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From instance segmentation to 3D growth trajectory reconstruction in Planktonic foraminifera

From instance segmentation to 3D growth trajectory reconstruction in Planktonic foraminifera
From instance segmentation to 3D growth trajectory reconstruction in Planktonic foraminifera
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.
cs.CV
arXiv
Lin, Huahua
afee4716-d506-458d-8813-a9fb37a115ec
Cai, Xiaohao
de483445-45e9-4b21-a4e8-b0427fc72cee
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Mulqueeney, James M.
20bf3f65-5f1a-4836-bccd-f8c97c6f61ab
Ezard, Thomas H. G.
a143a893-07d0-4673-a2dd-cea2cd7e1374
Lin, Huahua
afee4716-d506-458d-8813-a9fb37a115ec
Cai, Xiaohao
de483445-45e9-4b21-a4e8-b0427fc72cee
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Mulqueeney, James M.
20bf3f65-5f1a-4836-bccd-f8c97c6f61ab
Ezard, Thomas H. G.
a143a893-07d0-4673-a2dd-cea2cd7e1374

[Unknown type: UNSPECIFIED]

Record type: UNSPECIFIED

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.

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2511.02142v1 - Author's Original
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Published date: 4 November 2025
Keywords: cs.CV

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Local EPrints ID: 507657
URI: http://eprints.soton.ac.uk/id/eprint/507657
PURE UUID: 00de557c-55fd-4c18-b51f-5cf9810b51d4
ORCID for Huahua Lin: ORCID iD orcid.org/0000-0002-1600-6293
ORCID for Xiaohao Cai: ORCID iD orcid.org/0000-0003-0924-2834
ORCID for Mark Nixon: ORCID iD orcid.org/0000-0002-9174-5934
ORCID for James M. Mulqueeney: ORCID iD orcid.org/0000-0003-3502-745X
ORCID for Thomas H. G. Ezard: ORCID iD orcid.org/0000-0001-8305-6605

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Date deposited: 16 Dec 2025 18:14
Last modified: 18 Dec 2025 03:09

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Contributors

Author: Huahua Lin ORCID iD
Author: Xiaohao Cai ORCID iD
Author: Mark Nixon ORCID iD
Author: Thomas H. G. Ezard ORCID iD

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