Deep learning vs. atlas-based models for fast auto-segmentation of the masticatory muscles on head and neck CT images.

Radiation Oncology
Wen ChenYi Rong

Abstract

Impaired function of masticatory muscles will lead to trismus. Routine delineation of these muscles during planning may improve dose tracking and facilitate dose reduction resulting in decreased radiation-related trismus. This study aimed to compare a deep learning model with a commercial atlas-based model for fast auto-segmentation of the masticatory muscles on head and neck computed tomography (CT) images. Paired masseter (M), temporalis (T), medial and lateral pterygoid (MP, LP) muscles were manually segmented on 56 CT images. CT images were randomly divided into training (n = 27) and validation (n = 29) cohorts. Two methods were used for automatic delineation of masticatory muscles (MMs): Deep learning auto-segmentation (DLAS) and atlas-based auto-segmentation (ABAS). The automatic algorithms were evaluated using Dice similarity coefficient (DSC), recall, precision, Hausdorff distance (HD), HD95, and mean surface distance (MSD). A consolidated score was calculated by normalizing the metrics against interobserver variability and averaging over all patients. Differences in dose (∆Dose) to MMs for DLAS and ABAS segmentations were assessed. A paired t-test was used to compare the geometric and dosimetric difference between DLAS...Continue Reading

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Citations

Oct 10, 2021·International Journal of Computer Assisted Radiology and Surgery·Yichen PanTianmin Xu

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Software Mentioned

INTContour
DLAS
ABAS
RayStation system
Graph pad
SPSS
Raystation
GraphPad Prism
ANACONDA

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