An automatic method for segmentation of liver lesions in computed tomography images using deep neural networks

Liver cancer is one of the major causes of death by cancer. The early detection of lesions in the liver provides a better chance of treatment and cure of the disease. Computed tomography (CT) is one of the most used imaging techniques for the detection and diagnosis of liver lesions. However, the manual segmentation of liver and tumors, aside from being time-consuming, can still cause errors and may vary among specialists. Because of this hard work, computer-aided detection (CAD) and computer-aided diagnosis (CADx) systems have been developed to assist specialists in the detection and characterization of lesions in the liver and reduce the required time for diagnosis. The automatic segmentation of these lesions is a complex task since they present variability in contrast, shape, size, and location. In this work, a method to automatically segment liver lesions in CT images is proposed. The proposed method, which presents two deep convolutional neural networks (CNN) models, consists of five main steps: (1) image acquisition, (2) image pre-processing, (3) initial segmentation using RetinaNet, (4) lesion segmentation using U-Net, and (5) segmentation refinement. The proposed method was evaluated using a set of 131 CT images from the LiTS dataset, and the best result obtained a matthews correlation coefficient (MCC) of 83.62%, a sensitivity of 83.86%, a specificity of 99.96%, a Dice coefficient of 82.99%, a volumetric overlap error (VOE) of 27.89%, and a relative volume difference (RVD) of 1.69%. We show in our method that the problem of segmentation of liver lesions in CT images can be efficiently solved through the use of deep CNNs to define the scope of the problem and to precisely segment lesions.