Evaluation of Deep Learning Topcoders Method for Neuron Individualization in Histological Macaque Brain Section

TL;DR

This paper presents a novel pipeline that synthesizes pixel-level labels from point annotations and employs an ensemble deep learning method to accurately individualize neurons in histological macaque brain sections, addressing data annotation challenges.

Contribution

The study introduces a new pipeline for generating pixel-level labels from point annotations and applies an ensemble deep learning approach for neuron segmentation in neurological tissue.

Findings

Achieved 0.93 average detection accuracy in neuron segmentation

Successfully segmented neuronal cells at object and pixel levels

Demonstrated effectiveness on histological macaque brain data

Abstract

Cell individualization has a vital role in digital pathology image analysis. Deep Learning is considered as an efficient tool for instance segmentation tasks, including cell individualization. However, the precision of the Deep Learning model relies on massive unbiased dataset and manual pixel-level annotations, which is labor intensive. Moreover, most applications of Deep Learning have been developed for processing oncological data. To overcome these challenges, i) we established a pipeline to synthesize pixel-level labels with only point annotations provided; ii) we tested an ensemble Deep Learning algorithm to perform cell individualization on neurological data. Results suggest that the proposed method successfully segments neuronal cells in both object-level and pixel-level, with an average detection accuracy of 0.93.