EPySeg: a coding-free solution for automated segmentation of epithelia using deep learning

Mapping Intimacies ◽

10.1101/2020.06.30.179507 ◽

2020 ◽

Author(s):

Benoit Aigouy ◽

Benjamin Prud’Homme

Keyword(s):

Deep Learning ◽

Source Coding ◽

Epithelial Tissues ◽

Local Computer ◽

Individual Contributions ◽

Morphogenetic Event ◽

Automated Tools ◽

Python Package ◽

Manual Correction

AbstractEpithelia are dynamic tissues that self-remodel during their development. At morphogenesis, the tissue-scale organization of epithelia is obtained through a sum of individual contributions of the cells constituting the tissue. Therefore, understanding any morphogenetic event first requires a thorough segmentation of its constituent cells. This task, however, usually implies extensive manual correction, even with semi-automated tools. Here we present EPySeg, an open source, coding-free software that uses deep learning to segment epithelial tissues automatically and very efficiently. EPySeg, which comes with a straightforward graphical user interface, can be used as a python package on a local computer, or on the cloud via Google Colab for users not equipped with deep-learning compatible hardware. By alleviating human input in image segmentation, EPySeg accelerates and improves the characterization of epithelial tissues for all developmental biologists.

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EPySeg: a coding-free solution for automated segmentation of epithelia using deep learning

Development ◽

10.1242/dev.194589 ◽

2020 ◽

Vol 147 (24) ◽

pp. dev194589

Author(s):

Benoit Aigouy ◽

Claudio Cortes ◽

Shanda Liu ◽

Benjamin Prud'Homme

Keyword(s):

Deep Learning ◽

Source Coding ◽

Epithelial Tissues ◽

Local Computer ◽

Individual Contributions ◽

Morphogenetic Event ◽

Automated Tools ◽

Python Package ◽

Manual Correction

ABSTRACTEpithelia are dynamic tissues that self-remodel during their development. During morphogenesis, the tissue-scale organization of epithelia is obtained through a sum of individual contributions of the cells constituting the tissue. Therefore, understanding any morphogenetic event first requires a thorough segmentation of its constituent cells. This task, however, usually involves extensive manual correction, even with semi-automated tools. Here, we present EPySeg, an open-source, coding-free software that uses deep learning to segment membrane-stained epithelial tissues automatically and very efficiently. EPySeg, which comes with a straightforward graphical user interface, can be used as a Python package on a local computer, or on the cloud via Google Colab for users not equipped with deep-learning compatible hardware. By substantially reducing human input in image segmentation, EPySeg accelerates and improves the characterization of epithelial tissues for all developmental biologists.

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Seeker: Alignment-free identification of bacteriophage genomes by deep learning

10.1101/2020.04.04.025783 ◽

2020 ◽

Cited By ~ 4

Author(s):

Noam Auslander ◽

Ayal B. Gussow ◽

Sean Benler ◽

Yuri I. Wolf ◽

Eugene V. Koonin

Keyword(s):

Deep Learning ◽

Rapid Detection ◽

Sequence Similarity ◽

Biological Entity ◽

Learning Tool ◽

Alignment Free ◽

User Friendly ◽

Identification And Characterization ◽

Python Package

SummaryAdvances in metagenomics enable massive discovery of diverse, distinct microbes and viruses. Bacteriophages, the most abundant biological entity on Earth, evolve rapidly, and therefore, detection of unknown bacteriophages in sequence datasets is a challenge. The existing methods rely on sequence similarity to known bacteriophage sequences, impeding the identification and characterization of distinct bacteriophage families. We present Seeker, a deep-learning tool for reference-free identification of phage sequences. Seeker allows rapid detection of phages in sequence datasets and clean differentiation of phage sequences from bacterial ones, even for phages with little sequence similarity to established phage families. We comprehensively validate Seeker’s ability to identify unknown phages and employ Seeker to detect unknown phages, some of which are highly divergent from known phage families. We provide a web portal (seeker.pythonanywhere.com) and a user-friendly python package (https://github.com/gussow/seeker) allowing researchers to easily apply Seeker in metagenomic studies, for the detection of diverse unknown bacteriophages.

