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Large Scale Analysis of Bioimages Using Python
Published on Oct 13, 20141759 Views
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Chapter list
Large Scale Analysis of Bioimages Using Python00:00
Python Has A Good Ecosystem For Data Analysis00:07
Python Has A Growing Ecosystem Of Scientific Packages Around Numpy00:16
The Wider Ecosystem - 100:34
The Wider Ecosystem - 201:44
Multiple Packages Act Together03:22
Modularity Is Good Software Engineering04:00
Consistency Also Helps Human Users04:44
Mahotas: Computer Vision In Python05:58
Untitled08:14
Code18:28
Implementation Is In C++18:34
Full Code For The Function Which Computes The Bounding Box19:04
Result Is Fast, Type-Safe & Flexible21:10
Interfaces Are Hand-Written22:11
Scikit-Image Is Another Good Alternative23:22
Jug For Large Scale Analysis24:47
Jug Use Cases26:19
Jug Tasks28:10
Design Decisions29:14
Jug Execution Loop31:18
Processes Communicate Through Backend32:54
Example (Demo) - 133:58
Example (Demo) - 243:07
Example (Demo) - 343:14
Example (Demo) - 443:22
Example (Demo) - 543:23
Example (Demo) - 643:24
Jugfile Is Like Python Except For Taskgenerator43:53
Some Details44:57
To Run On A Cluster, Use The Cluster Interface46:40
Some More Operations47:20
Example Application48:09
Can We Use A Supervised Approach?51:16
Initial Remarks52:44
Different Submodules Lead To Different Methods55:33
Different Ways To Break Up Image55:56
Different Ways To Compute Features56:40
Image Segmentation58:01
Initial (Raw) Results58:10
Can We Combine The Methods?58:14
Linear Regression For Combination59:42
How Good Is 93 Percent?01:00:41
Comparison Example01:03:57
Label It Twice01:05:19
Cross-Validate By Experiment, Not By Image!01:05:59
Whole Computation Is Managed With Jug01:07:58
Conclusions01:15:05
Acknowledgements01:18:28
Thank You01:19:00