  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.
