DeepImageJ: Deep learning in bioimage analysis for dummies

2021 ◽  
Author(s):  
Estibaliz Gómez-de-Mariscal
Keyword(s):  
Deep Learning ◽  
Bioimage Analysis

scholarly journals On the objectivity, reliability, and validity of deep learning enabled bioimage analyses

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Dennis Segebarth ◽  
Matthias Griebel ◽  
Nikolai Stein ◽  
Cora R von Collenberg ◽  
Corinna Martin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Signal To Noise Ratio ◽  
Biological Effects ◽  
Reliability And Validity ◽  
Ground Truth ◽  
Training Data ◽  
Model Organisms ◽  
Data Annotation ◽  
Bioimage Analysis ◽  
Model Training

Bioimage analysis of fluorescent labels is widely used in the life sciences. Recent advances in deep learning (DL) allow automating time-consuming manual image analysis processes based on annotated training data. However, manual annotation of fluorescent features with a low signal-to-noise ratio is somewhat subjective. Training DL models on subjective annotations may be instable or yield biased models. In turn, these models may be unable to reliably detect biological effects. An analysis pipeline integrating data annotation, ground truth estimation, and model training can mitigate this risk. To evaluate this integrated process, we compared different DL-based analysis approaches. With data from two model organisms (mice, zebrafish) and five laboratories, we show that ground truth estimation from multiple human annotators helps to establish objectivity in fluorescent feature annotations. Furthermore, ensembles of multiple models trained on the estimated ground truth establish reliability and validity. Our research provides guidelines for reproducible DL-based bioimage analyses.

scholarly journals Tracking cell lineages in 3D by incremental deep learning

2021 ◽  
Author(s):  
Ko Sugawara ◽  
Cagri Cevrim ◽  
Michalis Averof
Keyword(s):  
Deep Learning ◽  
User Interface ◽  
Incremental Learning ◽  
Cell Tracking ◽  
Cell Lineages ◽  
Powerful Approach ◽  
Bioimage Analysis

Deep learning is emerging as a powerful approach for bioimage analysis, but its wider use is limited by the scarcity of annotated data for training. We present ELEPHANT, an interactive platform for cell tracking in 4D that seamlessly integrates annotation, deep learning, and proofreading. ELEPHANT's user interface supports cycles of incremental learning starting from sparse annotations, yielding accurate, user-validated cell lineages with a modest investment in time and effort.

scholarly journals Tackling the challenges of bioimage analysis

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Daniël M Pelt
Keyword(s):  
Deep Learning ◽  
Learning Methods ◽  
Bioimage Analysis

Using multiple human annotators and ensembles of trained networks can improve the performance of deep-learning methods in research.

scholarly journals Avoiding a replication crisis in deep-learning-based bioimage analysis

2021 ◽  
Vol 18 (10) ◽  
pp. 1136-1144
Author(s):  
Romain F. Laine ◽  
Ignacio Arganda-Carreras ◽  
Ricardo Henriques ◽  
Guillaume Jacquemet
Keyword(s):  
Deep Learning ◽  
Replication Crisis ◽  
Bioimage Analysis

scholarly journals Tracking cell lineages in 3D by incremental deep learning

eLife ◽  
2022 ◽  
Vol 11 ◽  
Author(s):  
Ko Sugawara ◽  
Çağrı Çevrim ◽  
Michalis Averof
Keyword(s):  
Deep Learning ◽  
Incremental Learning ◽  
Cell Tracking ◽  
State Of The Art ◽  
Training Data ◽  
Cell Lineages ◽  
Incremental Approach ◽  
Powerful Approach ◽  
Cell Track ◽  
Bioimage Analysis

Deep learning is emerging as a powerful approach for bioimage analysis. Its use in cell tracking is limited by the scarcity of annotated data for the training of deep-learning models. Moreover, annotation, training, prediction, and proofreading currently lack a unified user interface. We present ELEPHANT, an interactive platform for 3D cell tracking that addresses these challenges by taking an incremental approach to deep learning. ELEPHANT provides an interface that seamlessly integrates cell track annotation, deep learning, prediction, and proofreading. This enables users to implement cycles of incremental learning starting from a few annotated nuclei. Successive prediction-validation cycles enrich the training data, leading to rapid improvements in tracking performance. We test the software’s performance against state-of-the-art methods and track lineages spanning the entire course of leg regeneration in a crustacean over 1 week (504 timepoints). ELEPHANT yields accurate, fully-validated cell lineages with a modest investment in time and effort.

