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  1. Article: Identification and measurement of tropical tuna species in purse seiner catches using computer vision and deep learning

    Lekunberri, Xabier / Ruiz, Jon / Quincoces, Iñaki / Dornaika, Fadi / Arganda-Carreras, Ignacio / Fernandes, Jose A.

    Ecological informatics. 2022 Mar., v. 67

    2022  

    Abstract: Fishery monitoring programs are essential for effective management of marine resources, as they provide scientists and managers with the necessary data for both the preparation of scientific advice and fisheries control and surveillance. The monitoring ... ...

    Abstract Fishery monitoring programs are essential for effective management of marine resources, as they provide scientists and managers with the necessary data for both the preparation of scientific advice and fisheries control and surveillance. The monitoring is generally done by human observers, both in port and onboard, with a high cost involved. Consequently, some Regional Fisheries Management Organizations (RFMO) are opting for electronic monitoring (EM) as an alternative or complement to human observers in certain fisheries. This is the case of the tropical tuna purse seine fishery operating in the Indian and Atlantic oceans, which started an EM program on a voluntary basis in 2017. However, even when the monitoring is conducted though EM, the image analysis is a tedious task manually performed by experts. In this paper, we propose a cost-effective methodology for the automatic processing of the images already being collected by cameras onboard tropical tuna purse seiners. Firstly, the images are preprocessed to homogenize them across all vessels and facilitate subsequent steps. Secondly, the fish are individually segmented using a deep neural network (Mask R-CNN). Then, all segments are passed through other deep neural network (ResNet50V2) to classify them by species and estimate their size distribution. For the classification of fish, we achieved an accuracy for all species of over 70%, i.e., about 3 out of 4 individuals are correctly classified to their corresponding species. The size distribution estimates are aligned with official port measurements but calculated using a larger number of individuals. Finally, we also propose improvements to the current image capture systems which can facilitate the work of the proposed automation methodology.
    Keywords automation ; complement ; computer vision ; cost effectiveness ; fisheries ; humans ; image analysis ; monitoring ; tuna
    Language English
    Dates of publication 2022-03
    Publishing place Elsevier B.V.
    Document type Article
    ZDB-ID 2212016-6
    ISSN 1878-0512 ; 1574-9541
    ISSN (online) 1878-0512
    ISSN 1574-9541
    DOI 10.1016/j.ecoinf.2021.101495
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  2. Article ; Online: Avoiding a replication crisis in deep-learning-based bioimage analysis.

    Laine, Romain F / Arganda-Carreras, Ignacio / Henriques, Ricardo / Jacquemet, Guillaume

    Nature methods

    2021  Volume 18, Issue 10, Page(s) 1136–1144

    MeSH term(s) Biomedical Research/methods ; Biomedical Research/standards ; Computational Biology/methods ; Computational Biology/standards ; Deep Learning/standards ; Image Processing, Computer-Assisted/standards ; Microscopy/methods ; Microscopy/standards
    Language English
    Publishing date 2021-10-04
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2169522-2
    ISSN 1548-7105 ; 1548-7091
    ISSN (online) 1548-7105
    ISSN 1548-7091
    DOI 10.1038/s41592-021-01284-3
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  3. Article ; Online: Stable Deep Neural Network Architectures for Mitochondria Segmentation on Electron Microscopy Volumes.

    Franco-Barranco, Daniel / Muñoz-Barrutia, Arrate / Arganda-Carreras, Ignacio

    Neuroinformatics

    2021  Volume 20, Issue 2, Page(s) 437–450

    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 ... ...

    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 .
    MeSH term(s) Humans ; Image Processing, Computer-Assisted/methods ; Microscopy, Electron ; Mitochondria ; Neural Networks, Computer ; Reproducibility of Results
    Language English
    Publishing date 2021-12-02
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2111941-7
    ISSN 1559-0089 ; 1539-2791
    ISSN (online) 1559-0089
    ISSN 1539-2791
    DOI 10.1007/s12021-021-09556-1
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    Jg. 1995 - 2021: Lesesall (2.OG)
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  4. Article ; Online: 3D Domain Adaptive Instance Segmentation via Cyclic Segmentation GANs.

