Comparative clinical evaluation of atlas and deep-learning-based auto-segmentation of organ structures in liver cancer

被引：72

作者：

Ahn, Sang Hee ^{[1
]}

Yeo, Adam Unjin ^{[2
]}

Kim, Kwang Hyeon ^{[1
]}

Kim, Chankyu ^{[1
]}

Goh, Youngmoon ^{[3
]}

Cho, Shinhaeng ^{[4
]}

Lee, Se Byeong ^{[1
]}

Lim, Young Kyung ^{[1
]}

Kim, Haksoo ^{[1
]}

Shin, Dongho ^{[1
]}

Kim, Taeyoon ^{[1
]}

Kim, Tae Hyun ^{[1
]}

Youn, Sang Hee ^{[1
]}

Oh, Eun Sang ^{[1
]}

Jeong, Jong Hwi ^{[1
]}

机构：

[1] Natl Canc Ctr, Proton Therapy Ctr, Dept Radiat Oncol, 323 Ilsan Ro, Goyang Si 10408, Gyeonggi Do, South Korea

[2] Peter MacCallum Canc Ctr, Melbourne, Vic, Australia

[3] Asan Med Ctr, Dept Radiat Oncol, Seoul, South Korea

[4] Chonnam Natl Univ, Dept Radiat Oncol, Med Sch, Gwangju, South Korea

来源：

RADIATION ONCOLOGY | 2019年 / 14卷 / 01期

关键词：

Contouring; Atlas-based auto-segmentation; Deep-learning-based auto-segmentation; Deep convolution neural network (DCNN); CT IMAGES; REGISTRATION; HEAD;

D O I：

10.1186/s13014-019-1392-z

中图分类号：

R73 [肿瘤学];

学科分类号：

100214 ;

摘要：

Background Accurate and standardized descriptions of organs at risk (OARs) are essential in radiation therapy for treatment planning and evaluation. Traditionally, physicians have contoured patient images manually, which, is time-consuming and subject to inter-observer variability. This study aims to a) investigate whether customized, deep-learning-based auto-segmentation could overcome the limitations of manual contouring and b) compare its performance against a typical, atlas-based auto-segmentation method organ structures in liver cancer. Methods On-contrast computer tomography image sets of 70 liver cancer patients were used, and four OARs (heart, liver, kidney, and stomach) were manually delineated by three experienced physicians as reference structures. Atlas and deep learning auto-segmentations were respectively performed with MIM Maestro 6.5 (MIM Software Inc., Cleveland, OH) and, with a deep convolution neural network (DCNN). The Hausdorff distance (HD) and, dice similarity coefficient (DSC), volume overlap error (VOE), and relative volume difference (RVD) were used to quantitatively evaluate the four different methods in the case of the reference set of the four OAR structures. Results The atlas-based method yielded the following average DSC and standard deviation values (SD) for the heart, liver, right kidney, left kidney, and stomach: 0.92 +/- 0.04 (DSC +/- SD), 0.93 +/- 0.02, 0.86 +/- 0.07, 0.85 +/- 0.11, and 0.60 +/- 0.13 respectively. The deep-learning-based method yielded corresponding values for the OARs of 0.94 +/- 0.01, 0.93 +/- 0.01, 0.88 +/- 0.03, 0.86 +/- 0.03, and 0.73 +/- 0.09. The segmentation results show that the deep learning framework is superior to the atlas-based framwork except in the case of the liver. Specifically, in the case of the stomach, the DSC, VOE, and RVD showed a maximum difference of 21.67, 25.11, 28.80% respectively. Conclusions In this study, we demonstrated that a deep learning framework could be used more effectively and efficiently compared to atlas-based auto-segmentation for most OARs in human liver cancer. Extended use of the deep-learning-based framework is anticipated for auto-segmentations of other body sites.

引用

页码：1 / 13

页数：13

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