FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics

by   Tran Minh Quan, et al.

Electron microscopic connectomics is an ambitious research direction with the goal of studying comprehensive brain connectivity maps by using high-throughput, nano-scale microscopy. One of the main challenges in connectomics research is developing scalable image analysis algorithms that require minimal user intervention. Recently, deep learning has drawn much attention in computer vision because of its exceptional performance in image classification tasks. For this reason, its application to connectomic analyses holds great promise, as well. In this paper, we introduce a novel deep neural network architecture, FusionNet, for the automatic segmentation of neuronal structures in connectomics data. FusionNet leverages the latest advances in machine learning, such as semantic segmentation and residual neural networks, with the novel introduction of summation-based skip connections to allow a much deeper network architecture for a more accurate segmentation. We demonstrate the performance of the proposed method by comparing it with state-of-the-art electron microscopy (EM) segmentation methods from the ISBI EM segmentation challenge. We also show the segmentation results on two different tasks including cell membrane and cell body segmentation and a statistical analysis of cell morphology.


page 1

page 5

page 6

page 7

page 8


Scribble2Label: Scribble-Supervised Cell Segmentation via Self-Generating Pseudo-Labels with Consistency

Segmentation is a fundamental process in microscopic cell image analysis...

Automated image analysis in large-scale cellular electron microscopy: A literature survey

Large-scale electron microscopy (EM) datasets generated using (semi-) au...

QANet - Quality Assurance Network for Microscopy Cell Segmentation

Tools and methods for automatic image segmentation are rapidly developin...

An Iterative Convolutional Neural Network Algorithm Improves Electron Microscopy Image Segmentation

To build the connectomics map of the brain, we developed a new algorithm...

Understanding Important Features of Deep Learning Models for Transmission Electron Microscopy Image Segmentation

Cutting edge deep learning techniques allow for image segmentation with ...

Leveraging Domain Knowledge to improve EM image segmentation with Lifted Multicuts

The throughput of electron microscopes has increased significantly in re...

DVNet: A Memory-Efficient Three-Dimensional CNN for Large-Scale Neurovascular Reconstruction

Maps of brain microarchitecture are important for understanding neurolog...

Code Repositories


Neural network babysteps with tensorflow

view repo