# Automatic 3D Liver Segmentation Using Sparse Representation of Global and Local Image Information via Level Set Formulation

arXiv: https://arxiv.org/abs/1508.01521 · HTML: https://mlsanity.com/paper/1508.01521/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2015.

## Built on by (most cited first, 2 of 2)

- [Domain Generalization on Medical Imaging Classification using Episodic Training with Task Augmentation](https://mlsanity.com/paper/2106.06908/) (2021)
- [SAR-U-Net: squeeze-and-excitation block and atrous spatial pyramid pooling based residual U-Net for automatic liver segmentation in Computed Tomography](https://mlsanity.com/paper/2103.06419/) (2021)

## Abstract (arXiv)

In this paper, a novel framework for automated liver segmentation via a level set formulation is presented. A sparse representation of both global (region-based) and local (voxel-wise) image information is embedded in a level set formulation to innovate a new cost function. Two dictionaries are build: A region-based feature dictionary and a voxel-wise dictionary. These dictionaries are learned, using the K-SVD method, from a public database of liver segmentation challenge (MICCAI-SLiver07). The learned dictionaries provide prior knowledge to the level set formulation. For the quantitative evaluation, the proposed method is evaluated using the testing data of MICCAI-SLiver07 database. The results are evaluated using different metric scores computed by the challenge organizers. The experimental results demonstrate the superiority of the proposed framework by achieving the highest segmentation accuracy (79.6%) in comparison to the state-of-the-art methods.