Synopsis
Table of Contents. 1
Preface
1 Introduction. 1
1.1 What is image processing pipeline?. 1
1.2 What does web image processing pipeline consist of?. 3
1.3 What are big data microscopy experiments?. 4
1.4 Why are scientists interested in big data microscopy experiments?. 6
1.5 What is the range of applications leveraging image processing pipelines?. 91.6 Challenges of big data microscopy experiments. 10
1.7 Tradeoffs before and after digital images are acquired. 12
1.8 Enabling reproducible science from big data microscopy experiments. 14
2 Using Web Image Processing Pipeline for Big Data Microscopy Experiments. 1
2.1 Deploying and Testing the Web Image Processing Pipeline. 2
2.1.1 Types of deployment 4
2.1.2 Deployment of Docker Containers. 62.1.3 Deployment recommendations. 7
2.1.4 Test data and computational benchmarks. 8
2.2 Web Image Processing. 10
2.2.1 WIP processing functionality. 10
2.2.2 Examples of WIP usage. 12
2.3 Web Feature Extraction. 15
2.3.1 WFE processing functionality. 172.3.2 WFE usage. 19
2.4 Web Statistical Modeling. 21
2.4.1 WSM processing functionality. 232.4.2 WSM use case. 24
2.5 Summary. 25
3 Example Use Cases 1
3.1 Cell count and single cell detection. 1
3.1.1 Image processing pipeline. 2
3.1.2 Create a new image collection. 3
3.1.3 Stitching of image tiles. 4
3.1.4 Intensity scaling and pyramid building. 5
3.1.5 Image assembling. 6
3.1.6 Segmentation. 7
3.1.7 Binary image labeling. 8
3.1.8 Feature extraction and single cell detection. 8 3.1.9 Discussion. 93.2 Stem cell colony growth computation. 10
3.2.1 Image processing pipeline. 11
3.2.2 Colony tracking and feature extraction<. 12
3.2.3 Discussion. 13
3.3 Summary. 15
4 Building Web Image Processing Pipeline for Big Images. 1
4.1 Mapping functionality to information technologies. 14.2 The role of each technology in the client-server architecture. 5
4.3 &nbs
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