# A deep level set method for image segmentation

## TL;DR

This paper introduces a deep level set method combining FCNs and level set models for improved image segmentation, especially effective with limited labeled data, by integrating the models into training for semi-supervised learning.

## Contribution

The novel integration of level set models into FCN training enables semi-supervised learning and improves segmentation accuracy with limited data.

## Key findings

- Outperforms standalone FCN and level set models on medical images.
- Effective with small training datasets.
- Enhances segmentation accuracy in semi-supervised settings.

## Abstract

This paper proposes a novel image segmentation approachthat integrates fully convolutional networks (FCNs) with a level setmodel. Compared with a FCN, the integrated method can incorporatesmoothing and prior information to achieve an accurate segmentation.Furthermore, different than using the level set model as a post-processingtool, we integrate it into the training phase to fine-tune the FCN. Thisallows the use of unlabeled data during training in a semi-supervisedsetting. Using two types of medical imaging data (liver CT and left ven-tricle MRI data), we show that the integrated method achieves goodperformance even when little training data is available, outperformingthe FCN or the level set model alone.