Articles | Volume 5, issue 1
Short communication/technical note
10 Mar 2023
Short communication/technical note |  | 10 Mar 2023

Technical note: colab_zirc_dims: a Google Colab-compatible toolset for automated and semi-automated measurement of mineral grains in laser ablation–inductively coupled plasma–mass spectrometry images using deep learning models

Michael C. Sitar and Ryan J. Leary

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Cited articles

Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., and Farhan, L.: Review of deep learning: concepts, CNN architectures, challenges, applications, future directions, J. Big Data, 8, 53,, 2021. 
Augustsson, C., Voigt, T., Bernhart, K., Kreißler, M., Gaupp, R., Gärtner, A., Hofmann, M., and Linnemann, U.: Zircon size-age sorting and source-area effect: The German Triassic Buntsandstein Group, Sediment. Geol., 375, 218–231,, 2018. 
Bradski, G.: The OpenCV Library, Dr. Dobb's Journal of Software Tools, 25, 120–125, 2000. 
Bukharev, A., Budennyy, S., Lokhanova, O., Belozerov, B., and Zhukovskaya, E.: The Task of Instance Segmentation of Mineral Grains in Digital Images of Rock Samples (Thin Sections), in: 2018 International Conference on Artificial Intelligence Applications and Innovations (IC-AIAI), 2018 International Conference on Artificial Intelligence Applications and Innovations (IC-AIAI), IC-AIAI 2018, Nicosia, Cyprus, 31 October–2 November 2018, 18–23,, 2018. 
Short summary
We developed code to automatically and semi-automatically measure dimensions of detrital mineral grains in reflected-light images saved at laser ablation–inductively coupled plasma–mass spectrometry facilities that use Chromium targeting software. Our code uses trained deep learning models to segment grain images with greater accuracy than is achievable using other segmentation techniques. We implement our code in Jupyter notebooks which can also be run online via Google Colab.