Elevate your multiple brain metastases and follow-up workflows. Elements AI Tumor Segmentation, our AI-based tool, automatically processes your patient data to outline supported cranial lesions in under a minute, thereby offering potential time savings in clinical workflows.
Reduce
manual effort
Manual contouring of brain metastases can be labor-intensive, with median contouring times of 7-11 minutes per case1,3. Our advanced deep‑learning algorithm analyzes MRI images and segments supported cranial tumors in seconds.
Segment
with confidence
Process brain metastases lesions with statistically robust volumetric accuracy and high (97.5%) sensitivity with Elements AI Tumor Segmentation, as shown in a recent study for untreated lesions of >0.1cc2. Smaller brain metastases may benefit from algorithm-assisted detection followed by physician refinement2.
Streamline
longitudinal disease management
Integrate Elements AI Tumor Segmentation seamlessly into your multiple brain metastases and follow-up workflows. You can use the auto-segmented lesions in follow-up scans in our Elements Retreatment Review workflow.
The most time-consuming step in planning is identifying and contouring tumors. With Elements AI Tumor Segmentation, we were able to segment 31 brain metastases in under a minute—compared to 22 minutes manually—with strong agreement between manual contours and automated results⁴.
Automated segmentation
in a single click
Elements AI Tumor Segmentation automatically segments supported cranial tumors in under a minute. One study has found that the Brainlab software can automate the segmentation of untreated brain metastases with high sensitivity (97.5% for lesions >0.1cc) with statistically robust volumetric accuracy (Spearman’s ρ − 0.997, p < 0.001)2.
Layer your insights with AI Tumor Segmentation
Xiao Z, et al. A geometric evaluation of an artificial intelligence-based segmentation tool for brain metastases in stereotactic radiosurgery. Journal of Radiosurgery and SBRT. 2026;10(3):175-185.
Madhugiri VS, Prasad D. Early experience with an artificial intelligence-based module for brain metastasis detection and segmentation. J Neurooncol. 2025;171(2):365-372.
Lu SL, Xiao FR, Cheng JC, Yang WC, Cheng YH, Chang YC, Lin JY, Liang CH, Lu JT, Chen YF, Hsu FM. Randomized multi-reader evaluation of automated detection and segmentation of brain tumors in stereotactic radiosurgery with deep neural networks. Neuro Oncol. 2021;23(9):1560-1568. DOI: 10.1093/ neuonc/noab071
The statements made by Dheerendra Prasad during this interview represent their personal opinion and experiences. These statements may not be supported by scientific evidence or peer-review research. For verified information about the device, please refer to the manufacturer’s official documentation or consult clinical guidance.
Shah S et al. Evaluation of an artificial intelligence-based tool for brain metastasis auto-detection: A comparison with physician review, Journal of Radiosurgery and SBRT. 2026;10(3):157-164.