A General Deep Learning framework for Neuron Instance Segmentation based on Efficient UNet and Morphological Post-processing

TL;DR

This paper introduces an end-to-end deep learning framework utilizing EfficientNet and morphological post-processing for neuron instance segmentation in histological images, reducing annotation effort and improving segmentation accuracy.

Contribution

It proposes a novel approach combining point annotations with synthetic pixel-level masks and an efficient U-Net-based architecture for neuron segmentation.

Findings

Outperforms recent neuron segmentation methods

Synthetic masks effectively replace manual annotations

Morphological post-processing enhances instance separation

Abstract

Recent studies have demonstrated the superiority of deep learning in medical image analysis, especially in cell instance segmentation, a fundamental step for many biological studies. However, the excellent performance of the neural networks requires training on large, unbiased dataset and annotations, which is labor-intensive and expertise-demanding. This paper presents an end-to-end framework to automatically detect and segment NeuN stained neuronal cells on histological images using only point annotations. Unlike traditional nuclei segmentation with point annotation, we propose using point annotation and binary segmentation to synthesize pixel-level annotations. The synthetic masks are used as the ground truth to train the neural network, a U-Net-like architecture with a state-of-the-art network, EfficientNet, as the encoder. Validation results show the superiority of our model compared to other recent methods. In addition, we investigated multiple post-processing schemes and proposed an original strategy to convert the probability map into segmented instances using ultimate erosion and dynamic reconstruction. This approach is easy to configure and outperforms other classical post-processing techniques. This work aims to develop a robust and efficient framework for analyzing neurons using optical microscopic data, which can be used in preclinical biological studies and, more specifically, in the context of neurodegenerative diseases.