Comparison of skeletal segmentation by deep learning-based and atlas-based segmentation in prostate cancer patients.

Objective: We aimed to compare the deep learning-based (VSBONE BSI) and atlas-based (BONENAVI) segmentation accuracy that have been developed to measure the bone scan index based on skeletal segmentation.

Methods: We retrospectively conducted bone scans for 383 patients with prostate cancer. These patients were divided into two groups: 208 patients were injected with Tc-hydroxymethylene diphosphonate processed by VSBONE BSI, and 175 patients were injected with Tc-methylene diphosphonate processed by BONENAVI. Three observers classified the skeletal segmentations as either a "Match" or "Mismatch" in the following regions: the skull, cervical vertebrae, thoracic vertebrae, lumbar vertebrae, pelvis, sacrum, humerus, rib, sternum, clavicle, scapula, and femur. Segmentation error was defined if two or more observers selected "Mismatch" in the same region. We calculated the segmentation error rate according to each administration group and evaluated the presence of hot spots suspected bone metastases in "Mismatch" regions. Multivariate logistic regression analysis was used to determine the association between segmentation error and variables like age, uptake time, total counts, extent of disease, and gamma cameras.

Results: The regions of "Mismatch" were more common in the long tube bones for VSBONE BSI and in the pelvis and axial skeletons for BONENAVI. Segmentation error was observed in 49 cases (23.6%) with VSBONE BSI and 58 cases (33.1%) with BONENAVI. VSBONE BSI tended that "Mismatch" regions contained hot spots suspected of bone metastases in patients with multiple bone metastases and showed that patients with higher extent of disease (odds ratio = 8.34) were associated with segmentation error in multivariate logistic regression analysis.

Conclusions: VSBONE BSI has a potential to be higher segmentation accuracy compared with BONENAVI. However, the segmentation error in VSBONE BSI occurred dependent on bone metastases burden. We need to be careful when evaluating multiple bone metastases using VSBONE BSI.