Jie Xue1, Bao Wang2, Yang Ming3, Xuejun Liu1, Zekun Jiang4, Chengwei Wang5, Xiyu Liu6, Ligang Chen3, Jianhua Qu1, Shangchen Xu7,8, Xuqun Tang9, Ying Mao9, Yingchao Liu7,8, Dengwang Li4. 1. School of Business, Shandong Normal University, Jinan, China. 2. Department of Radiology, Qilu Hospital of Shandong University, Jinan, China. 3. Department of Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China. 4. Shandong Key Laboratory of Medical Physics and Image Processing, School of Physics and Electronics, Shandong Normal University, Jinan, China. 5. Department of Neurosurgery, the Second Hospital of Shandong University, Jinan, China. 6. Department of Radiology, the Affiliated Hospital of Qingdao University Medical College, Qingdao, China. 7. Department of Neurosurgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China. 8. Department of Neurosurgery, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, China. 9. Department of Neurosurgery, Huashan Hospital Affiliated to Fudan University, Shanghai, China.
Abstract
BACKGROUND: Three-dimensional T1 magnetization prepared rapid acquisition gradient echo (3D-T1-MPRAGE) is preferred in detecting brain metastases (BM) among MRI. We developed an automatic deep learning-based detection and segmentation method for BM (named BMDS net) on 3D-T1-MPRAGE images and evaluated its performance. METHODS: The BMDS net is a cascaded 3D fully convolution network (FCN) to automatically detect and segment BM. In total, 1652 patients with 3D-T1-MPRAGE images from 3 hospitals (n = 1201, 231, and 220, respectively) were retrospectively included. Manual segmentations were obtained by a neuroradiologist and a radiation oncologist in a consensus reading in 3D-T1-MPRAGE images. Sensitivity, specificity, and dice ratio of the segmentation were evaluated. Specificity and sensitivity measure the fractions of relevant segmented voxels. Dice ratio was used to quantitatively measure the overlap between automatic and manual segmentation results. Paired samples t-tests and analysis of variance were employed for statistical analysis. RESULTS: The BMDS net can detect all BM, providing a detection result with an accuracy of 100%. Automatic segmentations correlated strongly with manual segmentations through 4-fold cross-validation of the dataset with 1201 patients: the sensitivity was 0.96 ± 0.03 (range, 0.84-0.99), the specificity was 0.99 ± 0.0002 (range, 0.99-1.00), and the dice ratio was 0.85 ± 0.08 (range, 0.62-0.95) for total tumor volume. Similar performances on the other 2 datasets also demonstrate the robustness of BMDS net in correctly detecting and segmenting BM in various settings. CONCLUSIONS: The BMDS net yields accurate detection and segmentation of BM automatically and could assist stereotactic radiotherapy management for diagnosis, therapy planning, and follow-up.
BACKGROUND: Three-dimensional T1 magnetization prepared rapid acquisition gradient echo (3D-T1-MPRAGE) is preferred in detecting brain metastases (BM) among MRI. We developed an automatic deep learning-based detection and segmentation method for BM (named BMDS net) on 3D-T1-MPRAGE images and evaluated its performance. METHODS: The BMDS net is a cascaded 3D fully convolution network (FCN) to automatically detect and segment BM. In total, 1652 patients with 3D-T1-MPRAGE images from 3 hospitals (n = 1201, 231, and 220, respectively) were retrospectively included. Manual segmentations were obtained by a neuroradiologist and a radiation oncologist in a consensus reading in 3D-T1-MPRAGE images. Sensitivity, specificity, and dice ratio of the segmentation were evaluated. Specificity and sensitivity measure the fractions of relevant segmented voxels. Dice ratio was used to quantitatively measure the overlap between automatic and manual segmentation results. Paired samples t-tests and analysis of variance were employed for statistical analysis. RESULTS: The BMDS net can detect all BM, providing a detection result with an accuracy of 100%. Automatic segmentations correlated strongly with manual segmentations through 4-fold cross-validation of the dataset with 1201 patients: the sensitivity was 0.96 ± 0.03 (range, 0.84-0.99), the specificity was 0.99 ± 0.0002 (range, 0.99-1.00), and the dice ratio was 0.85 ± 0.08 (range, 0.62-0.95) for total tumor volume. Similar performances on the other 2 datasets also demonstrate the robustness of BMDS net in correctly detecting and segmenting BM in various settings. CONCLUSIONS: The BMDS net yields accurate detection and segmentation of BM automatically and could assist stereotactic radiotherapy management for diagnosis, therapy planning, and follow-up.
Authors: Mohammad Havaei; Axel Davy; David Warde-Farley; Antoine Biard; Aaron Courville; Yoshua Bengio; Chris Pal; Pierre-Marc Jodoin; Hugo Larochelle Journal: Med Image Anal Date: 2016-05-19 Impact factor: 8.545
Authors: Y W Lui; P D Chang; G Zaharchuk; D P Barboriak; A E Flanders; M Wintermark; C P Hess; C G Filippi Journal: AJNR Am J Neuroradiol Date: 2020-07-30 Impact factor: 3.825
Authors: Antonio Di Ieva; Carlo Russo; Sidong Liu; Anne Jian; Michael Y Bai; Yi Qian; John S Magnussen Journal: Neuroradiology Date: 2021-01-26 Impact factor: 2.804
Authors: Shuncong Wang; Yuanbo Feng; Lei Chen; Jie Yu; Chantal Van Ongeval; Guy Bormans; Yue Li; Yicheng Ni Journal: Am J Cancer Res Date: 2022-09-15 Impact factor: 5.942
Authors: Jeffrey D Rudie; David A Weiss; John B Colby; Andreas M Rauschecker; Benjamin Laguna; Steve Braunstein; Leo P Sugrue; Christopher P Hess; Javier E Villanueva-Meyer Journal: Radiol Artif Intell Date: 2021-03-10
Authors: Youngjin Yoo; Pascal Ceccaldi; Siqi Liu; Thomas J Re; Yue Cao; James M Balter; Eli Gibson Journal: J Med Imaging (Bellingham) Date: 2021-05-22