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- Title
Deep Learning Based Instance Segmentation of Titanium Dioxide Particles in the Form of Agglomerates in Scanning Electron Microscopy.
- Authors
Monchot, Paul; Coquelin, Loïc; Guerroudj, Khaled; Feltin, Nicolas; Delvallée, Alexandra; Crouzier, Loïc; Fischer, Nicolas; Drazic, Goran
- Abstract
The size characterization of particles present in the form of agglomerates in images measured by scanning electron microscopy (SEM) requires a powerful image segmentation tool in order to properly define the boundaries of each particle. In this work, we propose to use an algorithm from the deep statistical learning community, the Mask-RCNN, coupled with transfer learning to overcome the problem of generalization of the commonly used image processing methods such as watershed or active contour. Indeed, the adjustment of the parameters of these algorithms is almost systematically necessary and slows down the automation of the processing chain. The Mask-RCNN is adapted here to the case study and we present results obtained on titanium dioxide samples (non-spherical particles) with a level of performance evaluated by different metrics such as the DICE coefficient, which reaches an average value of 0.95 on the test images.
- Subjects
SCANNING electron microscopy; TITANIUM dioxide; DEEP learning; STATISTICAL learning; IMAGE segmentation; PLASTIC extrusion
- Publication
Nanomaterials (2079-4991), 2021, Vol 11, Issue 4, p968
- ISSN
2079-4991
- Publication type
Article
- DOI
10.3390/nano11040968