  Neural networks represent a field of research that can quickly achieve very
good results in the field of medical image segmentation using a GPU. A possible
way to achieve good results without GPUs are random forests. For this purpose,
two random forest approaches were compared with a state-of-the-art deep
convolutional neural network. To make the comparison the PhC-C2DH-U373 and the
retinal imaging datasets were used. The evaluation showed that the deep
convolutional neutral network achieved the best results. However, one of the
random forest approaches also achieved a similar high performance. Our results
indicate that random forest approaches are a good alternative to deep
convolutional neural networks and, thus, allow the usage of medical image
segmentation without a GPU.
