Zi Yang1,2, Hui Liu1, Yan Liu3, Strahinja Stojadinovic1, Robert Timmerman1, Lucien Nedzi1, Tu Dan1, Zabi Wardak1, Weiguo Lu1, Xuejun Gu1. 1. Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA. 2. Biomedical Engineering Graduate Program, The University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA. 3. College of Electrical Engineering, Sichuan University, Chengdu, 610065, China.
Abstract
PURPOSE: Stereotactic radiosurgery (SRS) has become a standard of care for patients' with brain metastases (BMs). However, the manual multiple BMs delineation can be time-consuming and could create an efficiency bottleneck in SRS workflow. There is a clinical need for automatic delineation and quantitative evaluation tools. In this study, building on our previous developed deep learning-based segmentation algorithms, we developed a web-based automated BMs segmentation and labeling platform to assist the SRS clinical workflow. METHOD: This platform was developed based on the Django framework, including a web client and a back-end server. The web client enables interactions as database access, data import, and image viewing. The server performs the segmentation and labeling tasks including: skull stripping; deep learning-based BMs segmentation; and affine registration-based BMs labeling. Additionally, the client can display BMs contours with corresponding atlas labels, and allows further postprocessing tasks including: (a) adjusting window levels; (b) displaying/hiding specific contours; (c) removing false-positive contours; (d) exporting contours as DICOM RTStruct files; etc. RESULTS: We evaluated this platform on 10 clinical cases with BMs number varied from 12-81 per case. The overall operation took about 4-5 min per patient. The segmentation accuracy was evaluated between the manual contour and automatic segmentation with several metrics. The averaged center of mass shift was 1.55 ± 0.36 mm, the Hausdorff distance was 2.98 ± 0.63 mm, the mean of surface-to-surface distance (SSD) was 1.06 ± 0.31 mm, and the standard deviation of SSD was 0.80 ± 0.16 mm. In addition, the initial averaged false-positive over union (FPoU) and false-negative rate (FNR) were 0.43 ± 0.19 and 0.15 ± 0.10 respectively. After case-specific postprocessing, the averaged FPoU and FNR were 0.19 ± 0.10 and 0.15 ± 0.10 respectively. CONCLUSION: The evaluated web-based BMs segmentation and labeling platform can substantially improve the clinical efficiency compared to manual contouring. This platform can be a useful tool for assisting SRS treatment planning and treatment follow-up.
PURPOSE: Stereotactic radiosurgery (SRS) has become a standard of care for patients' with brain metastases (BMs). However, the manual multiple BMs delineation can be time-consuming and could create an efficiency bottleneck in SRS workflow. There is a clinical need for automatic delineation and quantitative evaluation tools. In this study, building on our previous developed deep learning-based segmentation algorithms, we developed a web-based automated BMs segmentation and labeling platform to assist the SRS clinical workflow. METHOD: This platform was developed based on the Django framework, including a web client and a back-end server. The web client enables interactions as database access, data import, and image viewing. The server performs the segmentation and labeling tasks including: skull stripping; deep learning-based BMs segmentation; and affine registration-based BMs labeling. Additionally, the client can display BMs contours with corresponding atlas labels, and allows further postprocessing tasks including: (a) adjusting window levels; (b) displaying/hiding specific contours; (c) removing false-positive contours; (d) exporting contours as DICOM RTStruct files; etc. RESULTS: We evaluated this platform on 10 clinical cases with BMs number varied from 12-81 per case. The overall operation took about 4-5 min per patient. The segmentation accuracy was evaluated between the manual contour and automatic segmentation with several metrics. The averaged center of mass shift was 1.55 ± 0.36 mm, the Hausdorff distance was 2.98 ± 0.63 mm, the mean of surface-to-surface distance (SSD) was 1.06 ± 0.31 mm, and the standard deviation of SSD was 0.80 ± 0.16 mm. In addition, the initial averaged false-positive over union (FPoU) and false-negative rate (FNR) were 0.43 ± 0.19 and 0.15 ± 0.10 respectively. After case-specific postprocessing, the averaged FPoU and FNR were 0.19 ± 0.10 and 0.15 ± 0.10 respectively. CONCLUSION: The evaluated web-based BMs segmentation and labeling platform can substantially improve the clinical efficiency compared to manual contouring. This platform can be a useful tool for assisting SRS treatment planning and treatment follow-up.
Authors: Jack L Lancaster; Diana Tordesillas-Gutiérrez; Michael Martinez; Felipe Salinas; Alan Evans; Karl Zilles; John C Mazziotta; Peter T Fox Journal: Hum Brain Mapp Date: 2007-11 Impact factor: 5.038
Authors: E Shaw; C Scott; L Souhami; R Dinapoli; R Kline; J Loeffler; N Farnan Journal: Int J Radiat Oncol Biol Phys Date: 2000-05-01 Impact factor: 7.038
Authors: Bjoern H Menze; Heinz Handels; Mauricio Reyes; Oskar Maier; Janina von der Gablentz; Levin Ḧani; Mattias P Heinrich; Matthias Liebrand; Stefan Winzeck; Abdul Basit; Paul Bentley; Liang Chen; Daan Christiaens; Francis Dutil; Karl Egger; Chaolu Feng; Ben Glocker; Michael Götz; Tom Haeck; Hanna-Leena Halme; Mohammad Havaei; Khan M Iftekharuddin; Pierre-Marc Jodoin; Konstantinos Kamnitsas; Elias Kellner; Antti Korvenoja; Hugo Larochelle; Christian Ledig; Jia-Hong Lee; Frederik Maes; Qaiser Mahmood; Klaus H Maier-Hein; Richard McKinley; John Muschelli; Chris Pal; Linmin Pei; Janaki Raman Rangarajan; Syed M S Reza; David Robben; Daniel Rueckert; Eero Salli; Paul Suetens; Ching-Wei Wang; Matthias Wilms; Jan S Kirschke; Ulrike M Kr Amer; Thomas F Münte; Peter Schramm; Roland Wiest Journal: Med Image Anal Date: 2016-07-21 Impact factor: 8.545
Authors: Yan Liu; Strahinja Stojadinovic; Brian Hrycushko; Zabi Wardak; Steven Lau; Weiguo Lu; Yulong Yan; Steve B Jiang; Xin Zhen; Robert Timmerman; Lucien Nedzi; Xuejun Gu Journal: PLoS One Date: 2017-10-06 Impact factor: 3.240