Stable Deep Neural Network Architectures for Mitochondria Segmentation on Electron Microscopy Volumes
dc.contributor.author | Franco Barranco, Daniel | |
dc.contributor.author | Muñoz Barrutia, Arrate | |
dc.contributor.author | Arganda Carreras, Ignacio | |
dc.date.accessioned | 2023-02-08T17:31:58Z | |
dc.date.available | 2023-02-08T17:31:58Z | |
dc.date.issued | 2022-04 | |
dc.identifier.citation | Neuroinformatics 20(2) : 437-450 (2022) | es_ES |
dc.identifier.issn | 1539-2791 | |
dc.identifier.issn | 1559-0089 | |
dc.identifier.uri | http://hdl.handle.net/10810/59731 | |
dc.description.abstract | Electron microscopy (EM) allows the identification of intracellular organelles such as mitochondria, providing insights for clinical and scientific studies. In recent years, a number of novel deep learning architectures have been published reporting superior performance, or even human-level accuracy, compared to previous approaches on public mitochondria segmentation datasets. Unfortunately, many of these publications make neither the code nor the full training details public, leading to reproducibility issues and dubious model comparisons. Thus, following a recent code of best practices in the field, we present an extensive study of the state-of-the-art architectures and compare them to different variations of U-Net-like models for this task. To unveil the impact of architectural novelties, a common set of pre- and post-processing operations has been implemented and tested with each approach. Moreover, an exhaustive sweep of hyperparameters has been performed, running each configuration multiple times to measure their stability. Using this methodology, we found very stable architectures and training configurations that consistently obtain state-of-the-art results in the well-known EPFL Hippocampus mitochondria segmentation dataset and outperform all previous works on two other available datasets: Lucchi++ and Kasthuri++. The code and its documentation are publicly available at https://github.com/danifranco/EM_Image_Segmentation. | es_ES |
dc.description.sponsorship | Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work was supported by Ministerio de Ciencia, Innovación y Universidades, Agencia Estatal de Investigación, under Grants TEC2016-78052 and PID2019-109820RB-I00, MCIN/AEI/10.13039/501100011033/, co-finance by European Regional Development Fund (ERDF), “A way of making Europe.” I.A-C would like to acknowledge the support of the Leonardo Grant for Researchers and Cultural Creators, BBVA Foundation. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Springer | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/TEC2016-78052 | es_ES |
dc.relation | info:eu-repo/grantAgreement/MICINN/PID2019-109820RB-I00 | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | electron microscopy | es_ES |
dc.subject | mitochondria | es_ES |
dc.subject | semantic segmentation | es_ES |
dc.subject | deep learning | es_ES |
dc.subject | bioimage analysis | es_ES |
dc.title | Stable Deep Neural Network Architectures for Mitochondria Segmentation on Electron Microscopy Volumes | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | © The Author(s) 2021. This article is licensed under a Creative Commons Attri- bution 4.0 International License, which permits use, sharing, adapta- tion, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. | es_ES |
dc.rights.holder | Atribución 3.0 España | * |
dc.relation.publisherversion | https://link.springer.com/article/10.1007/s12021-021-09556-1 | es_ES |
dc.identifier.doi | 10.1007/s12021-021-09556-1 | |
dc.departamentoes | Ciencia de la computación e inteligencia artificial | es_ES |
dc.departamentoeu | Konputazio zientziak eta adimen artifiziala | es_ES |
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