Automatic Segmentation of Organs-at-Risk in Thoracic Computed Tomography Images Using Ensembled U-Net InceptionV3 Model

Abstract

The objective of this article is to automatically segment organs at risk (OARs) for thoracic radiology in computed tomography (CT) scan images. The OARs in the thoracic anatomical region during the radiotherapy treatment are mainly the neighbouring organs such as the esophagus, heart, trachea, and aorta. The dataset of 40 patients was used in the proposed work by splitting it into three parts: training, validation, and test sets. The implementation was performed on the Google Colab Pro+ framework with 52 GB of RAM and 265 GB of storage space. An ensemble model was evolved for the automatic segmentation of four OARs in thoracic CT images. U-Net with InceptionV3 as the backbone was used, and different hyperparameters were used during the training of the model. The proposed model achieved precise accuracy for OARs segmentation with an average dice coefficient of 0.9413, Hausdorff value of 0.1838, sensitivity of 0.9783, and specificity of 0.9895 on the Test dataset. An ensembled U-Net InceptionV3 model has been proposed, improving the segmentation results compared with the state-of-the-art techniques such as U-Net, ResNet, Vgg16, etc. The results of the experiments revealed that the proposed model effectively improved the performance of the segmentation of the esophagus, heart, trachea, and aorta.