Automatic Kidney Lesion Detection for CT Images Using Morphological Cascade Convolutional Neural Networks

The CT scan image is one of the most useful tools for diagnosing and locating lesions in the kidney. It can provide precise information about the location and size of lesions in many medical applications. Manual and traditional medical testings are labor-consuming and time-costing. Nowadays, detecting lesions in CT automatically is an integral assignment to the paramount importance of clinical diagnosis. Computer-aided diagnosis (CAD) is needed to develop and improve medical testing efficiency. However, it is still a tremendous challenge to the extant low precision and incomplete detection algorithm. In this paper, we proposed a lesion detection tool using multi intersection over union (IOU) threshold based on morphological cascade convolutional neural networks (CNNs). For improving the detection of small lesions (1–5 mm) and increasing the stableness of network, we proposed two morphology convolution layers and modified feature pyramid networks (FPNs) in the faster RCNN and combined four IOU threshold cascade RCNNs. In this lesion detection task, the modified CNN was trained in pytorch framework. The experiments were conducted in CT kidney images of DeepLesion that are published by hospitals’ picture archiving and communication systems (PACSs). Finally, our method achieved AP of 0.840 and AUC of 0.871, and the results demonstrated that our proposed detector is an outstanding tool for detecting lesions in CT and outperformed in the data set.