In search of best automated model: Explaining nanoparticle TEM image segmentation

This study focuses on finding the best segmentation model, which achieves high metrics and is robust to microscopy parameters. For this purpose, eight different models have been compared. The training dataset consists of 150 BF-TEM Platinum nanoparticle images containing 3629 nanoparticles of all kinds. Further, we examine the generalizability of the models on E-TEM Gold nanoparticle images. We also describe essential considerations while choosing a network for segmenting nanoparticle images that generalize well across the Platinum BF-TEM and Gold E-TEM nanoparticles dataset. The layer gradients are visualized to further explain the black-box nature of neural networks.PMID:34953311 | DOI:10.1016/j.ultramic.2021.113437

Source: Ultramicroscopy - December 25, 2021 Category: Laboratory Medicine Authors: Kunwar Muhammed Saaim Saima Khan Afridi Maryam Nisar Saiful Islam Source Type: research