The performance and stability of pharmaceutical hydrogels depend on microstructural features such as particle size and agglomeration. Characterization of these features is commonly performed through manual microscopic assessment, which can be subjective when particle boundaries are indistinct or agglomerates lack clearly defined interfaces. This study applies computer vision to automate the analysis of hydrogel micrographs, including particle segmentation, agglomerate identification, and the extraction of size-related morphological parameters. Two deep instance segmentation networks, the Mask R-CNN and the Mask2Former, were applied for this purpose. This study introduced a particle dilation and a graph-centric approach for agglomerate identification, differing from prior methods where agglomerates were annotated individually, thus mitigating some annotation challenges. The networks, trained on a hydrogel database, exhibited an Average Precision of 92.47% for Mask R-CNN and 91.86% for Mask2Former. The Mask2Former demonstrated superior Average Recall (AR) at 76.6%, compared to Mask R-CNN’s 72.32%. This study pioneers the application of Mask2Former, a Vision Transformer-based network, for particle segmentation which had superior AR performance. Considering the subjective nature of annotation for hydrogel micrographs, where false positives can be considered as valid particles, this study recommends the inclusion of AR as a metric for model selection. Furthermore, the extracted morphological features from segmented images showed close agreement with manual measurements. This workflow has potential to support formulation development and quality assessment in pharmaceutical settings. • Mask R-CNN and Mask2Former were applied to segment hydrogel particles in micrographs. • A particle dilation and graph-based method was used to detect agglomerates without manual labels. • Mask2Former achieved higher AR (76.6% vs 72.32%), but slightly lower AP (91.86% vs 92.47%). • AR is highlighted as a key metric in subjectively annotated particle datasets. • Both models enabled accurate particle morphology extraction aligned with experts annotations.
Khosravi et al. (Fri,) studied this question.