Segmentation and Identification of Small Intestinal Lesions Based on U-Net with Batch Normalization and Virtual Sample

Because of the important diagnostic value, computed tomography (CT) has been widely used in clinic. However, there are still some limitations in CT examination of small intestinal lesions. At present, the diagnosis of these lesions mainly depends on doctors' experience in CT image so that there would be some problems such as high labor cost, low efficiency and misdiagnosis risk. Therefore, a segmentation and identification method for small intestinal lesions based on U-Net with batch normalization (BN) and virtual sample, named as U-NetBV, is proposed in this paper. BN can help to improve the performance of the network and virtual sample can increase the number of samples which is usually small in CT image. Through U-NetBV, these lesions can be identified and segmented automatically. The experimental results show that the proposed U-NetBV has a promising prospect in the field of segmentation and identification of small intestinal lesions.