Tumor Segmentation in Patients with Head and Neck Cancers Using Deep Learning Based-on Multi-modality PET/CT Images

Abstract

Segmentation of head and neck cancer (HNC) primary tumors onmedical images is an essential, yet labor-intensive, aspect of radiotherapy.PET/CT imaging offers a unique ability to capture metabolic and anatomicinformation, which is invaluable for tumor detection and border definition. Anautomatic segmentation tool that could leverage the dual streams of informationfrom PET and CT imaging simultaneously, could substantially propel HNCradiotherapy workflows forward. Herein, we leverage a multi-institutionalPET/CT dataset of 201 HNC patients, as part of the MICCAI segmentationchallenge, to develop novel deep learning architectures for primary tumor auto-segmentation for HNC patients. We preprocess PET/CT images by normalizingintensities and applying data augmentation to mitigate overfitting. Both 2D and3D convolutional neural networks based on the U-net architecture, which wereoptimized with a model loss function based on a combination of dice similaritycoefficient (DSC) and binary cross entropy, were implemented. The median andmean DSC values comparing the predicted tumor segmentation with the groundtruth achieved by the models through 5-fold cross validation are 0.79 and 0.69for the 3D model, respectively, and 0.79 and 0.67 for the 2D model, respec-tively. These promising results show potential to provide an automatic, accurate,and efficient approach for primary tumor auto-segmentation to improve theclinical practice of HNC treatment.

Keywords: PET; CT; Tumor segmentation; Head and neck cancer; Deep learning; Auto-contouring.

Fig. 1.

An illustration of the 3D U-net model architecture.

Fig. 2.

An illustration of the sample-weight used to scale the BCE loss function for each image per patient based on the cross-sectional area of the tumor. The small squares show overlays of the tumor ground truth contours (red) and the cross-sectional images. Scale of the background grayscale color is the BCE weights.

Fig. 3.

The loss and DSC values as a function of epochs obtained during the 3D (A) and (B) and the 2D (C) and (D) model training.

Fig. 4.

Boxplots of the DSC distribution for the 5 test data sets (Set 1 to Set 5) used for the 3D and 2D segmentation model cross validation. The DSC mean values are given in the boxes and the lines inside the box refer to the DSC median values.

Fig. 5.

2D axial examples of overlays of the ground truth segmentations (red) and predicted segmentations (green) and CT images (first and third columns) and PET images (second and forth columns) with different 3D volumetric DSC values given at the right top.