Evaluation of protein crystallization state by sequential image classification

Kawabata, Kuniaki; Saitoh, K; Takahashi, M.; Asama, Hajime; Mishima, Taketoshi; Sugahara, M.; Miyano, Masashi

doi:10.1108/02602280810882607

Cited by 3 publications

(1 citation statement)

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“…Automated crystallization image classifications that attempt to meet the above criteria have been previously attempted. The research laboratories that first automated crystallization inspection quickly realized that image analysis would be a huge problem, and concomitantly developed algorithms to interpret them [17][18][19][20]. None of these programs was ever widely adopted.…”

Section: Introductionmentioning

confidence: 99%

Classification of crystallization outcomes using deep convolutional neural networks

et al. 2018

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The Machine Recognition of Crystallization Outcomes (MARCO) initiative has assembled roughly half a million annotated images of macromolecular crystallization experiments from various sources and setups. Here, state-of-the-art machine learning algorithms are trained and tested on different parts of this data set. We find that more than 94% of the test images can be correctly labeled, irrespective of their experimental origin. Because crystal recognition is key to high-density screening and the systematic analysis of crystallization experiments, this approach opens the door to both industrial and fundamental research applications.

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Section: Introductionmentioning

confidence: 99%

Classification of crystallization outcomes using deep convolutional neural networks

et al. 2018

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Automatic detection of crystallization evaluation region by using image processing

Kawabata

2011

Sensor Review

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Purpose -The purpose of this paper is to propose an imaging process method for automatically extracting assessment target regions in images of crystallization wells. Design/methodology/approach -The proposed method detects the target by image processing based on saturation variance in images of crystallization wells. Findings -The proposed method shows high accurate detection performance in a short time. Practical implications -The method is applicable to automatic crystallization machine. Especially, it is expected to implement to TERA system of RIKEN. Originality/value -The paper demonstrates target area detection from microscope images based on saturation variance in images of crystallization wells.

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