U-net architecture with embedded Inception-ResNet-v2 image encoding modules for automatic segmentation of organs-at-risk in head and neck cancer radiation therapy based on computed tomography scans

Abstract

Purpose. The purpose of this study was to utilize a deep learning model with an advanced inception module to automatically contour critical organs on the computed tomography (CT) scans of head and neck cancer patients who underwent radiation therapy treatment and interpret the clinical suitability of the model results through activation mapping. Materials and methods. This study included 25 critical organs that were delineated by expert radiation oncologists. Contoured medical images of 964 patients were sourced from a publicly available TCIA database. The proportion of training, validation, and testing samples for deep learning model development was 65%, 25%, and 10% respectively. The CT scans and segmentation masks were augmented with shift, scale, and rotate transformations. Additionally, medical images were pre-processed using contrast limited adaptive histogram equalization to enhance soft tissue contrast while contours were subjected to morphological operations to ensure their structural integrity. The segmentation model was based on the U-Net architecture with embedded Inception-ResNet-v2 blocks and was trained over 100 epochs with a batch size of 32 and an adaptive learning rate optimizer. The loss function combined the Jaccard Index and binary cross entropy. The model performance was evaluated with Dice Score, Jaccard Index, and Hausdorff Distances. The interpretability of the model was analyzed with guided gradient-weighted class activation mapping. Results. The Dice Score, Jaccard Index, and mean Hausdorff Distance averaged over all structures and patients were 0.82 ± 0.10, 0.71 ± 0.10, and 1.51 ± 1.17 mm respectively on the testing data sets. The Dice Scores for 86.4% of compared structures was within range or better than published interobserver variability derived from multi-institutional studies. The average model training time was 8 h per anatomical structure. The full segmentation of head and neck anatomy by the trained network required only 6.8 s per patient. Conclusions. High accuracy obtained on a large, multi-institutional data set, short segmentation time and clinically-realistic prediction reasoning make the model proposed in this work a feasible solution for head and neck CT scan segmentation in a clinical environment.