Skärberg Fredrik, Fager Cecilia, Mendoza-Lara Francisco, Josefson Mats, Olsson Eva, Lorén Niklas, Röding Magnus
Bioeconomy and Health, Agriculture and Food, RISE Research Institutes of Sweden, Göteborg, Sweden.
Department of Physics, Chalmers University of Technology, Göteborg, Sweden.
J Microsc. 2021 Jul;283(1):51-63. doi: 10.1111/jmi.13007. Epub 2021 May 4.
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.
相分离聚合物薄膜通常用作药物口服剂型（片剂或丸剂）的包衣，以促进药物的控释。一种典型的选择是使用乙基纤维素和羟丙基纤维素（EC/HPC）聚合物共混物。当EC/HPC薄膜与水接触时，水溶性HPC相的浸出会产生具有多孔网络的EC薄膜，药物通过该网络传输。药物释放可以通过控制这种多孔网络的结构来调整。对这种EC多孔薄膜进行成像和表征有助于理解如何控制和调整薄膜形成以及最终的药物释放。聚焦离子束与扫描电子显微镜（FIB-SEM）联用断层扫描是一种成熟的高分辨率成像技术，适用于此应用。然而，对于分割图像数据，在这种情况下即正确识别多孔网络，FIB-SEM是一项具有挑战性的技术。在这项工作中，我们实现了卷积神经网络用于FIB-SEM图像数据的分割。数据是从三个HPC相已被浸出的EC多孔薄膜中获取的。这三个数据集在控释应用的感兴趣范围内具有不同的孔隙率。我们证明了与手动分割结果非常吻合。特别是，与之前对相同数据集使用基于高斯尺度空间特征训练的随机森林分类器的工作相比，我们展示了改进。最后，我们通过开放获取所使用的数据和软件，促进了FIB-SEM分割方法的进一步发展。
