A web‐based brain metastases segmentation and labeling platform for stereotactic radiosurgery. Issue 8 (23rd May 2020)

• Record Type: Journal Article
• Authors: Yang, Zi; Liu, Hui; Liu, Yan; Stojadinovic, Strahinja; Timmerman, Robert; Nedzi, Lucien; Dan, Tu; Wardak, Zabi; Lu, Weiguo; Gu, Xuejun
• Is Part Of: Medical physics. Volume 47:Issue 8(2020)
• Page Start: 3263
• Page End: 3276
• Publication Date: 2020-05-23
• Subjects: brain metastases -- stereotactic radiosurgery -- segmentation; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.14201
• Languages: English
• ISSNs: 0094-2405

Abstract

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.