BIOMEDICAL IMAGING GROUP (BIG)
Laboratoire d'imagerie biomédicale (LIB)

Reproducible User-Friendly Deep Learning Workflows for Microscopy Image Analysis with deepImageJ

E. Gómez-de-Mariscal, C. García-López-de-Haro, C. de-la-Torre-Gutiérrez, R.F. Laine, G. Jacquemet, R. Henriques, D. Sage, A. Muñoz-Barrutia

Proceedings of the 2022 Workshop on Focus on Microscopy (FM'22), Porto, Portuguese Republic, Virtual, April 10-13, 2022, paper no. TU-PAR1-D.

In the last decade, advances in Deep Learning (DL) methodologies have enormously contributed to improving the solution of several bioimage analysis tasks such as denoising, super-resolution, segmentation, detection, tracking, response prediction, or computer-aided diagnosis. These techniques support automatic image processing workflows and have demonstrated the potential to surpass human-level performance in common tasks [1]. Consequently, they profoundly impact how life-science researchers conduct their bioimage data analysis. However, integrating this breakthrough technology into research pipelines remains a challenge for the scientific community. Training and evaluating DL models requires previous programming expertise and technical knowledge. Therefore, the transfer of this technology to the daily practice of life-sciences researchers remains a bottleneck [2]. Pioneering works have recently targeted the very need to make DL solutions accessible through user-friendly software [3, 4, 5, 6, 7, 8]. Moreover, the bioimage analysis community is increasingly interested in spreading general knowledge about DL and supporting its democratization [9, 10, 11, 12].

References

  1. F. Xing, Y. Xie, H. Su, F. Liu, L. Yang, "Deep Learning in Microscopy Image Analysis: A Survey," IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 10, pp. 4550-4568, October 2018.

  2. E. Meijering, "A Bird's-Eye View of Deep Learning in Bioimage Analysis," Computational and Structural Biotechnology Journal, vol. 18, pp. 2312-2325, August 7, 2020.

  3. E. Gómez-de-Mariscal, C. García-López-de-Haro, W. Ouyang, L. Donati, E. Lundberg, M. Unser, A. Muñoz-Barrutia, D. Sage, "DeepImageJ: A User-Friendly Environment to Run Deep Learning Models in ImageJ," Nature Methods—Techniques for Life Scientists and Chemists, vol. 18, no. 10, pp. 1192-1195, October 2021.

  4. L. von Chamier, R.F. Laine, J. Jukkala, C. Spahn, D. Krentzel, E. Nehme, M. Lerche, S. Hernández-Pérez, P.K. Mattila, E. Karinou, S. Holden, A.C. Solak, A. Krull, T.-O. Buchholz, M.L. Jones, L.A. Royer, C. Leterrier, Y. Shechtman, F. Jug, M. Heilemann, G. Jacquemet, R. Henriques, "Democratising Deep Learning for Microscopy with ZeroCostDL4Mic," Nature Communications, vol. 12, paper no. 2276, pp. 1-18, April 15, 2021.

  5. M. Weigert, U. Schmidt, T. Boothe, A. Müller, A. Dibrov, A. Jain, B. Wilhelm, D. Schmidt, C. Broaddus, S. Culley, M. Rocha-Martins, F. Segovia-Miranda, C. Norden, R. Henriques, M. Zerial, M. Solimena, J. Rink, P. Tomancak, L. Royer, F. Jug, E.W. Myers, "Content-Aware Image Restoration: Pushing the Limits of Fluorescence Microscopy," Nature Methods—Techniques for Life Scientists and Chemists, vol. 15, no. 12, pp. 1090-1097, December 2018.

  6. U. Schmidt, M. Weigert, C. Broaddus, G. Myers, "Cell Detection with Star-Convex Polygons," Proceedings of MICCAI 2018, Granada, Kingdom of Spain, September 16-20, 2018, [Lecture Notes in Computer Science, vol. 11071, Springer, 2018], pp. 265-273.

  7. C. Stringer, T. Wang, M. Michaelos, M. Pachitariu, "Cellpose: A Generalist Algorithm for Cellular Segmentation," Nature Methods—Techniques for Life Scientists and Chemists, vol. 18, no. 1, pp. 100-106, January 2021.

