Jointly Optimized Spatial Histogram UNET Architecture (JOSHUA) for Adipose Tissue Segmentation
Document Type
Article
Publication Date
1-1-2022
Abstract
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.
Identifier
85167679255 (Scopus)
Publication Title
Bme Frontiers
External Full Text Location
https://doi.org/10.34133/2022/9854084
e-ISSN
27658031
Volume
2022
Grant
K12GM074869
Fund Ref
University of Texas at Austin
Recommended Citation
Peeples, Joshua K.; Jameson, Julie F.; Kotta, Nisha M.; Grasman, Jonathan M.; Stoppel, Whitney L.; and Zare, Alina, "Jointly Optimized Spatial Histogram UNET Architecture (JOSHUA) for Adipose Tissue Segmentation" (2022). Faculty Publications. 3446.
https://digitalcommons.njit.edu/fac_pubs/3446
