Harnessing Multiple Level Features to Improve Segmentation Performance of Deep Neural Network: A Case Study in Magnetic Resonance Imaging of Nasopharyngeal Cancer

The lesion area of cancer presents complex physiological structures, posing significant challenges for accurate segmentation, which is required in radiotherapy or chemo-radiotherapy. The existing general-purpose segmentation models have made significant progress, but their segmentation performance remains unsatisfactory in specific cancers. To enhance the segmentation performance of nasopharyngeal carcinoma (NPC) MRI images, we develop a segmentation model by incorporating skip connections and receptive blocks within the deep convolutional neural network. We curate a collection of NPC images and evaluate the segmentation performance of the proposed model in comparison with the ground truth segmented images. The similarity between the two types of images is quantified by the Dice Similarity Coefficient (DSC) score, intersection over Union (IoU) score, recall value, and precision value. The mean DSC score was increased to 0.91, and the mean IoU score was improved to 0.84 in the segmentation of our curated NPC images. Our proposed method not only shows excellent performance in segmenting NPC, but also displays its generalization capacity on three public medical image datasets including skin cancer, lung, and chest image databases. The proposed model can leverage multi-layer feature extraction to improve the accuracy of tumor region segmentation. The improved generalization ability suggests that harnessing multi-level features in deep neural networks can improve segmentation performance.