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

## TL;DR

This paper introduces a multi-stage, multi-recursive-input fully convolutional network for neuronal boundary detection in electron microscopy images, effectively handling multi-scale features and ambiguous boundaries to improve segmentation accuracy.

## Contribution

It proposes a novel multi-stage network with recursive multi-scale inputs, inspired by biological visual systems, trained end-to-end for improved neuronal boundary detection.

## Key findings

- Achieves promising results on EM segmentation datasets.
- Effectively detects both thin and thick neuronal membranes.
- Outperforms existing methods in boundary detection accuracy.

## Abstract

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.