Deep-learning-based detection and segmentation of organs at risk in nasopharyngeal carcinoma computed tomographic images for radiotherapy planning.
Objective: Accurate detection and segmentation of organs at risks (OARs) in CT image is the key step for efficient planning of radiation therapy for nasopharyngeal carcinoma (NPC) treatment. We develop a fully automated deep-learning-based method (termed organs-at-risk detection and segmentation net...
| Publicado en: | European Radiology Vol. 29; no. 4; pp. 1961 - 1968 |
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| Autores principales: | , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=135371943&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 135371943 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Apr2019 vid: 29 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 135371943 135371943 NLM30302589 135371943 10.1007/s00330-018-5748-9 NLM30302589 135371943 ppf: 1961 ppct: 7 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep-learning-based detection and segmentation of organs at risk in nasopharyngeal carcinoma computed tomographic images for radiotherapy planning. aug: au: Liang, Shujun Tang, Fan Huang, Xia Yang, Kaifan Zhong, Tao Hu, Runyue Liu, Shangqing Yuan, Xinrui Zhang, Yu affil: Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, No. 1838 Guangzhou Northern Avenue, Baiyun District, 510515, Guangzhou, Guangdong, China sug: subj: Therapeutics Methods Body Regions Adult Tomography, X-Ray Computed Middle Age Neural Networks (Computer) Human Adolescence Aged Sensitivity and Specificity Young Adult Female Patient Care Plans Male Validation Studies Comparative Studies Evaluation Research Multicenter Studies OARS Multidimensional Functional Assessment Questionnaire Scales Funding Source Adult: 19-44 years Middle Aged: 45-64 years Adolescent: 13-18 years Aged: 65+ years Female Male ab: Objective: Accurate detection and segmentation of organs at risks (OARs) in CT image is the key step for efficient planning of radiation therapy for nasopharyngeal carcinoma (NPC) treatment. We develop a fully automated deep-learning-based method (termed organs-at-risk detection and segmentation network (ODS net)) on CT images and investigate ODS net performance in automated detection and segmentation of OARs.Methods: The ODS net consists of two convolutional neural networks (CNNs). The first CNN proposes organ bounding boxes along with their scores, and then a second CNN utilizes the proposed bounding boxes to predict segmentation masks for each organ. A total of 185 subjects were included in this study for statistical comparison. Sensitivity and specificity were performed to determine the performance of the detection and the Dice coefficient was used to quantitatively measure the overlap between automated segmentation results and manual segmentation. Paired samples t tests and analysis of variance were employed for statistical analysis.Results: ODS net provides an accurate detection result with a sensitivity of 0.997 to 1 for most organs and a specificity of 0.983 to 0.999. Furthermore, segmentation results from ODS net correlated strongly with manual segmentation with a Dice coefficient of more than 0.85 in most organs. A significantly higher Dice coefficient for all organs together (p = 0.0003 < 0.01) was obtained in ODS net (0.861 ± 0.07) than in fully convolutional neural network (FCN) (0.8 ± 0.07). The Dice coefficients of each OAR did not differ significantly between different T-staging patients.Conclusion: The ODS net yielded accurate automated detection and segmentation of OARs in CT images and thereby may improve and facilitate radiotherapy planning for NPC.Key Points: • A fully automated deep-learning method (ODS net) is developed to detect and segment OARs in clinical CT images. • This deep-learning-based framework produces reliable detection and segmentation results and thus can be useful in delineating OARs in NPC radiotherapy planning. • This deep-learning-based framework delineating a single image requires approximately 30 s, which is suitable for clinical workflows. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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