Liver segmentation and metastases detection in MR images using convolutional neural networks
Jansen, Mariëlle J A, Kuijf, Hugo J, Niekel, Maarten, Veldhuis, Wouter B, Wessels, Frank J, Viergever, Max A, Pluim, Josien P W
DOI: https://doi.org/10.1117/1.JMI.6.4.044003
Journal of Medical Imaging 6 (4),
Abstract
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 to detect liver metastases. First, the liver is automatically segmented using the six phases of abdominal dynamic contrast-enhanced (DCE) MR images. Next, DCE-MR and diffusion weighted 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 two 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.