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Automated medical image segmentation is a priority research area forcomputational methods. In particular, detection of cancerous tumors representsa current challenge in this area with potential for real-world impact. Thispaper describes a method developed in response to the 2019 Kidney TumorSegmentation Challenge (KiTS19). Axial computed tomography (CT) scans from 210kidney cancer patients were used to develop and evaluate this automaticsegmentation method based on a logical ensemble of fully-convolutional network(FCN) architectures, followed by volumetric validation. Data was pre-processedusing conventional computer vision techniques, thresholding, histogramequalization, morphological operations, centering, zooming and resizing. Threebinary FCN segmentation models were trained to classify kidney and tumor (2),and only tumor (1), respectively. Model output images were stacked andvolumetrically validated to produce the final segmentation for each patientscan. The average F1 score from kidney and tumor pixel classifications wascalculated as 0.6758 using preprocessed images and annotations; althoughrestoring to the original image format reduced this score. It remains to beseen how this compares to other solutions.

Kidney and Kidney Tumor Segmentation using a Logical Ensemble of U-nets with Volumetric Validation