# A deep level set method for image segmentation

arXiv: https://arxiv.org/abs/1705.06260 · HTML: https://mlsanity.com/paper/1705.06260/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2017.

## Built on by (most cited first, 4 of 4)

- [A survey on shape-constraint deep learning for medical image segmentation](https://mlsanity.com/paper/2101.07721/) (2021)
- [DiffSeg: A Segmentation Model for Skin Lesions Based on Diffusion Difference](https://mlsanity.com/paper/2404.16474/) (2024)
- [Diff-CL: A Novel Cross Pseudo-Supervision Method for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2503.09408/) (2025)
- [Single Point, Full Mask: Velocity-Guided Level Set Evolution for End-to-End Amodal Segmentation](https://mlsanity.com/paper/2508.01661/) (2025)

## Abstract (arXiv)

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.