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

TL;DR

This paper presents a CNN-based method for automatic liver segmentation and metastases detection in MR images, achieving high accuracy and sensitivity, which could improve early diagnosis and treatment planning.

Contribution

The study introduces a dual pathway CNN that combines DCE-MR and DW-MR images for improved detection of liver metastases, with high segmentation accuracy.

Findings

Median Dice coefficient of 0.95 for liver segmentation

Sensitivity of 99.8% for metastases detection

Approximately 2 false positives per image

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 (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.