Detection of Abnormal Regions on Temporal Subtraction Images based on CNN

Recently, visual screening based on CT images become the useful tool in the medical diagnosis. However, due to the increasing data volumes and the computational complexity of the algorithms, image processing technique for the high quality visual screening is still required. To this end, some computer aided diagnosis (CAD) algorithms are proposed. Meanwhile, cancer is a leading cause of death in the world. Detection of cancer region in CT images is the most important task to early detection and early treatment. We design and develop a framework combining convolutional neural networks (CNN) with temporal subtraction techniques-based non-rigid image registration algorithm. However, conventional CNN has the issue that as the layers deeper, global information close to input images is lost. Therefore, we add a skip connection to conventional CNN. By adding a new skip connection, the proposed CNN network maintains the global information without loss of important features of input image. All in all, our proposed method can be built into three main steps; i) pre-processing for image segmentation, ii) image matching for registration, and iii) classification of abnormal regions based on machine learning algorithms. We perform our proposed technique to 25 thoracic MDCT sets and obtain the AUC score of 0.951.