Comparison of texture-based classification and deep learning for plantar soft tissue histology segmentation.

ABSTRACT: Histomorphological measurements can be used to identify microstructural changes related to disease pathomechanics, in particular, plantar soft tissue changes with diabetes. However, these measurements are time-consuming and susceptible to sampling and human measurement error. We investigated two approaches to automate segmentation of plantar soft tissue stained with modified Hart's stain for elastin with the eventual goal of subsequent morphological analysis. The first approach used multiple texture- and color-based features with tile-wise classification. The second approach used a convolutional neural network modified from the U-Net architecture with fewer channel dimensions and additional downsampling steps. A hybrid color and texture feature, Fourier reduced histogram of uniform improve

SUBMITTER: Brady L 

PROVIDER: S-EPMC8263502 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature