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High-throughput Image-based Matrix Analysis Platform Software: development of an algorithm for standardized
Ali Aftabjahani1,2, Savas J Kim2, Chengliang Wu2
1Departments of Medicine and Medical Science, McMaster University, Hamilton, Ontario, Canada.
Background:
Scanning electron microscopy has been gaining traction as a tool to provide high-resolution images of biological specimens such as blood clots to associate their properties with clinical outcomes. However, there lacks a "gold-standard" method of quantifying these images.
Objective:
To develop and validate a novel high-throughput, semiautomated computational pipeline for quantifying high-resolution images of biological samples called HIMAPS (High-throughput Image-based Matrix Analysis Platform Software).
Methods:
From an imported image, a region of interest is defined, and individual pixels are calibrated to physical dimensions (μm). The brightness intensity values of each pixel are then normalized to generate relative intensity values and are subjected to the stratification algorithm to partition the image into layers. The identified fiber structures are enhanced and visualization is optimized as binary masks undergo morphologic refinement to reduce noise, which can then be quantified for: (1) fiber thickness from a skeletonized representation of the network, and (2) network porosity and density from identification of pores.
Results:
Fiber thickness was used as a validation method by comparing against the published standardization manual protocol. Our algorithm was within the 95% CI of the manual measurements (N = 8 images, >80 fibers per image) when the top 60% of the relative intensity value was included. The top 55% to 60% intensity-stratified reconstruction also led to the most robust pore size quantitation under the imaging conditions tested.
Conclusion:
This algorithm can accurately quantify physical properties of clots such as fiber thickness, fiber matrix density, and porosity in a semiautomated, rapid, and unbiased manner.
