Small Tumor Segmentation Using Multi-Slices in CT Image

Abstract

This paper proposes a novel method to improve auto detection of small tumors in segmentation of computed tomography (CT) images. For medical images, there is a high bias in a dataset that most parts of images are non-tumor. For recent tumor auto detection, convolutional neural network (CNN) is used. In these methods, small tumors are hardly detected because it is difficult to detect from only single slice image. In the proposed method, sequential multi-slices including a target slice image are used as an input patch. For training CNN model, a loss function called Multi-Slices (MS) loss which is calculated with several annotations of sequential multi-slices is proposed. By using multi-slices for segmentation of CT images, the segmentation model gets to recognize a small tumor which exists in sequential several slices. Our proposed method using multi-slices improves 5.9% in DICE coefficient compared with a conventional method using a single slice. This paper presents that the proposed method is effective for detection of small tumors.