  8. W. Ouyang, F. Mueller, M. Hjelmare, E. Lundberg, C. Zimmer, "ImJoy: An Open-Source Computational Platform for the Deep Learning Era," Nature Methods—Techniques for Life Scientists and Chemists, vol. 16, no. 12, pp. 1199-1200, December 2019.

  9. R.F. Laine, I. Arganda-Carreras, R. Henriques, G. Jacquemet, "Avoiding a Replication Crisis in Deep-Learning- Based Bioimage Analysis," Nature Methods—Techniques for Life Scientists and Chemists, vol. 18, no. 10, pp. 1136-1144, October 2021.

  10. A. Hallou, H.G. Yevick, B. Dumitrascu, V. Uhlmann, "Deep Learning for Bioimage Analysis in Developmental Biology," Development, vol. 148, no. 18, pp. 1-12, September 2021.

  11. L. von Chamier, R.F. Laine, R. Henriques, "Artificial Intelligence for Microscopy: What You Should Know," Biochemical Society Transactions, vol. 47, no. 4, pp. 1029-1040, August 2019.

  12. A.M. Lucas, P.V. Ryder, B. Lib, B.A. Cimini, K.W. Eliceiri, A.E. Carpenter, "Open-Source Deep-Learning Software for Bioimage Segmentation," Molecular Biology of the Cell, vol. 32, no. 9, pp. 823-829, April 19, 2021.

  13. C.A. Schneider, W.S. Rasband, K.W. Eliceiri, "NIH Image to ImageJ: 25 Years of Image Analysis," Nature Methods—Techniques for Life Scientists and Chemists, vol. 9, no. 7, pp. 671-675, June 28, 2012.

  14. J. Schindelin, I. Arganda-Carreras, E. Frise, V. Kaynig, M. Longair, T. Pietzsch, S. Preibisch, C. Rueden, S. Saalfeld, B. Schmid, J.-Y. Tinevez, D.J. White, V. Hartenstein, K. Eliceiri, P. Tomancak, A. Cardona, "Fiji: An Open-Source Platform for Biological-Image Analysis," Nature Methods—Techniques for Life Scientists and Chemists, vol. 9, no. 7, pp. 676-682, June 28, 2012.

  15. T. Falk, D. Mai, R. Bensch, Ö. Çiçek, A. Abdulkadir, Y. Marrakchi, A. Böhm, J. Deubner, Z. Jäckel, K. Seiwald, A. Dovzhenko, O. Tietz, C. Dal Bosco, S. Walsh, D. Saltukoglu, T.L. Tay, M. Prinz, K. Palme, M. Simons, I. Diester, T. Brox, O. Ronneberger, "U-Net: Deep Learning for Cell Counting, Detection, and Morphometry," Nature Methods—Techniques for Life Scientists and Chemists, vol. 16, no. 1, pp. 67-70, January 2019.

  16. E. Nehme, L.E. Weiss, T. Michaeli, Y. Shechtman, "Deep-STORM: Super-Resolution Single-Molecule Microscopy by Deep Learning," Optica, vol. 5, no. 4, pp. 458-464, April 2018. 

@INPROCEEDINGS(http://bigwww.epfl.ch/publications/gomezdemariscal2201.html,
AUTHOR="G{\'{o}}mez-de-Mariscal, E. and
	Garc{\'{i}}a-L{\'{o}}pez-de-Haro, C. and
	de-la-Torre-Guti{\'{e}}rrez, C. and Laine, R.F. and Jacquemet, G.
	and Henriques, R. and Sage, D. and Mu{\~{n}}oz-Barrutia, A.",
TITLE="Reproducible User-Friendly Deep Learning Workflows for Microscopy
	Image Analysis with {deepImageJ}",
BOOKTITLE="Proceedings of the 2022 Workshop on Focus on Microscopy
	({FM'22})",
YEAR="2022",
editor="",
volume="",
series="",
pages="",
address="Porto, Portuguese Republic",
month="April 10-13,",
organization="",
publisher="",
note="paper no.\ TU-PAR1-D")
© 2022 FM. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from FM. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.