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15 October 2019 Liver segmentation and metastases detection in MR images using convolutional neural networks
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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.

© 2019 Society of Photo-Optical Instrumentation Engineers (SPIE) 2329-4302/2019/$28.00 © 2019 SPIE
Mariëlle J. A. Jansen, Hugo J. Kuijf, Maarten Niekel, Wouter B. Veldhuis, Frank J. Wessels, Max A. Viergever, and Josien P. W. Pluim "Liver segmentation and metastases detection in MR images using convolutional neural networks," Journal of Medical Imaging 6(4), 044003 (15 October 2019). https://doi.org/10.1117/1.JMI.6.4.044003
Received: 20 March 2019; Accepted: 17 September 2019; Published: 15 October 2019
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