From Instance Segmentation to 3D Growth Trajectory Reconstruction in Planktonic Foraminifera

TL;DR

This paper introduces an automated pipeline combining instance segmentation and chamber ordering to reconstruct 3D growth trajectories of planktonic foraminifera from CT scans, reducing manual effort and enabling large-scale ecological analysis.

Contribution

It presents the first fully automated, end-to-end method for 3D growth trajectory reconstruction in foraminifera, integrating computer vision techniques with a novel chamber ordering algorithm.

Findings

Pipeline reduces manual segmentation effort significantly.

Chamber ordering remains accurate despite segmentation under-segmentation.

Robustness demonstrated on expert-annotated datasets.

Abstract

Planktonic foraminifera, marine protists characterized by their intricate chambered shells, serve as valuable indicators of past and present environmental conditions. Understanding their chamber growth trajectory provides crucial insights into organismal development and ecological adaptation under changing environments. However, automated tracing of chamber growth from imaging data remains largely unexplored, with existing approaches relying heavily on manual segmentation of each chamber, which is time-consuming and subjective. In this study, we propose an end-to-end pipeline that integrates instance segmentation, a computer vision technique not extensively explored in foraminifera, with a dedicated chamber ordering algorithm to automatically reconstruct three-dimensional growth trajectories from high-resolution computed tomography scans. We quantitatively and qualitatively evaluate multiple instance segmentation methods, each optimized for distinct spatial features of the chambers, and examine their downstream influence on growth-order reconstruction accuracy. Experimental results on expert-annotated datasets demonstrate that the proposed pipeline substantially reduces manual effort while maintaining biologically meaningful accuracy. Although segmentation models exhibit under-segmentation in smaller chambers due to reduced voxel fidelity and subtle inter-chamber connectivity, the chamber-ordering algorithm remains robust, achieving consistent reconstruction of developmental trajectories even under partial segmentation. This work provides the first fully automated and reproducible pipeline for digital foraminiferal growth analysis, establishing a foundation for large-scale, data-driven ecological studies.