Optimal processing for gel electrophoresis images: Applying Monte Carlo Tree Search in GelApp

In biomedical research, gel band size estimation in electrophoresis analysis is a routine process. To facilitate and automate this process, numerous software have been released, notably the GelApp mobile app. However, the band detection accuracy is limited due to a band detection algorithm that cannot adapt to the variations in input images. To address this, we used the Monte Carlo Tree Search with Upper Confidence Bound (MCTS-UCB) method to efficiently search for optimal image processing pipelines for the band detection task, thereby improving the image processing algorithm. Incorporating this into GelApp, we report a significant enhancement of gel band detection accuracy by 55.9±2.0% for protein polyacrylamide gels, and 35.9±2.5% for DNA SYBR green agarose gels. This implementation is a proof-of-concept in demonstrating MCTS-UCB as a strategy to optimise general image segmentation. The improved version of GelApp - GelApp 2.0 - is freely available on both Google Play Store (for Android platform), and Apple App Store (for iOS platform).

type: 
Journal Paper
journal: 
Electrophoresis 2016, 10.1002/elps.201600197
Url: 
http://onlinelibrary.wiley.com/doi/10.1002/elps.201600197/full
Impact Factor: 
3.028
Date of acceptance: 
2016-05-23