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.
Original language | English |
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Article number | 044003 |
Number of pages | 10 |
Journal | Journal of Medical Imaging |
Volume | 6 |
Issue number | 4 |
DOIs | |
Publication status | Published - 1 Oct 2019 |
Keywords
- deep learning
- detection
- diffusion weighted MRI
- dynamic contrast-enhanced MRI
- liver
- segmentation