Browsing by Author "Jain, Rishab"

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Deep learning auto-segmentation of cervical skeletal muscle for sarcopenia analysis in patients with head and neck cancer
(2022-07-28) Naser, Mohamed A.; Wahid, Kareem A.; Grossberg, Aaron J.; Olson, Brennan; Jain, Rishab; El-Habashy, Dina; Dede, Cem; Salama, Vivian; Abobakr, Moamen; Mohamed, Abdallah S.R.; He, Renjie; Jaskari, Joel; Sahlsten, Jaakko; Kaski, Kimmo; Fuller, Clifton D.
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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.
Muscle and adipose tissue segmentations at the third cervical vertebral level in patients with head and neck cancer
(2022-08-02) Wahid, Kareem A.; Olson, Brennan; Jain, Rishab; Grossberg, Aaron J.; El-Habashy, Dina; Dede, Cem; Salama, Vivian; Abobakr, Moamen; Mohamed, Abdallah S.R.; He, Renjie; Jaskari, Joel; Sahlsten, Jaakko; Kaski, Kimmo; Fuller, Clifton D.; Naser, Mohamed A.
Data Article
The accurate determination of sarcopenia is critical for disease management in patients with head and neck cancer (HNC). Quantitative determination of sarcopenia is currently dependent on manually-generated segmentations of skeletal muscle derived from computed tomography (CT) cross-sectional imaging. This has prompted the increasing utilization of machine learning models for automated sarcopenia determination. However, extant datasets currently do not provide the necessary manually-generated skeletal muscle segmentations at the C3 vertebral level needed for building these models. In this data descriptor, a set of 394 HNC patients were selected from The Cancer Imaging Archive, and their skeletal muscle and adipose tissue was manually segmented at the C3 vertebral level using sliceOmatic. Subsequently, using publicly disseminated Python scripts, we generated corresponding segmentations files in Neuroimaging Informatics Technology Initiative format. In addition to segmentation data, additional clinical demographic data germane to body composition analysis have been retrospectively collected for these patients. These data are a valuable resource for studying sarcopenia and body composition analysis in patients with HNC.