# Unsupervised Microvascular Image Segmentation Using an Active Contours Mimicking Neural Network

## TL;DR

This paper introduces an unsupervised deep learning approach for blood vessel segmentation in microscopy images, inspired by active contours, which outperforms supervised methods especially under varying imaging conditions.

## Contribution

The paper presents a novel unsupervised segmentation method using active contours-inspired loss and pooling layers, addressing challenges faced by supervised models under different imaging conditions.

## Key findings

- Outperforms supervised methods on labeled datasets
- Effective under different imaging conditions
- Provides open-source code and PyTorch implementations

## Abstract

The task of blood vessel segmentation in microscopy images is crucial for many diagnostic and research applications. However, vessels can look vastly different, depending on the transient imaging conditions, and collecting data for supervised training is laborious. We present a novel deep learning method for unsupervised segmentation of blood vessels. The method is inspired by the field of active contours and we introduce a new loss term, which is based on the morphological Active Contours Without Edges (ACWE) optimization method. The role of the morphological operators is played by novel pooling layers that are incorporated to the network's architecture. We demonstrate the challenges that are faced by previous supervised learning solutions, when the imaging conditions shift. Our unsupervised method is able to outperform such previous methods in both the labeled dataset, and when applied to similar but different datasets. Our code, as well as efficient PyTorch reimplementations of the baseline methods VesselNN and DeepVess is available on GitHub - https://github.com/shirgur/UMIS.