UNSUPERVISED SEMANTIC SEGMENTATION OF KIDNEYS USING RADIAL TRANSFORM SAMPLING ON LIMITED IMAGES

Abstract

Efficient training of supervised deep learning models for semantic segmentation requires a massive volume of annotated data. In this paper, we propose an unsupervised semantic segmentation method through the application of a radial transform method in the polar coordinate system to unannotated images. This method generates radial transformed images up to the number of pixels in the input image. Each generated image corresponds to a pixel in the original image, which is a spatial representation of the selected pixel with respect to other pixels, in the polar coordinate system. A dimension reduction method, such as a convolutional autoencoder (CAE), can extract features of these representations for clustering and later, labeling the original image by mapping the labels back to the original image. The advantage of the proposed radial transform technique is that it generates a massive number of training images by sampling pixels in the polar coordinate system from a very limited number of original images in the Cartesian coordinate system. The proposed approach achieved 88.20% accuracy in pixel-level segmentation of left kidney, right kidney, and non-kidney pixels in contrast-enhanced computed tomography (CT) images.