Deep Learning Auto-Segmentation of Cervical Skeletal Muscle for Sarcopenia Analysis in Patients With Head and Neck Cancer

Authors

Mohamed A Naser
Kareem A Wahid
Aaron J Grossberg
Brennan Olson
Rishab Jain
Dina El-Habashy
Cem Dede
Vivian Salama
Moamen Abobakr
Abdallah S R Mohamed
Renjie He
Joel Jaskari
Jaakko Sahlsten
Kimmo Kaski
Clifton D Fuller

Publication Date

1-1-2022

Journal

Frontiers in Oncology

Abstract

Background/purpose: Sarcopenia is a prognostic factor in patients with head and neck cancer (HNC). Sarcopenia can be determined using the skeletal muscle index (SMI) calculated from cervical neck skeletal muscle (SM) segmentations. However, SM segmentation requires manual input, which is time-consuming and variable. Therefore, we developed a fully-automated approach to segment cervical vertebra SM.

Materials/methods: 390 HNC patients with contrast-enhanced CT scans were utilized (300-training, 90-testing). Ground-truth single-slice SM segmentations at the C3 vertebra were manually generated. A multi-stage deep learning pipeline was developed, where a 3D ResUNet auto-segmented the C3 section (33 mm window), the middle slice of the section was auto-selected, and a 2D ResUNet 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 primarily determined using the Dice similarity coefficient (DSC). Predicted SMI was calculated using the auto-segmented SM cross-sectional area. Finally, using established SMI cutoffs, we performed a Kaplan-Meier analysis to determine associations with overall survival.

Results: Mean test set DSC of the 3D and 2D models were 0.96 and 0.95, respectively. Predicted SMI had high correlation to the ground-truth SMI in males and females (r>0.96). Predicted SMI stratified patients for overall survival in males (log-rank p = 0.01) but not females (log-rank p = 0.07), consistent with ground-truth SMI.

Conclusion: We developed a high-performance, multi-stage, fully-automated approach to segment cervical vertebra SM. Our study is an essential step towards fully-automated sarcopenia-related decision-making in patients with HNC.

Keywords

auto-segmentation, deep learning, skeletal muscle index, head and neck cancer, sarcopenia

DOI

10.3389/fonc.2022.930432

PMID

35965493

PMCID

PMC9366009

PubMedCentral® Posted Date

7-28-2022

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes