Clinical evaluation of deep learning and atlas-based auto-segmentation for critical organs at risk in radiation therapy.
Clinical evaluation of deep learning and atlas-based auto-segmentation for critical organs at risk in radiation therapy.
复制标题
DOI：
10.1002/jmrs.618
复制
发表时间：
2023-04
影响因子：
2.1
通讯作者：
Last, Andrew
中科院分区：
文献类型：
--
作者：
Gibbons, Eddie;Hoffmann, Matthew;Westhuyzen, Justin;Hodgson, Andrew;Chick, Brendan;Last, Andrew
危及器官轮廓是一项耗时的任务，是放射治疗的关键部分。基于Atlas的自动分割在减少从业者的时间负担方面取得了一定的成功；然而，这种方法往往需要大量的手工编辑才能达到临床准确的标准。深度学习（DL）自动分割最近成为一种很有前途的解决方案。本研究比较了DL和基于图谱的自动分割与临床“金标准”参考轮廓的准确性。90个CT数据集（30个头颈，30个胸椎，30个骨盆）使用atlas和DL分割技术自动轮廓。然后使用Dice相似系数（DSC）和Hausdorff距离（HD）定量测量16个关键桨的准确性。进行定性分析，以直观地将每个结构的准确性分类为四个明确定义的类别之一。此外，我们还记录了9个OARs子集将图谱和DL轮廓编辑到临床可接受水平的时间。在分析的16个桨中，DL在DSC测量的13个桨中，在HD测量的12个桨中，在定性测量的12个桨中，在统计上显著改善了图谱分割。DL轮廓子集的平均编辑时间分别比头颈部、胸部和骨盆的寰椎分割快50%、23%和61%（均P < 0.05）。对于大多数评估的桨，深度学习分割全面优于基于图谱的轮廓。在几何精度和视觉可接受性方面观察到改进，同时减少了编辑时间，从而提高了工作流程效率。在放射治疗中，绘制危险器官的轮廓是一项耗时的任务。自动分割是一种工具，可以用来减轻这种时间负担的临床医生。基于深度学习的自动分割已被证明比基于地图集的技术提供更高的准确性和节省时间的能力。
Contouring organs at risk (OARs) is a time‐intensive task that is a critical part of radiation therapy. Atlas‐based automatic segmentation has shown some success at reducing this time burden on practitioners; however, this method often requires significant manual editing to reach a clinically accurate standard. Deep learning (DL) auto‐segmentation has recently emerged as a promising solution. This study compares the accuracy of DL and atlas‐based auto‐segmentation in relation to clinical ‘gold standard’ reference contours. Ninety CT datasets (30 head and neck, 30 thoracic, 30 pelvic) were automatically contoured using both atlas and DL segmentation techniques. Sixteen critical OARs were then quantitatively measured for accuracy using the Dice similarity coefficient (DSC) and Hausdorff distance (HD). Qualitative analysis was performed to visually classify the accuracy of each structure into one of four explicitly defined categories. Additionally, the time to edit atlas and DL contours to a clinically acceptable level was recorded for a subset of 9 OARs. Of the 16 OARs analysed, DL delivered statistically significant improvements over atlas segmentation in 13 OARs measured with DSC, 12 OARs measured with HD, and 12 OARs measured qualitatively. The mean editing time for the subset of DL contours was 50%, 23% and 61% faster (all P < 0.05) than that of atlas segmentation for the head and neck, thorax, and pelvis respectively. Deep learning segmentation comprehensively outperformed atlas‐based contouring for the majority of evaluated OARs. Improvements were observed in geometric accuracy and visual acceptability, while editing time was reduced leading to increased workflow efficiency. Contouring organs at risk is a time consuming task in radiation therapy. Auto‐segmentation is a tool that can be used to alleviate this time burden on clinicians. Deep learning based auto‐segmentation has been shown to offer improved accuracy and time saving capabilities over atlas‐based techniques.
登录
查看更多内容
影响因子：
--
作者：
Vaassen F;Hazelaar C;Vaniqui A;Gooding M;van der Heyden B;Canters R;van Elmpt W
通讯作者：
van Elmpt W
影响因子：
--
作者：
Brouwer CL;Boukerroui D;Oliveira J;Looney P;Steenbakkers RJHM;Langendijk JA;Both S;Gooding MJ
通讯作者：
Gooding MJ
影响因子：
5.7
作者：
Sherer, Michael, V;Lin, Diana;Elguindi, Sharif;Duke, Simon;Tan, Li-Tee;Cacicedo, Jon;Dahele, Max;Gillespie, Erin F.
通讯作者：
Gillespie, Erin F.
影响因子：
3.3
作者：
Zabel, W. Jeffrey;Conway, Jessica L.;McVicar, Nevin
通讯作者：
McVicar, Nevin
DOI：
10.1186/1748-717x-7-32
发表时间：
2012-03-13
期刊：
Radiation oncology (London, England)
影响因子：
--
作者：
Brouwer CL;Steenbakkers RJ;van den Heuvel E;Duppen JC;Navran A;Bijl HP;Chouvalova O;Burlage FR;Meertens H;Langendijk JA;van 't Veld AA
通讯作者：
van 't Veld AA