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Seeker: alignment-free identification of bacteriophage genomes by deep learning

Nucleic Acids Research ◽

10.1093/nar/gkaa856 ◽

2020 ◽

Vol 48 (21) ◽

pp. e121-e121

Author(s):

Noam Auslander ◽

Ayal B Gussow ◽

Sean Benler ◽

Yuri I Wolf ◽

Eugene V Koonin

Keyword(s):

Deep Learning ◽

Sequence Similarity ◽

Detection Methods ◽

Biological Entity ◽

Metagenomic Sequencing ◽

Alignment Free ◽

User Friendly ◽

Identification And Characterization ◽

Python Package

Abstract Recent advances in metagenomic sequencing have enabled discovery of diverse, distinct microbes and viruses. Bacteriophages, the most abundant biological entity on Earth, evolve rapidly, and therefore, detection of unknown bacteriophages in sequence datasets is a challenge. Most of the existing detection methods rely on sequence similarity to known bacteriophage sequences, impeding the identification and characterization of distinct, highly divergent bacteriophage families. Here we present Seeker, a deep-learning tool for alignment-free identification of phage sequences. Seeker allows rapid detection of phages in sequence datasets and differentiation of phage sequences from bacterial ones, even when those phages exhibit little sequence similarity to established phage families. We comprehensively validate Seeker's ability to identify previously unidentified phages, and employ this method to detect unknown phages, some of which are highly divergent from the known phage families. We provide a web portal (seeker.pythonanywhere.com) and a user-friendly Python package (github.com/gussow/seeker) allowing researchers to easily apply Seeker in metagenomic studies, for the detection of diverse unknown bacteriophages.

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pymia: A Python package for data handling and evaluation in deep learning-based medical image analysis

Computer Methods and Programs in Biomedicine ◽

10.1016/j.cmpb.2020.105796 ◽

2021 ◽

Vol 198 ◽

pp. 105796

Author(s):

Alain Jungo ◽

Olivier Scheidegger ◽

Mauricio Reyes ◽

Fabian Balsiger

Keyword(s):

Image Analysis ◽

Deep Learning ◽

Medical Image ◽

Medical Image Analysis ◽

Data Handling ◽

Python Package

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Dynamic Imaging and Characterization of Volatile Aerosols in E-Cigarette Emissions Using Deep Learning-Based Holographic Microscopy

ACS Sensors ◽

10.1021/acssensors.1c00628 ◽

2021 ◽

Author(s):

Yi Luo ◽

Yichen Wu ◽

Liqiao Li ◽

Yuening Guo ◽

Ege Çetintaş ◽

...

Keyword(s):

Deep Learning ◽

Dynamic Imaging ◽

Holographic Microscopy

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Characterization of Optical Coherence Tomography Images for Colon Lesion Differentiation under Deep Learning

Applied Sciences ◽

10.3390/app11073119 ◽

2021 ◽

Vol 11 (7) ◽

pp. 3119

Author(s):

Cristina L. Saratxaga ◽

Jorge Bote ◽

Juan F. Ortega-Morán ◽

Artzai Picón ◽

Elena Terradillos ◽

...

Keyword(s):

Optical Coherence Tomography ◽

Deep Learning ◽

Data Augmentation ◽

Optical Biopsy ◽

Colon Polyps ◽

Optical Coherence ◽

Colon Lesion ◽

Patient Prognosis ◽

B Scan Images

(1) Background: Clinicians demand new tools for early diagnosis and improved detection of colon lesions that are vital for patient prognosis. Optical coherence tomography (OCT) allows microscopical inspection of tissue and might serve as an optical biopsy method that could lead to in-situ diagnosis and treatment decisions; (2) Methods: A database of murine (rat) healthy, hyperplastic and neoplastic colonic samples with more than 94,000 images was acquired. A methodology that includes a data augmentation processing strategy and a deep learning model for automatic classification (benign vs. malignant) of OCT images is presented and validated over this dataset. Comparative evaluation is performed both over individual B-scan images and C-scan volumes; (3) Results: A model was trained and evaluated with the proposed methodology using six different data splits to present statistically significant results. Considering this, 0.9695 (±0.0141) sensitivity and 0.8094 (±0.1524) specificity were obtained when diagnosis was performed over B-scan images. On the other hand, 0.9821 (±0.0197) sensitivity and 0.7865 (±0.205) specificity were achieved when diagnosis was made considering all the images in the whole C-scan volume; (4) Conclusions: The proposed methodology based on deep learning showed great potential for the automatic characterization of colon polyps and future development of the optical biopsy paradigm.

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