Cervical Vertebrae Skeletal Muscle Auto Segmentation for Sarcopenia Analysis Using Pre-Therapy CT in Head and Neck Cancer Patients

Abstract

<h3>Purpose/Objective(s)</h3> Sarcopenia is a well-known prognostic factor in head and neck cancer (HNC) patients. Sarcopenia status in HNC patients can be determined using the skeletal muscle (SM) index (SMI) as calculated from the cross-sectional area (CSA) of C3 cervical vertebrae musculature. However, current approaches to generate the C3 SM segmentations rely on human judgment which is time consuming and prone to inter-observer variability. We hypothesize that deep learning (DL) can be used to develop a fully automated approach to segment SM at the C3 vertebral level for SMI calculations which can be used for subsequent body composition and sarcopenia assessment. <h3>Materials/Methods</h3> 390 HNC patients from a public database with corresponding contrast-enhanced computed tomography (CT) scans were utilized. Patients were split into training (n=300) and testing (n=90) sets. Ground truth (GT) single-slice SM segmentations at the C3 vertebral level were manually generated. A multi-stage DL pipeline was developed using the MONAI package, where a 3D ResUnet model auto-segmented the C3 section, the middle slice of the segmented C3 section was auto-selected, and a 2D ResUnet model auto-segmented the auto-selected slice. Both the 3D and 2D approaches trained five sub-models (5-fold cross-validation) and combined sub-model predictions on the test set using majority vote ensembling. Model performance was determined using the Dice similarity coefficient (DSC). Additionally, we quantified the absolute difference between the slice locations of the C3 mid-section predicted by the 3D model and the single-slice CT GT image. Furthermore, we compared the GT C3 vertebral level SM CSA and the corresponding SMI to the 2D model predictions. Finally, using these SMI values coupled to previously established sarcopenia stratification cutoffs, we performed a Kaplan Meier analysis on the entire dataset to determine associations to overall survival. <h3>Results</h3> Mean test set DSC of the 3D and 2D models were 0.96 and 0.95, respectively. The mid-slices of the 3D model segmentations were within 2 mm distance of the GT slices for 78% of the test patients. The CSA derived from the 2D model predictions using the auto-selected slice from the 3D model and corresponding SMI values were highly correlated to the CSA (r=0.98) and SMI (r=0.96) derived from the GT segmentations. Kaplan Meier analysis revealed that the SMI values derived from auto-segmented SM could stratify patients for overall survival in males (log-rank p < 0.05) but not females (log-rank p > 0.05), with results that were consistent with SMI values derived from the GT segmentations. <h3>Conclusion</h3> Using open-source toolkits and public datasets, we developed a high-performance multi-stage DL auto-segmentation for SM at the C3 vertebrae. This work is a vital first step towards fully automated workflows for sarcopenia-related clinical decision making.