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Jointly Optimized Spatial Histogram UNET Architecture (JOSHUA) for Adipose Tissue Segmentation

View ORCID ProfileJoshua K. Peeples, View ORCID ProfileJulie F. Jameson, Nisha M. Kotta, View ORCID ProfileJonathan M. Grasman, View ORCID ProfileWhitney L. Stoppel, View ORCID ProfileAlina Zare
doi: https://doi.org/10.1101/2021.11.22.469463
Joshua K. Peeples
1Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, 32611, USA
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Julie F. Jameson
2Department of Chemical Engineering, University of Florida, Gainesville, FL, 32611, USA
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Nisha M. Kotta
3J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, 32611, USA
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Jonathan M. Grasman
4Department of Biomedical Engineering, New Jersey Institute of Technology, University Heights, Newark, NJ, 07102, USA
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Whitney L. Stoppel
2Department of Chemical Engineering, University of Florida, Gainesville, FL, 32611, USA
3J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, 32611, USA
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  • For correspondence: whitney.stoppel@ufl.edu azare@ece.ufl.edu
Alina Zare
1Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, 32611, USA
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  • For correspondence: whitney.stoppel@ufl.edu azare@ece.ufl.edu
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Abstract

Objective We quantify adipose tissue deposition at surgical sites as a function of biomaterial implantation.

Impact Statement To our knowledge, this study is the first investigation to apply convolutional neural network (CNN) models to identify and segment adipose tissue in histological images from silk fibroin biomaterial implants.

Introduction When designing biomaterials for the treatment of various soft tissue injuries and diseases, one must consider the extent of adipose tissue deposition. In this work, we implant silk fibroin biomaterials in a rodent subcutaneous injury model. Current strategies for quantifying adipose tissue after biomaterial implantation are often tedious and prone to human bias during analysis.

Methods We used CNN models with novel spatial histogram layer(s) that can more accurately identify and segment regions of adipose tissue in hematoxylin and eosin (H&E) and Masson’s Trichrome stained images, allowing for determination of the optimal biomaterial formulation. We compared the method, Jointly Optimized Spatial Histogram UNET Architecture (JOSHUA), to the baseline UNET model and an extension of the baseline model, Attention UNET, as well as to versions of the models with a supplemental “attention”-inspired mechanism (JOSHUA+ and UNET+).

Results The inclusion of histogram layer(s) in our models shows improved performance through qualitative and quantitative evaluation.

Conclusion Our results demonstrate that the proposed methods, JOSHUA and JOSHUA+, are highly beneficial for adipose tissue identification and localization. The new histological dataset and code for our experiments are publicly available.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/GatorSense/Histological_Segmentation

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted November 23, 2021.
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Jointly Optimized Spatial Histogram UNET Architecture (JOSHUA) for Adipose Tissue Segmentation
Joshua K. Peeples, Julie F. Jameson, Nisha M. Kotta, Jonathan M. Grasman, Whitney L. Stoppel, Alina Zare
bioRxiv 2021.11.22.469463; doi: https://doi.org/10.1101/2021.11.22.469463
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Jointly Optimized Spatial Histogram UNET Architecture (JOSHUA) for Adipose Tissue Segmentation
Joshua K. Peeples, Julie F. Jameson, Nisha M. Kotta, Jonathan M. Grasman, Whitney L. Stoppel, Alina Zare
bioRxiv 2021.11.22.469463; doi: https://doi.org/10.1101/2021.11.22.469463

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