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Authors: Fabian Schmeisser 1 ; 2 ; Maria Caroprese 3 ; 4 ; Gillian Lovell 3 ; 4 ; Andreas Dengel 1 ; 2 and Sheraz Ahmed 1

Affiliations: 1 German Research Center for Artificial Intelligence (DFKI) GmbH, Kaiserslautern 67663, Germany ; 2 RPTU Kaiserslautern-Landau, Kaiserslautern 67663, Germany ; 3 Sartorius BioAnalytics, Royston, U.K. ; 4 Sartorius Corporate Research, Royston, U.K.

Keyword(s): Cell Segmentation, 2.5D, 3D, Weak Supervision.

Abstract: Cell segmentation in volumetric microscopic images is a fundamental step towards automating the analysis of life-like representations of complex specimens. As the performance of current Deep Learning algorithms is held back by the lack of accurately annotated ground truth, a pipeline is proposed that produces accurate 3D cell instance segmentation masks solely from slice-wise bounding box annotations. In an effort to further reduce the time requirements for the annotation process, a study is conducted on how to effectively reduce the size of the training set. To this end, three slice-reduction strategies are suggested and evaluated in combination with bounding box supervision. We find that as low as 1% of weakly labeled training data suffices to produce accurate results, and that predictions produced by a 10 times smaller dataset are of equal quality to when the full dataset is exploited for training.

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Paper citation in several formats:
Schmeisser, F., Caroprese, M., Lovell, G., Dengel, A. and Ahmed, S. (2025). How to Box Your Cells: An Introduction to Box Supervision for 2.5D Cell Instance Segmentation and a Study of Applications. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5; ISSN 2184-433X, SciTePress, pages 853-860. DOI: 10.5220/0013189800003890

@conference{icaart25,
author={Fabian Schmeisser and Maria Caroprese and Gillian Lovell and Andreas Dengel and Sheraz Ahmed},
title={How to Box Your Cells: An Introduction to Box Supervision for 2.5D Cell Instance Segmentation and a Study of Applications},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={853-860},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013189800003890},
isbn={978-989-758-737-5},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - How to Box Your Cells: An Introduction to Box Supervision for 2.5D Cell Instance Segmentation and a Study of Applications
SN - 978-989-758-737-5
IS - 2184-433X
AU - Schmeisser, F.
AU - Caroprese, M.
AU - Lovell, G.
AU - Dengel, A.
AU - Ahmed, S.
PY - 2025
SP - 853
EP - 860
DO - 10.5220/0013189800003890
PB - SciTePress

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