# Reimagining Image Segmentation using Active Contour: From Chan Vese Algorithm into a Proposal Novel Functional Loss Framework

## TL;DR

This paper reimagines image segmentation by extending the Chan-Vese algorithm into a novel functional loss framework using active contours, with empirical validation on standard datasets and implementation in PyTorch.

## Contribution

It introduces a new functional segmentation loss based on active contours, integrating the Chan-Vese algorithm into modern deep learning frameworks.

## Key findings

- The proposed loss improves segmentation accuracy on benchmark datasets.
- Comparison shows advantages over traditional loss functions.
- Implementation details enable reproducibility and further research.

## Abstract

In this paper, we present a comprehensive study and analysis of the Chan-Vese algorithm for image segmentation. We employ a discretized scheme derived from the empirical study of the Chan-Vese model's functional energy and its partial differential equation based on its level set function. We provide a proof of the results and an implementation using MATLAB. Leveraging modern computer vision methodologies, we propose a functional segmentation loss based on active contours, utilizing pytorch.nn.ModuleLoss and a level set based on the Chan-Vese algorithm. We compare our results with common computer vision segmentation datasets and evaluate the performance of classical loss functions against our proposed method. All code and materials used are available at https://github.com/gguzzy/chan_vese_functional_loss.