being considered the state-of-the-art. More recently, however, the field of medical image segmentation has seen accelerated growth driven by advances in computer vision, particularly through the application of deep learning algorithms, suggesting we have entered the fourth generation of auto-segmentation algorithm development. In this paper, the authors review traditional (nondeep learning) algorithms particularly relevant for applications in radiotherapy. Concepts from deep learning are introduced focusing on convolutional neural networks and fully-convolutional networks which are generally used for segmentation tasks. Furthermore, the authors provide a summary of deep learning auto-segmentation radiotherapy applications reported in the literature. Lastly, considerations for clinical deployment (commissioning and QA) of auto-segmentation software are provided. (C) 2019 Elsevier Inc. All rights reserved.