ArticleNanomaterials (Basel, Switzerland)2021
Deep Learning Based Instance Segmentation of Titanium Dioxide Particles in the Form of Agglomerates in Scanning Electron Microscopy.
Article in Nanomaterials (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 45 citations in OpenAlex.
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- Quantifying Monomer-Dimer Distribution of Nanoparticles from Uncorrelated Optical Images Using Deep Learning.ACS omega · 2025Article
- Characterization of Particle Shape with an Improved 3D Light Scattering Sensor (3D-LSS) for Aerosols.Sensors (Basel, Switzerland) · 2024Article
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- Weighted average ensemble-based semantic segmentation in biological electron microscopy images.Histochemistry and cell biology · 2022Article
- Efficacy Evaluation of Ultrasound with Active Contour Model for Hemodialysis in Children with Renal Failure.Computational and mathematical methods in medicine · 2022Article
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
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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.
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