A Study on Kidney Segmentation Techniques Using DNN Models

Kidney stone disease has become one of the risks for humans all over the world and most of people at the initial stage do not notice stone formation in kidney as disease and it slowly damages the organ. Present estimation of people suffering from this disease approximately 30 million. Currently, detection of kidney stone is carried out physically by doctors on medical scanned images. But this procedure is cumbersome and instinctive as it relies on the physician. The domain of Artificial Intelligence (AI) within the last decade has experienced a rapid development and has attained power to simulate human-like thinking in various situations. When the Deep Neural Networks (DNNs) are trained with huge dataset and high computational resources it can bring out great outcomes. The different imaging techniques available for detecting kidney diseases are CT scanning, Ultrasound imaging and X-rays. But the most preferred method is computed tomography (CT) imaging because of its imaging capability with better spatial resolution and high-level contrast. Image segmentation can be referred as a crucial step in digital processing of image. The main objective of segmentation is to improve and modify the image representation into some form which is more vital and simpler to be analyzed. So, to get an detailed overview of existing segmentation techniques, we have carried-out a survey of kidney segmentation and classification techniques, their benefits, drawbacks and challenges available.