    Lauenburg, Leander / Lin, Zudi / Zhang, Ruihan / Santos, Marcia Dos / Huang, Siyu / Arganda-Carreras, Ignacio / Boyden, Edward S / Pfister, Hanspeter / Wei, Donglai

    IEEE journal of biomedical and health informatics

    2023  Volume 27, Issue 8, Page(s) 4018–4027

    Abstract: 3D instance segmentation for unlabeled imaging modalities is a challenging but essential task as collecting expert annotation can be expensive and time-consuming. Existing works segment a new modality by either deploying pre-trained models optimized on ... ...

    Abstract 3D instance segmentation for unlabeled imaging modalities is a challenging but essential task as collecting expert annotation can be expensive and time-consuming. Existing works segment a new modality by either deploying pre-trained models optimized on diverse training data or sequentially conducting image translation and segmentation with two relatively independent networks. In this work, we propose a novel Cyclic Segmentation Generative Adversarial Network (CySGAN) that conducts image translation and instance segmentation simultaneously using a unified network with weight sharing. Since the image translation layer can be removed at inference time, our proposed model does not introduce additional computational cost upon a standard segmentation model. For optimizing CySGAN, besides the CycleGAN losses for image translation and supervised losses for the annotated source domain, we also utilize self-supervised and segmentation-based adversarial objectives to enhance the model performance by leveraging unlabeled target domain images. We benchmark our approach on the task of 3D neuronal nuclei segmentation with annotated electron microscopy (EM) images and unlabeled expansion microscopy (ExM) data. The proposed CySGAN outperforms pre-trained generalist models, feature-level domain adaptation models, and the baselines that conduct image translation and segmentation sequentially. Our implementation and the newly collected, densely annotated ExM zebrafish brain nuclei dataset, named NucExM, are publicly available at https://connectomics-bazaar.github.io/proj/CySGAN/index.html.
    MeSH term(s) Animals ; Zebrafish ; Benchmarking ; Microscopy ; Image Processing, Computer-Assisted
    Language English
    Publishing date 2023-08-07
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, N.I.H., Extramural
    ZDB-ID 2695320-1
    ISSN 2168-2208 ; 2168-2194
    ISSN (online) 2168-2208
    ISSN 2168-2194
    DOI 10.1109/JBHI.2023.3281332
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    Zs.A 5483: Show issues Location:
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  5. Article ; Online: Designing Image Analysis Pipelines in Light Microscopy: A Rational Approach.

    Arganda-Carreras, Ignacio / Andrey, Philippe

    Methods in molecular biology (Clifton, N.J.)

    2017  Volume 1563, Page(s) 185–207

    Abstract: With the progress of microscopy techniques and the rapidly growing amounts of acquired imaging data, there is an increased need for automated image processing and analysis solutions in biological studies. Each new application requires the design of a ... ...

    Abstract With the progress of microscopy techniques and the rapidly growing amounts of acquired imaging data, there is an increased need for automated image processing and analysis solutions in biological studies. Each new application requires the design of a specific image analysis pipeline, by assembling a series of image processing operations. Many commercial or free bioimage analysis software are now available and several textbooks and reviews have presented the mathematical and computational fundamentals of image processing and analysis. Tens, if not hundreds, of algorithms and methods have been developed and integrated into image analysis software, resulting in a combinatorial explosion of possible image processing sequences. This paper presents a general guideline methodology to rationally address the design of image processing and analysis pipelines. The originality of the proposed approach is to follow an iterative, backwards procedure from the target objectives of analysis. The proposed goal-oriented strategy should help biologists to better apprehend image analysis in the context of their research and should allow them to efficiently interact with image processing specialists.
    Language English
    Publishing date 2017
    Publishing country United States
    Document type Journal Article
    ISSN 1940-6029
    ISSN (online) 1940-6029
    DOI 10.1007/978-1-4939-6810-7_13
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  6. Article ; Online: DL4MicEverywhere: deep learning for microscopy made flexible, shareable and reproducible.

