A new method of using a convolutional neural network (CNN) to perform automatic tumor segmentation from two-dimensional transaxial slices of positron emission tomography (PET) images of high-risk primary prostate cancer patients is introduced.

We compare three different methods including (1) usual image segmentation with a CNN whose continuous output is converted to binary labels with a constant threshold, (2) our new technique of choosing separate thresholds for each image PET slice with a CNN to label the pixels directly from the PET slices, and (3) the combination of the two former methods based on using the second CNN to choose the optimal thresholds to convert the output of the first CNN. The CNNs are trained and tested multiple times by using a data set of 864 slices from the PET images of 78 prostate cancer patients.

According to our results, the Dice scores computed from the predictions of the second method are statistically higher than those of the typical image segmentation (p-value<0.002).

The new method of choosing unique thresholds to convert the pixels of the PET slices directly into binary tumor masks is not only faster and more computationally efficient but also yields better results.