  Automated medical image segmentation is a priority research area for
computational methods. In particular, detection of cancerous tumors represents
a current challenge in this area with potential for real-world impact. This
paper describes a method developed in response to the 2019 Kidney Tumor
Segmentation Challenge (KiTS19). Axial computed tomography (CT) scans from 210
kidney cancer patients were used to develop and evaluate this automatic
segmentation method based on a logical ensemble of fully-convolutional network
(FCN) architectures, followed by volumetric validation. Data was pre-processed
using conventional computer vision techniques, thresholding, histogram
equalization, morphological operations, centering, zooming and resizing. Three
binary FCN segmentation models were trained to classify kidney and tumor (2),
and only tumor (1), respectively. Model output images were stacked and
volumetrically validated to produce the final segmentation for each patient
scan. The average F1 score from kidney and tumor pixel classifications was
calculated as 0.6758 using preprocessed images and annotations; although
restoring to the original image format reduced this score. It remains to be
seen how this compares to other solutions.