    Hidalgo-Cenalmor, Iván / Pylvänäinen, Joanna W / G Ferreira, Mariana / Russell, Craig T / Saguy, Alon / Arganda-Carreras, Ignacio / Shechtman, Yoav / Jacquemet, Guillaume / Henriques, Ricardo / Gómez-de-Mariscal, Estibaliz

    Nature methods

    2024  

    Language English
    Publishing date 2024-05-17
    Publishing country United States
    Document type Letter
    ZDB-ID 2169522-2
    ISSN 1548-7105 ; 1548-7091
    ISSN (online) 1548-7105
    ISSN 1548-7091
    DOI 10.1038/s41592-024-02295-6
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  7. Book ; Online: Stable deep neural network architectures for mitochondria segmentation on electron microscopy volumes

    Franco-Barranco, Daniel / Muñoz-Barrutia, Arrate / Arganda-Carreras, Ignacio

    2021  

    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 ... ...

    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 do not make neither the code nor the full training details public to support the results obtained, leading to reproducibility issues and dubious model comparisons. For that reason, and following a recent code of best practices for reporting experimental results, we present an extensive study of the state-of-the-art deep learning architectures for the segmentation of mitochondria on EM volumes, and evaluate the impact in performance of different variations of 2D and 3D U-Net-like models for this task. To better understand the contribution of each component, a common set of pre- and post-processing operations has been implemented and tested with each approach. Moreover, an exhaustive sweep of hyperparameters values for all architectures have been performed and each configuration has been run multiple times to report the mean and standard deviation values of the evaluation metrics. Using this methodology, we found very stable architectures and hyperparameter configurations that consistently obtain state-of-the-art results in the well-known EPFL Hippocampus mitochondria segmentation dataset. Furthermore, we have benchmarked our proposed models on two other available datasets, Lucchi++ and Kasthuri++, where they outperform all previous works. The code derived from this research and its documentation are publicly available.
    Keywords Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition
    Subject code 004
    Publishing date 2021-04-08
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: A Comparative Analysis of Human Behavior Prediction Approaches in Intelligent Environments.

    Almeida, Aitor / Bermejo, Unai / Bilbao, Aritz / Azkune, Gorka / Aguilera, Unai / Emaldi, Mikel / Dornaika, Fadi / Arganda-Carreras, Ignacio

    Sensors (Basel, Switzerland)

    2022  Volume 22, Issue 3

    Abstract: Behavior modeling has multiple applications in the intelligent environment domain. It has been used in different tasks, such as the stratification of different pathologies, prediction of the user actions and activities, or modeling the energy usage. ... ...

    Abstract Behavior modeling has multiple applications in the intelligent environment domain. It has been used in different tasks, such as the stratification of different pathologies, prediction of the user actions and activities, or modeling the energy usage. Specifically, behavior prediction can be used to forecast the future evolution of the users and to identify those behaviors that deviate from the expected conduct. In this paper, we propose the use of embeddings to represent the user actions, and study and compare several behavior prediction approaches. We test multiple model (LSTM, CNNs, GCNs, and transformers) architectures to ascertain the best approach to using embeddings for behavior modeling and also evaluate multiple embedding retrofitting approaches. To do so, we use the Kasteren dataset for intelligent environments, which is one of the most widely used datasets in the areas of activity recognition and behavior modeling.
    MeSH term(s) Humans ; Neural Networks, Computer
    Language English
    Publishing date 2022-01-18
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s22030701
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  9. Article ; Online: Deep learning based domain adaptation for mitochondria segmentation on EM volumes.

