Benign and Malignant Tumor Segmentation on Thorax Computed Tomography Images

Medical imaging techniques are frequently used for tumor detection and diagnosis. Segmentation of tumor from medical images is a popular field of study. To this end, various deep neural network based methods are introduced for segmenting tumor regions. Within the scope of this study, we first collected a data set consisting of thorax CT (Computed Tomography) images with two class labels as benign and malignant with the help of chest radiologists and chest disease clinicians. Then, we trained four different deep neural network based segmentation methods, Mask R-CNN, YOLACT, SOLOV2, and U-Net, and compared their accuracies. Finally, we conducted experiments to show which CT image channels are more useful for segmentation. Among the tested methods, it was observed that the YOLACT algorithm returned the best results in classifying tumors and U-Net yielded the best segmentation masks.