Kidney and Kidney Tumour Segmentation in CT Images

TL;DR

This paper presents a CNN-based 3D U-Net model for automatic segmentation of kidneys and tumors in CT images, achieving promising accuracy on the KiTS21 dataset to assist medical diagnosis.

Contribution

Developed a novel 3D U-Net segmentation approach with pre-processing and patch-wise input analysis for kidney and tumor delineation in CT images.

Findings

Achieved an average Dice score of 0.6374 on test data.

Model attained a kidney Dice score of 0.8034.

Tumor Dice score was 0.4713.

Abstract

Automatic segmentation of kidney and kidney tumour in Computed Tomography (CT) images is essential, as it uses less time as compared to the current gold standard of manual segmentation. However, many hospitals are still reliant on manual study and segmentation of CT images by medical practitioners because of its higher accuracy. Thus, this study focuses on the development of an approach for automatic kidney and kidney tumour segmentation in contrast-enhanced CT images. A method based on Convolutional Neural Network (CNN) was proposed, where a 3D U-Net segmentation model was developed and trained to delineate the kidney and kidney tumour from CT scans. Each CT image was pre-processed before inputting to the CNN, and the effect of down-sampled and patch-wise input images on the model performance was analysed. The proposed method was evaluated on the publicly available 2021 Kidney and Kidney Tumour Segmentation Challenge (KiTS21) dataset. The method with the best performing model recorded an average training Dice score of 0.6129, with the kidney and kidney tumour Dice scores of 0.7923 and 0.4344, respectively. For testing, the model obtained a kidney Dice score of 0.8034, and a kidney tumour Dice score of 0.4713, with an average Dice score of 0.6374.