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 inve...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.展开更多
基金support from the National Science Foundation Graduate Research Fellowship (DGE-1842473)the NIH Tissue Engineering Research Center (P41 EB002520)for support of this work in its initial stages+3 种基金support from the National Institutes of Health Institutional Research and Academic Career Development Awards Program at the Tufts University (K12GM074869,Training in Education and Critical Research Skills (TEACRS))support from an NIH postdoctoral fellowship (F32-DE026058)supported by the National Center for Advancing Translation Sciences (NCATS),a component of the National Institutes of Health (NIH)under award number KL2TR003018support from the University of Florida Herbert Wertheim College of Engineering Graduate School Preeminence Award and Institute for Cell and Tissue Science and Engineering Pittman Fellowship.
文摘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.
