ArticlePhysics and imaging in radiation oncology2026
An advanced automated pipeline for brain tumour segmentation on magnetic resonance imaging for gamma knife radiosurgery.
Article in Physics and imaging in radiation oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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 (SD Conclusions: The auto-segmentation pipeline enabled consistent and rapid delineation of multiple intracranial tumours, achieving clinically acceptable performance metrics and efficiency suitable for SRS.
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