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An advanced automated pipeline for brain tumour segmentation on magnetic resonance imaging for gamma knife
Prabhakar Ramachandran1,2,3, Akshay C Dhargave1, Zachery M Colbert1
1Department of Radiation Oncology, Princess Alexandra Hospital, Woolloongabba, Queensland 4102, Australia.
Background And Purpose:
Accurate delineation of intracranial tumours is crucial for stereotactic radiosurgery (SRS), where target definition directly influences treatment outcome. We developed and clinically integrated an automated multi-tumour segmentation pipeline using three-dimensional nnU-Net models for brain metastases, pituitary adenomas, vestibular schwannomas, and meningiomas.
Materials And Methods:
Four independent, tumour-specific models were trained on T1-weighted Magnetisation-Prepared RApid Gradient Echo Magnetic Resonance Imaging data using the nnU‑Net architecture. For model development, 100 cases per tumour-type (n = 400) were used, and to evaluate the clinical workflow, 25 additional cases per tumour-type (n = 100) were processed prospectively. The performance was assessed using the Dice Similarity Coefficient (DSC), the 95th-percentile Hausdorff Distance (HD95), and the Average Symmetric Surface Distance (ASSD). The pipeline continuously monitored incoming Digital Imaging and Communications in Medicine (DICOM) images using a listener and applied the appropriate tumour-specific segmentation model. It, then, automatically exported the DICOM images and the inferred Radiotherapy Structure-Set to the treatment planning system.
Results:
Among all the tumour-types, vestibular schwannomas achieved the highest performance (DSC: 0.90 ± 0.03; HD95: 0.93 ± 0.34 mm; ASSD: 0.31 ± 0.09 mm) followed by brain metastases (DSC: 0.83 ± 0.08; HD95: 1.33 ± 0.55 mm; ASSD: 0.47 ± 0.19 mm), pituitary adenomas (DSC: 0.81 ± 0.09; HD95: 2.39 ± 1.14 mm; ASSD: 0.78 ± 0.32 mm) and meningiomas (DSC: 0.80 ± 0.11; HD95: 4.46 ± 3.64 mm; ASSD: 1.19 ± 0.80 mm). All tumour-types were segmented with consistent performance (SDDSC < 0.11), and segmentation was completed within two to four minutes per case.
Conclusions:
The auto-segmentation pipeline enabled consistent and rapid delineation of multiple intracranial tumours, achieving clinically acceptable performance metrics and efficiency suitable for SRS.
