Evaluating Skellytour for Automated Skeleton Segmentation from Whole-Body CT Images.

Abstract

Purpose To construct and evaluate the performance of a machine learning model for bone segmentation using whole-body CT images. Materials and Methods In this retrospective study, whole-body CT scans (from June 2010 to January 2018) from 90 patients (mean age, 61 years ± 9 [SD]; 45 male, 45 female) with multiple myeloma were manually segmented using 60 labels and subsegmented into cortical and trabecular bone. Segmentations were verified by board-certified radiology and nuclear medicine physicians. The impacts of isotropy, resolution, multiple labeling schemes, and postprocessing were assessed. Model performance was assessed on internal and external test datasets (362 scans) and benchmarked against the TotalSegmentator segmentation model. Performance was assessed using Dice similarity coefficient (DSC), normalized surface distance (NSD), and manual inspection. Results Skellytour achieved consistently high segmentation performance on the internal dataset (DSC: 0.94, NSD: 0.99) and two external datasets (DSC: 0.94, 0.96; NSD: 0.999, 1.0), outperforming TotalSegmentator on the first two datasets. Subsegmentation performance was also high (DSC: 0.95, NSD: 0.995). Skellytour produced finely detailed segmentations, even in low-density bones. Conclusion The study demonstrates that Skellytour is an accurate and generalizable bone segmentation and subsegmentation model for CT data; it is available as a Python package via GitHub <i>(https://github.com/cpwardell/Skellytour)</i>. <b>Keywords:</b> CT, Informatics, Skeletal-Axial, Demineralization-Bone, Comparative Studies, Segmentation, Supervised Learning, Convolutional Neural Network (CNN) <i>Supplemental material is available for this article.</i> Published under a CC BY 4.0 license. See also commentary by Khosravi and Rouzrokh in this issue.