scholarly journals Deep learning for bioimage analysis in developmental biology

Development ◽  
2021 ◽  
Vol 148 (18) ◽  
Author(s):  
Adrien Hallou ◽  
Hannah G. Yevick ◽  
Bianca Dumitrascu ◽  
Virginie Uhlmann
Keyword(s):  
Developmental Biology ◽  
Deep Learning ◽  
Open Source ◽  
State Of The Art ◽  
Biological Systems ◽  
Research Project ◽  
Space And Time ◽  
Complex Image ◽  
The Future ◽  
Bioimage Analysis

ABSTRACT Deep learning has transformed the way large and complex image datasets can be processed, reshaping what is possible in bioimage analysis. As the complexity and size of bioimage data continues to grow, this new analysis paradigm is becoming increasingly ubiquitous. In this Review, we begin by introducing the concepts needed for beginners to understand deep learning. We then review how deep learning has impacted bioimage analysis and explore the open-source resources available to integrate it into a research project. Finally, we discuss the future of deep learning applied to cell and developmental biology. We analyze how state-of-the-art methodologies have the potential to transform our understanding of biological systems through new image-based analysis and modelling that integrate multimodal inputs in space and time.

scholarly journals Interactive biomedical segmentation tool powered by deep learning and ImJoy

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 142
Author(s):  
Wei Ouyang ◽  
Trang Le ◽  
Hao Xu ◽  
Emma Lundberg
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Training Data ◽  
Fine Tuning ◽  
Interactive Machine Learning ◽  
Biomedical Image ◽  
Wide Range ◽  
Human Protein Atlas ◽  
Bioimage Analysis ◽  
User Friendly

Deep learning-based methods play an increasingly important role in bioimage analysis. User-friendly tools are crucial for increasing the adoption of deep learning models and efforts have been made to support them in existing image analysis platforms. Due to hardware and software complexities, many of them have been struggling to support re-training and fine-tuning of models which is essential  to avoid  overfitting and hallucination issues  when working with limited training data. Meanwhile, interactive machine learning provides an efficient way to train models on limited training data. It works by gradually adding new annotations by correcting the model predictions while the model is training in the background. In this work, we developed an ImJoy plugin for interactive training and an annotation tool for image segmentation. With a small example dataset obtained from the Human Protein Atlas, we demonstrate that CellPose-based segmentation models can be trained interactively from scratch within 10-40 minutes, which is at least 6x faster than the conventional annotation workflow and less labor intensive. We envision that the developed tool can make deep learning segmentation methods incrementally adoptable for new users and be used in a wide range of applications for biomedical image segmentation.

scholarly journals Open-source deep-learning software for bioimage segmentation

2021 ◽  
Vol 32 (9) ◽  
pp. 823-829
Author(s):  
Alice M. Lucas ◽  
Pearl V. Ryder ◽  
Bin Li ◽  
Beth A. Cimini ◽  
Kevin W. Eliceiri ◽  
...  
Keyword(s):  
Deep Learning ◽  
Open Source ◽  
Open Source Software ◽  
Learning Tools ◽  
Complex Information ◽  
Biological Structures ◽  
Bioimage Analysis ◽  
Learning Software ◽  
Dynamic Relationships ◽  
Microscopy Images

Microscopy images are rich in information about the dynamic relationships among biological structures. However, extracting this complex information can be challenging, especially when biological structures are closely packed, distinguished by texture rather than intensity, and/or low intensity relative to the background. By learning from large amounts of annotated data, deep learning can accomplish several previously intractable bioimage analysis tasks. Until the past few years, however, most deep-learning workflows required significant computational expertise to be applied. Here, we survey several new open-source software tools that aim to make deep-learning–based image segmentation accessible to biologists with limited computational experience. These tools take many different forms, such as web apps, plug-ins for existing imaging analysis software, and preconfigured interactive notebooks and pipelines. In addition to surveying these tools, we overview several challenges that remain in the field. We hope to expand awareness of the powerful deep-learning tools available to biologists for image analysis.

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning
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