FusionNet: A Deep Fully Residual Convolutional Neural Network for Image Segmentation in Connectomics

Tran Minh Quan,Won-Ki Jeong,David Grant Colburn Hildebrand

doi:10.3389/fcomp.2021.613981

Abstract

Cellular-resolution connectomics is an ambitious research direction with the goal of generating comprehensive brain connectivity maps using high-throughput, nano-scale electron microscopy. One of the main challenges in connectomics research is developing scalable image analysis algorithms that require minimal user intervention. Deep learning has provided exceptional performance in image classification tasks in computer vision, leading to a recent explosion in popularity. Similarly, its application to connectomic analyses holds great promise. Here, we introduce a deep neural network architecture, FusionNet, with a focus on its application to accomplish automatic segmentation of neuronal structures in connectomics data. FusionNet combines recent advances in machine learning, such as semantic segmentation and residual neural networks, with summation-based skip connections. This results in a much deeper network architecture and improves segmentation accuracy. We demonstrate the performance of the proposed method by comparing it with several other popular electron microscopy segmentation methods. We further illustrate its flexibility through segmentation results for two different tasks: cell membrane segmentation and cell nucleus segmentation.

Highlights

The brain is considered the most complex organ in the human body
We demonstrate the performance of the proposed deep learning architecture by comparing it with several electron microscopy (EM) segmentation methods listed in the leader board of the ISBI 2012 EM segmentation challenge (Arganda-Carreras et al, 2015)
Each FusionNet can be considered as a V-cycle in the multigrid method (Shapira, 2008) commonly used in numerical analysis, where the contraction in the encoding path is similar to restriction from a fine to a coarse grid, the expansion in the decoding is similar to the prolongation toward the final segmentation, and the skip connections play a role similar to relaxation

Summary

Introduction

The brain is considered the most complex organ in the human body. Despite decades of intense research, our understanding of how its structure relates to its function remains limited (Lichtman and Denk, 2011). Connectomics research seeks to disentangle the complicated neuronal circuits embedded within the brain. This field has gained substantial attention recently thanks to the advent of new serialsection electron microscopy (EM) technologies (Briggman and Bock, 2012; Hayworth et al, 2014; Eberle and Zeidler, 2018; Zheng et al, 2018; Graham et al, 2019). The resolution afforded by EM is sufficient for resolving tiny but important neuronal structures that are often densely packed together, such as dendritic spine necks and synaptic vesicles. These structures are often only tens of nanometers in diameter (Helmstaedter, 2013). Handling and analyzing EM datasets is one of the most challenging problems in connectomics

Methods

Results

Conclusion