  Quantitative cancer image analysis relies on the accurate delineation of
tumours, a very specialised and time-consuming task. For this reason, methods
for automated segmentation of tumours in medical imaging have been extensively
developed in recent years, being Computed Tomography one of the most popular
imaging modalities explored. However, the large amount of 3D voxels in a
typical scan is prohibitive for the entire volume to be analysed at once in
conventional hardware. To overcome this issue, the processes of downsampling
and/or resampling are generally implemented when using traditional
convolutional neural networks in medical imaging. In this paper, we propose a
new methodology that introduces a process of sparsification of the input images
and submanifold sparse convolutional networks as an alternative to
downsampling. As a proof of concept, we applied this new methodology to
Computed Tomography images of renal cancer patients, obtaining performances of
segmentations of kidneys and tumours competitive with previous methods (~84.6%
Dice similarity coefficient), while achieving a significant improvement in
computation time (2-3 min per training epoch).
