Zhongjian Ju1, Wen Guo2,3, Shanshan Gu1, Jin Zhou2, Wei Yang1, Xiaohu Cong1, Xiangkun Dai1, Hong Quan2, Jie Liu4, Baolin Qu5, Guocai Liu6. 1. Department of Radiation Oncology, The First Medical Center, People's Liberation Army General Hospital, No. 28 Fuxing Road, Haidian District, Beijing, 100853, China. 2. School of Physics Science and Technology, Wuhan University, No. 299, Bayi Road, Luojiashan Street, Wuhan, 430072, China. 3. Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, No. 1 Jianshe East Road, Zhengzhou, 450003, China. 4. Beijing Eastraycloud Technology Inc. Chengdu R&D Center.Suite, 1405-1406,Building Guannan Shangyu,NO.1,Xingguang Road,Wuhou District, Chengdu, 610094, China. 5. Department of Radiation Oncology, The First Medical Center, People's Liberation Army General Hospital, No. 28 Fuxing Road, Haidian District, Beijing, 100853, China. qubl6212@sina.com. 6. College of Electrical and Information Engineering, Hunan University, Changsha, 410082, China.
Abstract
BACKGROUND: It is very important to accurately delineate the CTV on the patient's three-dimensional CT image in the radiotherapy process. Limited to the scarcity of clinical samples and the difficulty of automatic delineation, the research of automatic delineation of cervical cancer CTV based on CT images for new patients is slow. This study aimed to assess the value of Dense-Fully Connected Convolution Network (Dense V-Net) in predicting Clinical Target Volume (CTV) pre-delineation in cervical cancer patients for radiotherapy. METHODS: In this study, we used Dense V-Net, a dense and fully connected convolutional network with suitable feature learning in small samples to automatically pre-delineate the CTV of cervical cancer patients based on computed tomography (CT) images and then we assessed the outcome. The CT data of 133 patients with stage IB and IIA postoperative cervical cancer with a comparable delineation scope was enrolled in this study. One hundred and thirteen patients were randomly designated as the training set to adjust the model parameters. Twenty cases were used as the test set to assess the network performance. The 8 most representative parameters were also used to assess the pre-sketching accuracy from 3 aspects: sketching similarity, sketching offset, and sketching volume difference. RESULTS: The results presented that the DSC, DC/mm, HD/cm, MAD/mm, ∆V, SI, IncI and JD of CTV were 0.82 ± 0.03, 4.28 ± 2.35, 1.86 ± 0.48, 2.52 ± 0.40, 0.09 ± 0.05, 0.84 ± 0.04, 0.80 ± 0.05, and 0.30 ± 0.04, respectively, and the results were greater than those with a single network. CONCLUSIONS: Dense V-Net can correctly predict CTV pre-delineation of cervical cancer patients and can be applied in clinical practice after completing simple modifications.
BACKGROUND: It is very important to accurately delineate the CTV on the patient's three-dimensional CT image in the radiotherapy process. Limited to the scarcity of clinical samples and the difficulty of automatic delineation, the research of automatic delineation of cervical cancer CTV based on CT images for new patients is slow. This study aimed to assess the value of Dense-Fully Connected Convolution Network (Dense V-Net) in predicting Clinical Target Volume (CTV) pre-delineation in cervical cancerpatients for radiotherapy. METHODS: In this study, we used Dense V-Net, a dense and fully connected convolutional network with suitable feature learning in small samples to automatically pre-delineate the CTV of cervical cancerpatients based on computed tomography (CT) images and then we assessed the outcome. The CT data of 133 patients with stage IB and IIA postoperative cervical cancer with a comparable delineation scope was enrolled in this study. One hundred and thirteen patients were randomly designated as the training set to adjust the model parameters. Twenty cases were used as the test set to assess the network performance. The 8 most representative parameters were also used to assess the pre-sketching accuracy from 3 aspects: sketching similarity, sketching offset, and sketching volume difference. RESULTS: The results presented that the DSC, DC/mm, HD/cm, MAD/mm, ∆V, SI, IncI and JD of CTV were 0.82 ± 0.03, 4.28 ± 2.35, 1.86 ± 0.48, 2.52 ± 0.40, 0.09 ± 0.05, 0.84 ± 0.04, 0.80 ± 0.05, and 0.30 ± 0.04, respectively, and the results were greater than those with a single network. CONCLUSIONS: Dense V-Net can correctly predict CTV pre-delineation of cervical cancerpatients and can be applied in clinical practice after completing simple modifications.
Authors: B Berthon; M Evans; C Marshall; N Palaniappan; N Cole; V Jayaprakasam; T Rackley; E Spezi Journal: Radiother Oncol Date: 2017-01-23 Impact factor: 6.280
Authors: Eli Gibson; Francesco Giganti; Yipeng Hu; Ester Bonmati; Steve Bandula; Kurinchi Gurusamy; Brian Davidson; Stephen P Pereira; Matthew J Clarkson; Dean C Barratt Journal: IEEE Trans Med Imaging Date: 2018-02-14 Impact factor: 10.048