Machine learning of hierarchical clustering to segment 2D and 3D images

TL;DR

This paper introduces a machine learning-based hierarchical clustering method for segmenting 2D and 3D images, enhancing accuracy and scalability, especially in complex neural tissue EM images.

Contribution

It presents an active learning approach that integrates multiple features across scales for hierarchical segmentation, applicable to high-dimensional and large datasets.

Findings

Improved segmentation accuracy over existing algorithms in EM images

Effective handling of data with arbitrary dimensions

Scalable to very large datasets

Abstract

We aim to improve segmentation through the use of machine learning tools during region agglomeration. We propose an active learning approach for performing hierarchical agglomerative segmentation from superpixels. Our method combines multiple features at all scales of the agglomerative process, works for data with an arbitrary number of dimensions, and scales to very large datasets. We advocate the use of variation of information to measure segmentation accuracy, particularly in 3D electron microscopy (EM) images of neural tissue, and using this metric demonstrate an improvement over competing algorithms in EM and natural images.