Kidney Tumor Segmentation and Grade Identification in CT Images

Kidney tumor grade identification by means of feature based classification combined with semantic segmentation of Computed Tomography (CT) images targeting tumor region extraction are the two main contributions of this paper. For all the patients involved in the study three phases of the CT examinations are considered: native, arterial and venous phase. Medical specialists have annotated the images marking the regions in which tumors reside for each of the three phases. On each type of phase deep learning based segmentation has been applied in order to identify the tumor area. The differentiation among various tumor grades by means of deep learning is extremely challenging due to the small amount of available image data and due to the high similarity between different tumor grades. Hence feature based classification is applied to segmented regions in order to distinguish among the four tumor grades. The accuracy of the recognition varies from 60% up to 90% depending on the tumor grade, with first grade tumors being more difficult to be recognized while fourth grade tumors are correctly identified for most patients.