# Liver segmentation in CT images using three dimensional to two dimensional fully convolutional network

arXiv: https://arxiv.org/abs/1802.07800 · HTML: https://mlsanity.com/paper/1802.07800/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2018.

## Built on by (most cited first, 3 of 3)

- [Deep Multimodal Fusion of Data with Heterogeneous Dimensionality via Projective Networks](https://mlsanity.com/paper/2402.01311/) (2024)
- [Nuclei Segmentation in Histopathology Images using Deep Learning with Local and Global Views](https://mlsanity.com/paper/2112.03998/) (2021)
- [Brain Tumor Classification by Cascaded Multiscale Multitask Learning Framework Based on Feature Aggregation](https://mlsanity.com/paper/2112.14320/) (2021)

## Abstract (arXiv)

The need for CT scan analysis is growing for pre-diagnosis and therapy of abdominal organs. Automatic organ segmentation of abdominal CT scan can help radiologists analyze the scans faster and segment organ images with fewer errors. However, existing methods are not efficient enough to perform the segmentation process for victims of accidents and emergencies situations. In this paper we propose an efficient liver segmentation with our 3D to 2D fully connected network (3D-2D-FCN). The segmented mask is enhanced by means of conditional random field on the organ's border. Consequently, we segment a target liver in less than a minute with Dice score of 93.52.