A Topological Nomenclature for 3D Shape Analysis in Connectomics

TL;DR

This paper introduces a topological nomenclature system for analyzing complex 3D biological shapes like neurons and mitochondria, improving shape categorization and manipulation in connectomics.

Contribution

It develops a novel topological naming scheme and graph-based representation for biological objects, enhancing shape analysis and retrieval in neuroscience.

Findings

Graphs align with expert perception

Topological features outperform existing descriptors

Method improves shape retrieval and decomposition

Abstract

One of the essential tasks in connectomics is the morphology analysis of neurons and organelles like mitochondria to shed light on their biological properties. However, these biological objects often have tangled parts or complex branching patterns, which make it hard to abstract, categorize, and manipulate their morphology. In this paper, we develop a novel topological nomenclature system to name these objects like the appellation for chemical compounds to promote neuroscience analysis based on their skeletal structures. We first convert the volumetric representation into the topology-preserving reduced graph to untangle the objects. Next, we develop nomenclature rules for pyramidal neurons and mitochondria from the reduced graph and finally learn the feature embedding for shape manipulation. In ablation studies, we quantitatively show that graphs generated by our proposed method align with the perception of experts. On 3D shape retrieval and decomposition tasks, we qualitatively demonstrate that the encoded topological nomenclature features achieve better results than state-of-the-art shape descriptors. To advance neuroscience, we will release a 3D segmentation dataset of mitochondria and pyramidal neurons reconstructed from a 100um cube electron microscopy volume with their reduced graph and topological nomenclature annotations. Code is publicly available at https://github.com/donglaiw/ibexHelper.