Manuel Conson1, Laura Cella1, Roberto Pacelli2, Marco Comerci3, Raffaele Liuzzi1, Marco Salvatore4, Mario Quarantelli1. 1. Department of Advanced Biomedical Sciences, Federico II University School of Medicine, Naples, Italy; Institute of Biostructure and Bioimaging, National Research Council (CNR), Naples, Italy. 2. Department of Advanced Biomedical Sciences, Federico II University School of Medicine, Naples, Italy; Institute of Biostructure and Bioimaging, National Research Council (CNR), Naples, Italy. Electronic address: roberto.pacelli@unina.it. 3. Institute of Biostructure and Bioimaging, National Research Council (CNR), Naples, Italy. 4. Department of Advanced Biomedical Sciences, Federico II University School of Medicine, Naples, Italy.
Abstract
PURPOSE: To implement and evaluate a magnetic resonance imaging atlas-based automated segmentation (MRI-ABAS) procedure for cortical and sub-cortical grey matter areas definition, suitable for dose-distribution analyses in brain tumor patients undergoing radiotherapy (RT). PATIENTS AND METHODS: 3T-MRI scans performed before RT in ten brain tumor patients were used. The MRI-ABAS procedure consists of grey matter classification and atlas-based regions of interest definition. The Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm was applied to structures manually delineated by four experts to generate the standard reference. Performance was assessed comparing multiple geometrical metrics (including Dice Similarity Coefficient - DSC). Dosimetric parameters from dose-volume-histograms were also generated and compared. RESULTS: Compared with manual delineation, MRI-ABAS showed excellent reproducibility [median DSCABAS=1 (95% CI, 0.97-1.0) vs. DSCMANUAL=0.90 (0.73-0.98)], acceptable accuracy [DSCABAS=0.81 (0.68-0.94) vs. DSCMANUAL=0.90 (0.76-0.98)], and an overall 90% reduction in delineation time. Dosimetric parameters obtained using MRI-ABAS were comparable with those obtained by manual contouring. CONCLUSIONS: The speed, reproducibility, and robustness of the process make MRI-ABAS a valuable tool for investigating radiation dose-volume effects in non-target brain structures providing additional standardized data without additional time-consuming procedures.
PURPOSE: To implement and evaluate a magnetic resonance imaging atlas-based automated segmentation (MRI-ABAS) procedure for cortical and sub-cortical grey matter areas definition, suitable for dose-distribution analyses in brain tumorpatients undergoing radiotherapy (RT). PATIENTS AND METHODS: 3T-MRI scans performed before RT in ten brain tumorpatients were used. The MRI-ABAS procedure consists of grey matter classification and atlas-based regions of interest definition. The Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm was applied to structures manually delineated by four experts to generate the standard reference. Performance was assessed comparing multiple geometrical metrics (including Dice Similarity Coefficient - DSC). Dosimetric parameters from dose-volume-histograms were also generated and compared. RESULTS: Compared with manual delineation, MRI-ABAS showed excellent reproducibility [median DSCABAS=1 (95% CI, 0.97-1.0) vs. DSCMANUAL=0.90 (0.73-0.98)], acceptable accuracy [DSCABAS=0.81 (0.68-0.94) vs. DSCMANUAL=0.90 (0.76-0.98)], and an overall 90% reduction in delineation time. Dosimetric parameters obtained using MRI-ABAS were comparable with those obtained by manual contouring. CONCLUSIONS: The speed, reproducibility, and robustness of the process make MRI-ABAS a valuable tool for investigating radiation dose-volume effects in non-target brain structures providing additional standardized data without additional time-consuming procedures.
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