A Deep Learning Based Brain Metastases Segmentation Web Platform for Stereotactic Radiosurgery

Z. Yang, X. Ju, H. Liu, M. Chen, R. D. Timmerman, T. Dan, Z. Wardak, Weiguo Lu, Xuejun Gu

Research output: Contribution to journalArticlepeer-review

Abstract

PURPOSE/OBJECTIVE(S): Stereotactic radiosurgery (SRS) enables the treatment of multiple (> 4) brain metastases (mBMs) patients with reduced neurotoxicity. BMs delineation serves as an essential part in the SRS management involving treatment planning and follow-up. However, manual contouring is highly time-consuming, especially in patients with mBMs. In this study, we propose a deep learning-based BMs segmentation web platform including BMs labeling and database management to improve the efficiency of SRS workflow. MATERIALS/METHODS: The platform is designed with a web client and a back-end server. The web client enables user interactions and data visualization, while the back-end server embeds the core algorithms and maintains a database. All the embedded deep learning models were previously-trained using 271 T1c MRI data with 1058 BMs in total. Once the user imports new MRI data from the client, the server will execute tasks including: 1) CNN-based BMs auto-segmentation 2) Siamese network based false-positive segmentation reduction 3) Affine registration-based BMs labeling, and then send the results back to the web client. The web client displays the received segmentation results, and allows the users to conduct further modifications and export as DICOM RTSTRUCT files. In addition, the server will update the database with the new data. Users can access this database from the web client to retrieve any patient data including image, segmentation and other metadata. RESULTS: The entire process takes about 4 to 5 minutes to complete per new data. The platform can segment mBMs with initial averaged sensitivity of 0.96 and specificity of 0.59. Then after the false-positive reduction, the averaged sensitivity and specificity currently are 0.86 and 0.89. Due to the flexibility of false-positive reduction algorithm, we can balance the sensitivity/specificity as clinical need. The segmentation accuracy is evaluated by the mean (std) Hausdorff distance as 2.75 (0.57) mm, the mean (std) center of mass shift as 1.41 (0.32) mm, and the mean (std) of surface-to-surface distance as 1.01 (0.28) mm, with the BMs labeling accuracy as 100%. CONCLUSION: The deep learning BMs segmentation web platform can accurately segment and label mBMs, and maintain a database for improved data management. It can facilitate the efficiency of the SRS workflow including planning and treatment follow-up, and can be a promising tool for the BMs management.

Original languageEnglish (US)
Pages (from-to)e120
JournalInternational journal of radiation oncology, biology, physics
Volume111
Issue number3
DOIs
StatePublished - Nov 1 2021

ASJC Scopus subject areas

  • Radiation
  • Oncology
  • Radiology Nuclear Medicine and imaging
  • Cancer Research

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