| Digital rock physics benchmarks—Part I: Imaging and segmentation H Andrä, N Combaret, J Dvorkin, E Glatt, J Han, M Kabel, Y Keehm, ... Computers & Geosciences 50, 25-32, 2013 | 805 | 2013 |
| Digital rock physics benchmarks—Part II: Computing effective properties H Andrä, N Combaret, J Dvorkin, E Glatt, J Han, M Kabel, Y Keehm, ... Computers & Geosciences 50, 33-43, 2013 | 686 | 2013 |
| Structural clusters of evolutionary trace residues are statistically significant and common in proteins S Madabushi, H Yao, M Marsh, DM Kristensen, A Philippi, ME Sowa, ... Journal of molecular biology 316 (1), 139-154, 2002 | 262 | 2002 |
| Automated segmentation of computed tomography images of fiber-reinforced composites by deep learning A Badran, D Marshall, Z Legault, R Makovetsky, B Provencher, N Piché, ... Journal of Materials Science 55, 16273-16289, 2020 | 87 | 2020 |
| Application of deep learning convolutional neural networks for internal tablet defect detection: high accuracy, throughput, and adaptability X Ma, N Kittikunakorn, B Sorman, H Xi, A Chen, M Marsh, A Mongeau, ... Journal of Pharmaceutical Sciences 109 (4), 1547-1557, 2020 | 71 | 2020 |
| Dragonfly as a platform for easy image-based deep learning applications R Makovetsky, N Piche, M Marsh Microscopy and microanalysis 24 (S1), 532-533, 2018 | 65 | 2018 |
| X-ray CT and laboratory measurements on glacial till subsoil cores: assessment of inherent and compaction-affected soil structure characteristics M Lamandé, D Wildenschild, FE Berisso, A Garbout, M Marsh, P Moldrup, ... Soil Science 178 (7), 359-368, 2013 | 48 | 2013 |
| Steps toward automated deprocessing of integrated circuits EL Principe, N Asadizanjani, D Forte, M Tehranipoor, R Chivas, ... International Symposium for Testing and Failure Analysis 81504, 285-298, 2017 | 43 | 2017 |
| Deep learning-based segmentation of cryo-electron tomograms JE Heebner, C Purnell, RK Hylton, M Marsh, MA Grillo, MT Swulius J Vis Exp 189, e64435, 2022 | 31 | 2022 |
| Processing of micro-CT images of granodiorite rock samples using convolutional neural networks (CNN), Part I: Super-resolution enhancement using a 3D CNN A Roslin, M Marsh, N Piché, B Provencher, TR Mitchell, IA Onederra, ... Minerals Engineering 188, 107748, 2022 | 23 | 2022 |
| Correlative X-ray and electron microscopy for multi-scale characterization of heterogeneous shale reservoir pore systems J Goral, I Miskovic, J Gelb, M Marsh | 17 | 2016 |
| Poromechanics investigation at pore-scale using digital rock physics laboratory S Zhang, N Saxena, P Barthelemy, M Marsh, G Mavko, T Mukerji Proc., The Proceedings of 2011 COMSOL Conference in Stuttgart, 2011 | 17 | 2011 |
| Volumetric reconstruction of a human retinal pigment epithelial cell reveals specialized membranes and polarized distribution of organelles M Lindell, D Kar, A Sedova, YJ Kim, OS Packer, U Schmidt-Erfurth, ... Investigative Ophthalmology & Visual Science 64 (15), 35-35, 2023 | 15 | 2023 |
| Deep learning convolutional neural networks for pharmaceutical tablet defect detection X Ma, N Kittikunakorn, B Sorman, H Xi, A Chen, M Marsh, A Mongeau, ... Microscopy and Microanalysis 26 (S2), 1606-1609, 2020 | 15 | 2020 |
| Simplifying and streamlining large-scale materials image processing with wizard-driven and scalable deep learning B Provencher, N Piché, M Marsh Microscopy and Microanalysis 25 (S2), 402-403, 2019 | 13 | 2019 |
| Dragonfly SegmentationTrainer-a general and user-friendly machine learning image segmentation solution N Piche, I Bouchard, M Marsh Microscopy and Microanalysis 23 (S1), 132-133, 2017 | 10 | 2017 |
| Processing of micro-CT images of granodiorite rock samples using convolutional neural networks (CNN), Part II: Semantic segmentation using a 2.5 D CNN A Roslin, M Marsh, B Provencher, TR Mitchell, IA Onederra, CR Leonardi Minerals Engineering 195, 108027, 2023 | 8 | 2023 |
| Forget about cleaning up your micrographs: deep learning segmentation is robust to image artifacts P Dong, B Provencher, N Basim, N Piché, M Marsh Microscopy and Microanalysis 26 (S2), 1468-1469, 2020 | 8 | 2020 |
| Hyperparameter tuning for deep learning semantic image segmentation of micro computed tomography scanned fiber-reinforced composites B Provencher, A Badran, J Kroll, M Marsh Tomography of Materials and Structures 5, 100032, 2024 | 6 | 2024 |
| Deep learning-based segmentation of high-resolution computed tomography image data outperforms commonly used automatic bone segmentation methods DM Patton, EN Henning, RW Goulet, SK Carroll, EMR Bigelow, ... bioRxiv, 2021.07. 27.453890, 2021 | 5 | 2021 |