Deep learning for fully automated tumor segmentation and extraction of magnetic resonance radiomics features in cervical cancer.
Objective: To develop and evaluate the performance of U-Net for fully automated localization and segmentation of cervical tumors in magnetic resonance (MR) images and the robustness of extracting apparent diffusion coefficient (ADC) radiomics features.Methods: This retrospective study involved analy...
| Publicado en: | European Radiology Vol. 30; no. 3; pp. 1297 - 1306 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
2020
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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=148390795&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 148390795 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: 2020 vid: 30 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 148390795 148390795 NLM31712961 148390795 10.1007/s00330-019-06467-3 NLM31712961 148390795 ppf: 1297 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep learning for fully automated tumor segmentation and extraction of magnetic resonance radiomics features in cervical cancer. aug: au: Lin, Yu-Chun Lin, Chia-Hung Lu, Hsin-Ying Chiang, Hsin-Ju Wang, Ho-Kai Huang, Yu-Ting Ng, Shu-Hang Hong, Ji-Hong Yen, Tzu-Chen Lai, Chyong-Huey Lin, Gigin affil: Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, 33382, Taoyuan, Taiwan sug: subj: Magnetic Resonance Imaging Methods Carcinoma, Squamous Cell Cervix Neoplasms Image Processing, Computer Assisted Methods Body Weights and Measures Young Adult Carcinoma, Squamous Cell Pathology Aged, 80 and Over Aged Female Cervix Neoplasms Pathology Adult Carcinoma Pathology Carcinoma Retrospective Design Middle Age Reproducibility of Results Human Funding Source Aged, 80 & over Aged: 65+ years Adult: 19-44 years Middle Aged: 45-64 years Female ab: Objective: To develop and evaluate the performance of U-Net for fully automated localization and segmentation of cervical tumors in magnetic resonance (MR) images and the robustness of extracting apparent diffusion coefficient (ADC) radiomics features.Methods: This retrospective study involved analysis of MR images from 169 patients with cervical cancer stage IB-IVA captured; among them, diffusion-weighted (DW) images from 144 patients were used for training, and another 25 patients were recruited for testing. A U-Net convolutional network was developed to perform automated tumor segmentation. The manually delineated tumor region was used as the ground truth for comparison. Segmentation performance was assessed for various combinations of input sources for training. ADC radiomics were extracted and assessed using Pearson correlation. The reproducibility of the training was also assessed.Results: Combining b0, b1000, and ADC images as a triple-channel input exhibited the highest learning efficacy in the training phase and had the highest accuracy in the testing dataset, with a dice coefficient of 0.82, sensitivity 0.89, and a positive predicted value 0.92. The first-order ADC radiomics parameters were significantly correlated between the manually contoured and fully automated segmentation methods (p < 0.05). Reproducibility between the first and second training iterations was high for the first-order radiomics parameters (intraclass correlation coefficient = 0.70-0.99).Conclusion: U-Net-based deep learning can perform accurate localization and segmentation of cervical cancer in DW MR images. First-order radiomics features extracted from whole tumor volume demonstrate the potential robustness for longitudinal monitoring of tumor responses in broad clinical settings. U-Net-based deep learning can perform accurate localization and segmentation of cervical cancer in DW MR images.Key Points: • U-Net-based deep learning can perform accurate fully automated localization and segmentation of cervical cancer in diffusion-weighted MR images. • Combining b0, b1000, and apparent diffusion coefficient (ADC) images exhibited the highest accuracy in fully automated localization. • First-order radiomics feature extraction from whole tumor volume was robust and could thus potentially be used for longitudinal monitoring of treatment responses. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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