  Primary tumors have a high likelihood of developing metastases in the liver
and early detection of these metastases is crucial for patient outcome. We
propose a method based on convolutional neural networks (CNN) to detect liver
metastases. First, the liver was automatically segmented using the six phases
of abdominal dynamic contrast enhanced (DCE) MR images. Next, DCE-MR and
diffusion weighted (DW) MR images are used for metastases detection within the
liver mask. The liver segmentations have a median Dice similarity coefficient
of 0.95 compared with manual annotations. The metastases detection method has a
sensitivity of 99.8% with a median of 2 false positives per image. The
combination of the two MR sequences in a dual pathway network is proven
valuable for the detection of liver metastases. In conclusion, a high quality
liver segmentation can be obtained in which we can successfully detect liver
metastases.
