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

Joshua K Peeples1, Julie F Jameson2, Nisha M Kotta3

  • 1Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA.

BME Frontiers
|October 18, 2023
PubMed
Summary

A new machine learning algorithm, JOSHUA, accurately quantifies adipose tissue in histological images. This method improves biomaterial assessment for soft tissue repair, offering a less biased alternative to manual analysis.

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Area of Science:

  • Biomaterials Science
  • Computational Biology
  • Histology

Background:

  • Adipose tissue deposition is critical in biomaterial design for soft tissue repair.
  • Current quantification methods are time-consuming and prone to bias.
  • Silk fibroin biomaterials are being investigated for tissue regeneration.

Purpose of the Study:

  • To develop a machine learning algorithm for quantifying adipose tissue at surgical sites.
  • To apply convolutional neural network (CNN) models to histological images of silk fibroin implants.
  • To establish a quantitative, unbiased method for evaluating biomaterial performance.

Main Methods:

  • Utilized CNN models with novel spatial histogram layers for adipose tissue segmentation.
  • Developed the Jointly Optimized Spatial Histogram UNET Architecture (JOSHUA) and its variant JOSHUA+.
  • Compared JOSHUA models against baseline UNET and attention UNET models using H&E and Masson's trichrome stained images.

Main Results:

  • JOSHUA models demonstrated improved performance in identifying and segmenting adipose tissue.
  • Qualitative and quantitative evaluations confirmed the efficacy of histogram layers.
  • The developed models accurately localized adipose tissue in histological samples.

Conclusions:

  • JOSHUA and JOSHUA+ are effective tools for adipose tissue identification and localization in biomaterial studies.
  • The proposed methods offer a significant advancement over traditional quantification techniques.
  • A new histological dataset and code are publicly available to facilitate further research.