    Franco-Barranco, Daniel / Pastor-Tronch, Julio / González-Marfil, Aitor / Muñoz-Barrutia, Arrate / Arganda-Carreras, Ignacio

    Computer methods and programs in biomedicine

    2022  Volume 222, Page(s) 106949

    Abstract: Background and objective: Accurate segmentation of electron microscopy (EM) volumes of the brain is essential to characterize neuronal structures at a cell or organelle level. While supervised deep learning methods have led to major breakthroughs in ... ...

    Abstract Background and objective: Accurate segmentation of electron microscopy (EM) volumes of the brain is essential to characterize neuronal structures at a cell or organelle level. While supervised deep learning methods have led to major breakthroughs in that direction during the past years, they usually require large amounts of annotated data to be trained, and perform poorly on other data acquired under similar experimental and imaging conditions. This is a problem known as domain adaptation, since models that learned from a sample distribution (or source domain) struggle to maintain their performance on samples extracted from a different distribution or target domain. In this work, we address the complex case of deep learning based domain adaptation for mitochondria segmentation across EM datasets from different tissues and species.
    Methods: We present three unsupervised domain adaptation strategies to improve mitochondria segmentation in the target domain based on (1) state-of-the-art style transfer between images of both domains; (2) self-supervised learning to pre-train a model using unlabeled source and target images, and then fine-tune it only with the source labels; and (3) multi-task neural network architectures trained end-to-end with both labeled and unlabeled images. Additionally, to ensure good generalization in our models, we propose a new training stopping criterion based on morphological priors obtained exclusively in the source domain. The code and its documentation are publicly available at https://github.com/danifranco/EM_domain_adaptation.
    Results: We carried out all possible cross-dataset experiments using three publicly available EM datasets. We evaluated our proposed strategies and those of others based on the mitochondria semantic labels predicted on the target datasets.
    Conclusions: The methods introduced here outperform the baseline methods and compare favorably to the state of the art. In the absence of validation labels, monitoring our proposed morphology-based metric is an intuitive and effective way to stop the training process and select in average optimal models.
    MeSH term(s) Deep Learning ; Image Processing, Computer-Assisted/methods ; Microscopy, Electron ; Mitochondria ; Neural Networks, Computer
    Language English
    Publishing date 2022-06-14
    Publishing country Ireland
    Document type Journal Article
    ZDB-ID 632564-6
    ISSN 1872-7565 ; 0169-2607
    ISSN (online) 1872-7565
    ISSN 0169-2607
    DOI 10.1016/j.cmpb.2022.106949
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  10. Article: MorphoLibJ: integrated library and plugins for mathematical morphology with ImageJ

    Legland, David / Arganda-Carreras, Ignacio / Andrey, Philippe

    Bioinformatics. 2016 Nov. 15, v. 32, no. 22

    2016  

    Abstract: Motivation: Mathematical morphology (MM) provides many powerful operators for processing 2D and 3D images. However, most MM plugins currently implemented for the popular ImageJ/Fiji platform are limited to the processing of 2D images. Results: The ... ...

    Abstract Motivation: Mathematical morphology (MM) provides many powerful operators for processing 2D and 3D images. However, most MM plugins currently implemented for the popular ImageJ/Fiji platform are limited to the processing of 2D images. Results: The MorphoLibJ library proposes a large collection of generic tools based on MM to process binary and grey-level 2D and 3D images, integrated into user-friendly plugins. We illustrate how MorphoLibJ can facilitate the exploitation of 3D images of plant tissues. Availability and Implementation: MorphoLibJ is freely available at http://imagej.net/MorphoLibJ Contact: david.legland@nantes.inra.fr Supplementary information: Supplementary data are available at Bioinformatics online.
    Keywords bioinformatics ; digital images ; plant tissues
    Language English
    Dates of publication 2016-1115
    Size p. 3532-3534.
    Publishing place Oxford University Press
    Document type Article
    ZDB-ID 1468345-3
    ISSN 1460-2059 ; 1367-4803
    ISSN (online) 1460-2059
    ISSN 1367-4803
    DOI 10.1093/bioinformatics/btw413
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