Liver segmentation and metastases detection in MR images using convolutional neural networks

Mariëlle J.A. Jansen (Corresponding author), Hugo J. Kuijf, Maarten Niekel, Wouter B. Veldhuis, Frank J. Wessels, Max A. Viergever, Josien P.W. Pluim

Research output: Contribution to journalArticleAcademicpeer-review

6 Citations (Scopus)
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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 languageEnglish
Article number044003
Number of pages10
JournalJournal of Medical Imaging
Volume6
Issue number4
DOIs
Publication statusPublished - 1 Oct 2019

Keywords

  • deep learning
  • detection
  • diffusion weighted MRI
  • dynamic contrast-enhanced MRI
  • liver
  • segmentation

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