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 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.

RhizoVision Crown:An Integrated Hardware and Software Platform for Root Crown Phenotyping

Root crown phenotyping measures the top portion of crop root systems and can be used for marker-assisted breeding,genetic mapping,and understanding how roots influence soil resource acquisition.Several imaging protocols and image analysis programs exist,but they are not optimized for high-throughput,repeatable,and robust root crown phenotyping.The RhizoVision Crown platform integrates an imaging unit,image capture software,and image analysis software that are optimized for reliable extraction of measurements from large numbers of root crowns.The hardware platform utilizes a backlight and a monochrome machine vision camera to capture root crown silhouettes.The RhizoVision Imager and RhizoVision Analyzer are free,open-source software that streamline image capture and image analysis with intuitive graphical user interfaces.The RhizoVision Analyzer was physically validated using copper wire,and features were extensively validated using 10,464 groundtruth simulated images of dicot and monocot root systems.This platform was then used to phenotype soybean and wheat root crowns.A total of 2,799 soybean(Glycine max)root crowns of 187 lines and 1,753 wheat(Triticum aestivum)root crowns of 186 lines were phenotyped.Principal component analysis indicated similar correlations among features in both species.The maximum heritability was 0.74 in soybean and 0.22 in wheat,indicating that differences in species and populations need to be considered.The integrated RhizoVision Crown platform facilitates high-throughput phenotyping of crop root crowns and sets a standard by which open plant phenotyping platforms can be benchmarked.

Evaluation of Postharvest Senescence of Broccoli via Hyperspectral Imaging

Fresh fruit and vegetables are invaluable for human health;however,their quality often deteriorates before reaching consumers due to ongoing biochemical processes and compositional changes.We currently lack any objective indices which indicate the freshness of fruit or vegetables resulting in limited capacity to improve product quality eventually leading to food loss and waste.