Jointly Optimized Spatial Histogram UNET Architecture (JOSHUA) for Adipose Tissue Segmentation
Objective . We aim to develop a machine learning algorithm to 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...
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| Published in: | BME frontiers Vol. 2022; p. 9854084 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
AAAS
01-01-2022
American Association for the Advancement of Science (AAAS) |
| Online Access: | Get full text |
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| Summary: | Objective
. We aim to develop a machine learning algorithm to 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 analyzed adipose tissue accumulation in histological images of sectioned silk fibroin-based biomaterials excised from rodents following subcutaneous implantation for 1, 2, 4, or 8 weeks. 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 used in our experiments are publicly available. |
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| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 The authors declare that there is no conflict of interest regarding the publication of this manuscript. |
| ISSN: | 2765-8031 2765-8031 |
| DOI: | 10.34133/2022/9854084 |