TY - JOUR
T1 - Optimized Atlas-Based Auto-Segmentation of Bony Structures from Whole-Body Computed Tomography
AU - Gao, Lei
AU - Yusufaly, Tahir I.
AU - Williamson, Casey W.
AU - Mell, Loren K.
N1 - Publisher Copyright:
© 2023 American Society for Radiation Oncology
PY - 2023/9/1
Y1 - 2023/9/1
N2 - Purpose: To develop and test a method for fully automated segmentation of bony structures from whole-body computed tomography (CT) and evaluate its performance compared with manual segmentation. Methods and Materials: We developed a workflow for automatic whole-body bone segmentation using atlas-based segmentation (ABS) method with a postprocessing module (ABSPP) in MIM MAESTRO software. Fifty-two CT scans comprised the training set to build the atlas library, and 29 CT scans comprised the test set. To validate the workflow, we compared Dice similarity coefficient (DSC), mean distance to agreement, and relative volume errors between ABSPP and ABS with no postprocessing (ABSNPP) with manual segmentation as the reference (gold standard). Results: The ABSPP method resulted in significantly improved segmentation accuracy (DSC range, 0.85-0.98) compared with the ABSNPP method (DSC range, 0.55-0.87; P < .001). Mean distance to agreement results also indicated high agreement between ABSPP and manual reference delineations (range, 0.11-1.56 mm), which was significantly improved compared with ABSNPP (range, 1.00-2.34 mm) for the majority of tested bony structures. Relative volume errors were also significantly lower for ABSPP compared with ABSNPP for most bony structures. Conclusions: We developed a fully automated MIM workflow for bony structure segmentation from whole-body CT, which exhibited high accuracy compared with manual delineation. The integrated postprocessing module significantly improved workflow performance.
AB - Purpose: To develop and test a method for fully automated segmentation of bony structures from whole-body computed tomography (CT) and evaluate its performance compared with manual segmentation. Methods and Materials: We developed a workflow for automatic whole-body bone segmentation using atlas-based segmentation (ABS) method with a postprocessing module (ABSPP) in MIM MAESTRO software. Fifty-two CT scans comprised the training set to build the atlas library, and 29 CT scans comprised the test set. To validate the workflow, we compared Dice similarity coefficient (DSC), mean distance to agreement, and relative volume errors between ABSPP and ABS with no postprocessing (ABSNPP) with manual segmentation as the reference (gold standard). Results: The ABSPP method resulted in significantly improved segmentation accuracy (DSC range, 0.85-0.98) compared with the ABSNPP method (DSC range, 0.55-0.87; P < .001). Mean distance to agreement results also indicated high agreement between ABSPP and manual reference delineations (range, 0.11-1.56 mm), which was significantly improved compared with ABSNPP (range, 1.00-2.34 mm) for the majority of tested bony structures. Relative volume errors were also significantly lower for ABSPP compared with ABSNPP for most bony structures. Conclusions: We developed a fully automated MIM workflow for bony structure segmentation from whole-body CT, which exhibited high accuracy compared with manual delineation. The integrated postprocessing module significantly improved workflow performance.
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U2 - 10.1016/j.prro.2023.03.013
DO - 10.1016/j.prro.2023.03.013
M3 - Article
C2 - 37030539
AN - SCOPUS:85160035708
SN - 1879-8500
VL - 13
SP - e442-e450
JO - Practical Radiation Oncology
JF - Practical Radiation Oncology
IS - 5
ER -