A web-based brain metastases segmentation and labeling platform for stereotactic radiosurgery.

Abstract

Stereotactic radiosurgery (SRS) has becomea standard of care for patients' withbrain metastases (BMs). However, the manualmultipleBMs delineation can be time-consuming and could create an efficiencybottleneck in SRS workflow. There is a clinical needfor automatic delineation and quantitative evaluationtools. 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. This platformwas 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, andimage 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 12-81per case. The overall operation took about 4-5min per patient. The segmentation accuracywas evaluated between the manual contour and automatic segmentation withseveral metrics.The averaged center of mass shift was 1.55±0.36mm, theHausdorff distance was 2.98±0.63mm, the mean of surface-to-surface distance (SSD) was 1.06±0.31mm, andthe standard deviation of SSD was 0.80±0.16mm. 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. The evaluated web-based BMs segmentation and labeling platform can substantially improve the clinical efficiencycompared to manual contouring. This platform can be a useful tool for assisting SRS treatment planning and treatment follow-up.