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

arXiv: https://arxiv.org/abs/1910.06635 · HTML: https://mlsanity.com/paper/1910.06635/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2019.

## Built on by (most cited first, 6 of 6)

- [Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations](https://mlsanity.com/paper/2103.06384/) (2021)
- [Regularized directional representations for medical image registration](https://mlsanity.com/paper/2111.15509/) (2021)
- [Influence of uncertainty estimation techniques on false-positive reduction in liver lesion detection](https://mlsanity.com/paper/2206.10911/) (2022)
- [End-to-end Deformable Attention Graph Neural Network for Single-view Liver Mesh Reconstruction](https://mlsanity.com/paper/2303.07432/) (2023)
- [AI Radiologist: Revolutionizing Liver Tissue Segmentation with Convolutional Neural Networks and a Clinician-Friendly GUI](https://mlsanity.com/paper/2406.07688/) (2024)
- [Automated surgical planning with nnU-Net: delineation of the anatomy in hepatobiliary phase MRI](https://mlsanity.com/paper/2508.14133/) (2025)

## Abstract (arXiv)

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.