Automated tumor localization and segmentation through hybrid neural network in head and neck cancer

Head and Neck (H&N) cancer accounts for 3% of cancer cases in the United States. Precise tumor segmentation in H&N is of utmost importance for treatment planning and administering personalized treatment dose. We aimed to develop an automatic tumor localization and segmentation method in enhancing the clinical efficiency and ultimately improving treatment outcomes.

In this study, a hybrid neural network (HNN) was developed by integrating object localization and segmentation into a unified framework. It consists of 4 stages: preprocessing, HNN training, object localization and segmentation, and postprocessing. We utilized a dataset consisting of PET and CT images for 48 patients and designed a Hybrid Neural Network (HNN) which consists of YOLOv4 object detection model + U-Net model for image segmentation. YOLOv4 was used to identify regions of interests (ROI), while the U-Net was employed for the precise image segmentation. In our experiments we considered 2 object detection architectures to identify possible tumor regions, namely YOLOv4 and Faster-RCNN. The evaluation metrics for both were evaluated and compared.

We evaluated the performance of 3 model combinations: YOLOv4 + U-Net, Faster-RCNN + U-Net, and U-Net alone. The models were evaluated based on Sensitivity, Specificity, F-Score, and Intersection over Union (IoU). YOLOv4 + U-Net achieved the best values with Sensitivity of 0.89, Specificity of 0.99, F-Score of 0.84, and IoU of 0.72.

A new hybrid neural network (HNN) for fully automatic tumor localization and segmentation was developed and the experimental results. showcased the HNN's impressive performance, indicating its potential to be a valuable H&N tumor segmentation tool.