3D volume segmentation and reconstruction. Supervised image classification and automated quantification of superparamagnetic iron oxide nanoparticles in histology slides for safety assessment

Abstract

This article presents an automated image-processing workflow for quantitative assessment of SPION accumulation in tissue sections stained with Prussian blue for iron detection. We utilized supervised machine learning with manually labeled features used for training the classifier. Performance of the classifier was validated by 10-fold cross-validation of obtained data and by measuring Dice and Jaccard Similarity Coefficients between manually segmented image and automated segmentation. The proposed approach provides time and cost-effective solution for quantitative imaging analysis of SPION in tissue with a precision similar to that obtained via thresholding method for stain quantification. Furthermore, we exploited the classifiers to generate segmented 3D volumes from histological slides. This enabled visualization of particles which were obscured in original 3D histology stacks. Our approach offers a powerful tool for preclinical assessment of the precise tissue-specific SPION biodistribution, which could affect both their toxicity and their efficacy as nanocarriers for medicines.

Keywords:

• Superparamagnetic iron oxide nanoparticles (SPION)
• supervised machine learning
• classification
• Perls' Prussian blue
• 3D histology volumes

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Acknowledgements

The authors thank the European Commission for the partial funding of this work under the MULTIFUN project (grant ref 262943) and NoCanTher (grant ref 685795). Authors also thank Maria del Puerto Morales and Marzia Marciello at ICMM for the supply and characterisation of the material as part of the MULTIFUN project.

Compliance with ethical standards

All animal experiments were done in accordance with Health Products Regulatory Authority (Ireland).

Disclosure statement

The authors declare no conflict of interest.

Data availability statement

A GitLab repository (WEKA_4_Iron_quantification), containing resources to reproduce results presented in this article is available at https://gitlab.com/abogdans/weka_4_iron_quantification.

Additional information

Funding

The European Commission funds under the MULTIFUN project [grant ref 262943] and NoCanTher [grant ref 685795].