Multi-stage Multi-recursive-input Fully Convolutional Networks for Neuronal Boundary Detection

03/24/2017
by   Wei Shen, et al.
0

In the field of connectomics, neuroscientists seek to identify cortical connectivity comprehensively. Neuronal boundary detection from the Electron Microscopy (EM) images is often done to assist the automatic reconstruction of neuronal circuit. But the segmentation of EM images is a challenging problem, as it requires the detector to be able to detect both filament-like thin and blob-like thick membrane, while suppressing the ambiguous intracellular structure. In this paper, we propose multi-stage multi-recursive-input fully convolutional networks to address this problem. The multiple recursive inputs for one stage, i.e., the multiple side outputs with different receptive field sizes learned from the lower stage, provide multi-scale contextual boundary information for the consecutive learning. This design is biologically-plausible, as it likes a human visual system to compare different possible segmentation solutions to address the ambiguous boundary issue. Our multi-stage networks are trained end-to-end. It achieves promising results on two public available EM segmentation datasets, the mouse piriform cortex dataset and the ISBI 2012 EM dataset.

READ FULL TEXT

page 1

page 3

page 7

page 8

research
11/17/2020

Multi Receptive Field Network for Semantic Segmentation

Semantic segmentation is one of the key tasks in computer vision, which ...
research
06/06/2018

A multi-scale pyramid of 3D fully convolutional networks for abdominal multi-organ segmentation

Recent advances in deep learning, like 3D fully convolutional networks (...
research
10/01/2013

Deep and Wide Multiscale Recursive Networks for Robust Image Labeling

Feedforward multilayer networks trained by supervised learning have rece...
research
02/13/2019

Automated Segmentation of the Optic Disk and Cup using Dual-Stage Fully Convolutional Networks

Automated segmentation of the optic cup and disk on retinal fundus image...
research
11/17/2014

Fully Convolutional Neural Networks for Crowd Segmentation

In this paper, we propose a fast fully convolutional neural network (FCN...
research
11/30/2019

EM-NET: Centerline-Aware Mitochondria Segmentation in EM Images via Hierarchical View-Ensemble Convolutional Network

Although deep encoder-decoder networks have achieved astonishing perform...
research
12/12/2016

3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study

This study investigates a 3D and fully convolutional neural network (CNN...

Please sign up or login with your details

Forgot password? Click here to reset