ImageJ and CellProfiler: Complements in Open‐Source Bioimage Analysis

2021 ◽  
Vol 1 (5) ◽  
Author(s):  
Ellen T.A. Dobson ◽  
Beth Cimini ◽  
Anna H. Klemm ◽  
Carolina Wählby ◽  
Anne E. Carpenter ◽  
...  
Keyword(s):  
Open Source ◽  
Bioimage Analysis

scholarly journals ImagePy: an open-source, Python-based and platform-independent software package for bioimage analysis

2018 ◽  
Vol 34 (18) ◽  
pp. 3238-3240 ◽  
Author(s):  
Anliang Wang ◽  
Xiaolong Yan ◽  
Zhijun Wei
Keyword(s):  
Open Source ◽  
Software Package ◽  
Bioimage Analysis

scholarly journals QuPath: Open source software for digital pathology image analysis

2017 ◽  
Author(s):  
Peter Bankhead ◽  
Maurice B Loughrey ◽  
José A Fernández ◽  
Yvonne Dombrowski ◽  
Darragh G McArt ◽  
...  
Keyword(s):  
Image Analysis ◽  
Open Source ◽  
Digital Pathology ◽  
Tumor Identification ◽  
Wide Range ◽  
Slide Image ◽  
Bioimage Analysis ◽  
Additional Image ◽  
User Friendly ◽  
Biomarker Evaluation

AbstractQuPath is new bioimage analysis software designed to meet the growing need for a user-friendly, extensible, open-source solution for digital pathology and whole slide image analysis. In addition to offering a comprehensive panel of tumor identification and high-throughput biomarker evaluation tools, QuPath provides researchers with powerful batch-processing and scripting functionality, and an extensible platform with which to develop and share new algorithms to analyze complex tissue images. Furthermore, QuPath’s flexible design makes it suitable for a wide range of additional image analysis applications across biomedical research.

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 Developing open-source software for bioimage analysis: opportunities and challenges

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 302
Author(s):  
Florian Levet ◽  
Anne E. Carpenter ◽  
Kevin W. Eliceiri ◽  
Anna Kreshuk ◽  
Peter Bankhead ◽  
...  
Keyword(s):  
Computer Science ◽  
Open Source ◽  
Computational Methods ◽  
Open Source Software ◽  
Analysis Software ◽  
Efficient Manner ◽  
Bioimage Analysis ◽  
The Common ◽  
Science Labs

Fast-paced innovations in imaging have resulted in single systems producing exponential amounts of data to be analyzed. Computational methods developed in computer science labs have proven to be crucial for analyzing these data in an unbiased and efficient manner, reaching a prominent role in most microscopy studies. Still, their use usually requires expertise in bioimage analysis, and their accessibility for life scientists has therefore become a bottleneck. Open-source software for bioimage analysis has developed to disseminate these computational methods to a wider audience, and to life scientists in particular. In recent years, the influence of many open-source tools has grown tremendously, helping tens of thousands of life scientists in the process. As creators of successful open-source bioimage analysis software, we here discuss the motivations that can initiate development of a new tool, the common challenges faced, and the characteristics required for achieving success.

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.

Open Source

2007 ◽  
Author(s):  
Fadi P. Deek ◽  
James A. M. McHugh
Keyword(s):  
Open Source
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