SEGMENT3D: A Web-based Application for Collaborative Segmentation of 3D images used in the Shoot Apical Meristem

TL;DR

SEGMENT3D is a web-based collaborative tool that improves 3D image segmentation accuracy by combining automatic results with user corrections, facilitating plant growth analysis and machine learning data creation.

Contribution

It introduces a novel web application for collaborative 3D image segmentation that integrates automatic and manual methods for improved accuracy.

Findings

Effective merging of segmented tiles via consensus analysis

Supports interactive correction of segmentation results

Applicable to various 3D imaging modalities

Abstract

The quantitative analysis of 3D confocal microscopy images of the shoot apical meristem helps understanding the growth process of some plants. Cell segmentation in these images is crucial for computational plant analysis and many automated methods have been proposed. However, variations in signal intensity across the image mitigate the effectiveness of those approaches with no easy way for user correction. We propose a web-based collaborative 3D image segmentation application, SEGMENT3D, to leverage automatic segmentation results. The image is divided into 3D tiles that can be either segmented interactively from scratch or corrected from a pre-existing segmentation. Individual segmentation results per tile are then automatically merged via consensus analysis and then stitched to complete the segmentation for the entire image stack. SEGMENT3D is a comprehensive application that can be applied to other 3D imaging modalities and general objects. It also provides an easy way to create supervised data to advance segmentation using machine learning models.