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Kidney cancer is one of the leading causes of cancer death worldwide. The high rates of mortality and occurrences of cancer show the importance of research and the development of means for the early diagnosis. The correct segmentation of kidney becomes an essential step to help with the case’s analysis and degree of severity. Computational methods have been used for this purpose, emerging as alternatives to manual segmentation and as mechanisms to reduce fatigue during diagnosis. Fully convolutional neural networks have presented great prominence in automatic segmentation in medical images. This work aims to build a novel network based on the U-Net architecture, using the concept of Pyramid Pooling Module (PPM) blocks that we call PPM-UNet. We propose this model to force learning features in multiple resolutions during their encoding phase and emphasise differences between similar regions. The hypothesis is that in cases where the texture of the images is very similar, the PPM block will be able to represent information with greater precision. In the experiments, we verified that the PPM-UNet obtained promising results of 0.9466 of Dice for the kidney and 0.9383 of Dice for tumour regions when the method was applied to the Kits19 database.

A PPM-based UNet for Tumour and Kidney Segmentation in CTScans