# Variational multichannel multiclass segmentation using unsupervised lifting with CNNs

arXiv: https://arxiv.org/abs/2302.02214 · HTML: https://mlsanity.com/paper/2302.02214/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 4 Feb 2023.

## Builds on (most cited first, 6 of 6)

- [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017)
- [Mumford-Shah Loss Functional for Image Segmentation with Deep Learning](https://mlsanity.com/paper/1904.02872/) (2019)
- [A Partially Learned Algorithm for Joint Photoacoustic Reconstruction and Segmentation](https://mlsanity.com/paper/1906.07499/) (2019)
- [A primal-dual hybrid gradient method for non-linear operators with applications to MRI](https://mlsanity.com/paper/1309.5032/) (2013)
- [Model-based learning of local image features for unsupervised texture segmentation](https://mlsanity.com/paper/1708.00180/) (2017)
- [Lifting-based variational multiclass segmentation algorithm: design, convergence analysis, and implementation with applications in medical imaging](https://mlsanity.com/paper/2202.04680/) (2022)

## Abstract (arXiv)

We propose an unsupervised image segmentation approach, that combines a variational energy functional and deep convolutional neural networks. The variational part is based on a recent multichannel multiphase Chan-Vese model, which is capable to extract useful information from multiple input images simultaneously. We implement a flexible multiclass segmentation method that divides a given image into K different regions. We use convolutional neural networks (CNNs) targeting a pre-decomposition of the image. By subsequently minimising the segmentation functional, the final segmentation is obtained in a fully unsupervised manner. Special emphasis is given to the extraction of informative feature maps serving as a starting point for the segmentation. The initial results indicate that the proposed method is able to decompose and segment the different regions of various types of images, such as texture and medical images and compare its performance with another multiphase segmentation method.