Convolutional neural networks for segmentation of FIB‐SEM nanotomography data from porous polymer films for controlled drug release

Abstract

Phase‐separated polymer films are commonly used as coatings around pharmaceutical oral dosage forms (tablets or pellets) to facilitate controlled drug release. A typical choice is to use ethyl cellulose and hydroxypropyl cellulose (EC/HPC) polymer blends. When an EC/HPC film is in contact with water, the leaching out of the water‐soluble HPC phase produces an EC film with a porous network through which the drug is transported. The drug release can be tailored by controlling the structure of this porous network. Imaging and characterization of such EC porous films facilitates understanding of how to control and tailor film formation and ultimately drug release. Combined focused ion beam and scanning electron microscope (FIB‐SEM) tomography is a well‐established technique for high‐resolution imaging, and suitable for this application. However, for segmenting image data, in this case to correctly identify the porous network, FIB‐SEM is a challenging technique to work with. In this work, we implement convolutional neural networks for segmentation of FIB‐SEM image data. The data are acquired from three EC porous films where the HPC phases have been leached out. The three data sets have varying porosities in a range of interest for controlled drug release applications. We demonstrate very good agreement with manual segmentations. In particular, we demonstrate an improvement in comparison to previous work on the same data sets that utilized a random forest classifier trained on Gaussian scale‐space features. Finally, we facilitate further development of FIB‐SEM segmentation methods by making the data and software used open access.

A new approach to prediction riboflavin absorbance using imprinted polymer and ensemble machine learning algorithms

“…They segmented the data using a random forest classifier. Their results showed good agreement with manual segmentations [ 33 ]. Kaffayatullah Khan and colleagues (2022) predicted the flexural capacity of flexural members with excellent accuracy using the RF regression model [ 34 ].…”