Joint Cell Nuclei Detection and Segmentation in Microscopy Images Using 3D Convolutional Networks

TL;DR

This paper introduces a 3D convolutional neural network that jointly detects and segments cell nuclei in microscopy images, improving accuracy and providing uncertainty estimates, trained on a specialized 3D dataset.

Contribution

The paper presents a novel end-to-end 3D CNN model for simultaneous nuclei detection and segmentation, with a new approach for uncertainty estimation in predictions.

Findings

Significantly improved detection accuracy over existing methods

Enhanced segmentation quality compared to benchmarks

Effective uncertainty estimation correlated with prediction accuracy

Abstract

We propose a 3D convolutional neural network to simultaneously segment and detect cell nuclei in confocal microscopy images. Mirroring the co-dependency of these tasks, our proposed model consists of two serial components: the first part computes a segmentation of cell bodies, while the second module identifies the centers of these cells. Our model is trained end-to-end from scratch on a mouse parotid salivary gland stem cell nuclei dataset comprising 107 image stacks from three independent cell preparations, each containing several hundred individual cell nuclei in 3D. In our experiments, we conduct a thorough evaluation of both detection accuracy and segmentation quality, on two different datasets. The results show that the proposed method provides significantly improved detection and segmentation accuracy compared to state-of-the-art and benchmark algorithms. Finally, we use a previously described test-time drop-out strategy to obtain uncertainty estimates on our predictions and validate these estimates by demonstrating that they are strongly correlated with accuracy.