Crystal search - feasibility study of a real-time deep learning process for crystallization well images.

ABSTRACT: To avoid the time-consuming and often monotonous task of manual inspection of crystallization plates, a Python-based program to automatically detect crystals in crystallization wells employing deep learning techniques was developed. The program uses manually scored crystallization trials deposited in a database of an in-house crystallization robot as a training set. Since the success rate of such a system is able to catch up with manual inspection by trained persons, it will become an important tool for crystallographers working on biological samples. Four network architectures were compared and the SqueezeNet architecture performed best. In detecting crystals AlexNet accomplished a better result, but with a lower threshold the mean value for crystal detection was improved for SqueezeNet.

SUBMITTER: Thielmann Y 

PROVIDER: S-EPMC10317135 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature