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Table 1 Performance results of the threefold cross-validation for tumor and kidney segmentation with a standard 3D U-Net model on 120 CT scans from the KiTS19 challenge

From: MIScnn: a framework for medical image segmentation with convolutional neural networks and deep learning

Metric

Training

Validation

Tversky loss

0.3672

0.4609

Soft Dice similarity coefficient

0.8776

0.8235

Categorical cross-entropy

− 0.8584

− 0.7899

Dice similarity coefficient: background

–

0.9994

Dice similarity coefficient: kidney

–

0.9319

Dice similarity coefficient: tumor

–

0.6750

  1. Each metric is computed between the provided ground truth and our model prediction and then averaged between the three folds
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BMC Medical Imaging

ISSN: 1471-2342

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