# Topology preserving Image segmentation using the iterative convolution-thresholding method

arXiv: https://arxiv.org/abs/2503.17792 · HTML: https://mlsanity.com/paper/2503.17792/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 43 references that are in the map.
- First posted on arXiv 22 Mar 2025.

## Builds on (most cited first, 11 of 11)

- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Image Segmentation Using Deep Learning: A Survey](https://mlsanity.com/paper/2001.05566/) (2020)
- [Brain Tumor Segmentation with Deep Neural Networks](https://mlsanity.com/paper/1505.03540/) (2015)
- [Deep Convolutional Neural Networks with Spatial Regularization, Volume and Star-shape Priori for Image Segmentation](https://mlsanity.com/paper/2002.03989/) (2020)
- [An efficient iterative thresholding method for image segmentation](https://mlsanity.com/paper/1608.01431/) (2016)
- [Minimal Paths for Tubular Structure Segmentation with Coherence Penalty and Adaptive Anisotropy](https://mlsanity.com/paper/1809.07987/) (2018)
- [The iterative convolution-thresholding method (ICTM) for image segmentation](https://mlsanity.com/paper/1904.10917/) (2019)
- [A Binary Characterization Method for Shape Convexity and Applications](https://mlsanity.com/paper/2203.11395/) (2022)
- [An efficient threshold dynamics method for topology optimization for fluids](https://mlsanity.com/paper/1812.09437/) (2018)
- [A Characteristic Function-based Algorithm for Geodesic Active Contours](https://mlsanity.com/paper/2007.00525/) (2020)
- [A prediction-correction based iterative convolution-thresholding method for topology optimization of heat transfer problems](https://mlsanity.com/paper/2401.01069/) (2024)

## Abstract (arXiv)

Variational models are widely used in image segmentation, with various models designed to address different types of images by optimizing specific objective functionals. However, traditional segmentation models primarily focus on the visual attributes of the image, often neglecting the topological properties of the target objects. This limitation can lead to segmentation results that deviate from the ground truth, particularly in images with complex topological structures. In this paper, we introduce a topology-preserving constraint into the iterative convolution-thresholding method (ICTM), resulting in the topology-preserving ICTM (TP-ICTM). Extensive experiments demonstrate that, by explicitly preserving the topological properties of target objects-such as connectivity-the proposed algorithm achieves enhanced accuracy and robustness, particularly in images with intricate structures or noise.