A Novel Approach for 3D Renal Segmentation Using a Modified GAN Model and Texture Analysis

This paper introduces a novel framework for renal segmentation of kidney transplant patients suspected of renal rejection. The framework applies image processing techniques for texture analysis utilizing a modified Pix 2 Pix GAN model to capture the varied kidney shapes in the dataset of 36 subject volumes acquired using BOLD MRI scans. For this problem, we built a framework that analyzes the kidney texture based on four steps: (i) calculate the average CDF for each case to map CDF values to their corresponding intensities for contrast enhancement (ii) extract the region of interest for the kidney to focus on the kidney structure, (iii) calculate the probability maps using the histograms of the contours for the kidney and non-kidney regions, (iv) Create a common-layer across the dataset using the masks by calculating the average of the pixel values of the images to accommodate the shared information within the mask images. Finally, stack the three layers to have the RGB channels contain relevant information about the renal dataset as input for the modified GAN model. The proposed framework achieved an average accuracy and Dice Similarity Coefficient: $90.3 \%$, and $83.1 \%$, respectively. The framework’s primary results underscore its efficiency in providing segmentation for renal diagnosis.