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Michael Ehmann

As seen in: Nature
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Articles by Michael Ehmann

Feasibility of automated AI-based contouring and stable radiomic feature assessment by HyperSight-CBCT Imaging for adaptive high-precision radiotherapy of prostate cancer

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By Ralf Schmidt, Matthias F. Froelich, Stefan O. Schoenberg, Michael Ehmann, Daniel Buergy, Jens Fleckenstein, Frank A. Giordano, Judit Boda-Heggemann, Constantin Dreher, Dion Bajerski, Alicia S. Bicu, Victor Siefert, Miriam Eckl, Marvin Willam, Florian Stieler
| Nature Verified
Abstract This study evaluated segmentation accuracy, efficiency, and radiomic feature stability for manual (MD), artificial intelligence-based (AI), and hybrid (MD + AI) contouring of pelvic organs on planning CT (pCT) and HyperSight cone-beam CT (hCBCT) for adaptive radiotherapy. Dice similarity and 95th percentile Hausdorff distance (HD95) quantified segmentation agreement, while radiomic feature stability was assessed using the concordance correlation coefficient (CCC).
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