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Domain Adapted Multitask Learning for Segmenting Amoeboid Cells in Microscopy

IEEE TRANSACTIONS ON MEDICAL IMAGING


Suvadip Mukherjee, Rituparna Sarkar, Maria Manich, Elisabeth Labruyère and Jean-Christophe Olivo-Marin


Abstract


The method proposed in this paper is a robust combination of multi-task learning and unsupervised domain adaptation for segmenting amoeboid cells in microscopy. A highlight of this work is the manner in which themodel’s hyperparameters are estimated. The detriments of ad-hoc parameter estimation are well known, but this issue remains largely unaddressed in the context of CNN based segmentation. Using a novel min-max formulation

of the segmentation cost function our proposed method analytically estimates the model’s hyperparameters, while simultaneously learning the CNN weights during training.

This end-to-end framework provides a consolidated mechanism to harness the potential of multi-task learning to isolate and segment clustered cells from low contrast brightfield images, and it simultaneously leverages deep domain adaptation to segment fluorescent cells without explicit pixel level re-annotation of the data. Experimental validations on

multi-cellular images strongly suggest the effectiveness of the proposed technique, and our quantitative results show at least 15% and 10% improvement in cell segmentation on

brightfield and fluorescence images respectively compared to contemporary supervised segmentation methods.


Index Terms — Cell segmentation, domain adaptation, multi-task learning, deep learning, microscopy, cell biology.


More information at DOI: 10.1109/TMI.2022.3203022

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