CNN-based Preprocessing to Optimize Watershed-based Cell Segmentation in 3D Confocal Microscopy Images

TL;DR

This paper introduces a CNN-based preprocessing method combined with watershed postprocessing strategies to improve 3D cell segmentation accuracy in confocal microscopy images, addressing challenges of data volume and intensity variation.

Contribution

It presents a novel 3D segmentation approach that integrates CNN preprocessing with watershed strategies, tailored for multi-instance cell segmentation in microscopy images.

Findings

Proposed method outperforms existing techniques on Arabidopsis thaliana images.

CNN preprocessing enhances segmentation accuracy and robustness.

Three watershed strategies effectively segment cell shapes even with vague boundaries.

Abstract

The quantitative analysis of cellular membranes helps understanding developmental processes at the cellular level. Particularly 3D microscopic image data offers valuable insights into cell dynamics, but error-free automatic segmentation remains challenging due to the huge amount of data generated and strong variations in image intensities. In this paper, we propose a new 3D segmentation approach which combines the discriminative power of convolutional neural networks (CNNs) for preprocessing and investigates the performance of three watershed-based postprocessing strategies (WS), which are well suited to segment object shapes, even when supplied with vague seed and boundary constraints. To leverage the full potential of the watershed algorithm, the multi-instance segmentation problem is initially interpreted as three-class semantic segmentation problem, which in turn is well-suited for the application of CNNs. Using manually annotated 3D confocal microscopy images of Arabidopsis thaliana, we show the superior performance of the proposed method compared to the state of the art.