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Using textons to rank crystallization droplets by the likely presence of crystals
The visual inspection of crystallization experiments is an important yet time-consuming and subjective step in X-ray crystallography. Previously published studies have focused on automatically classifying crystallization droplets into distinct but ultimately arbitrary experiment outcomes; here, a m...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
International Union of Crystallography
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4188010/ https://www.ncbi.nlm.nih.gov/pubmed/25286854 http://dx.doi.org/10.1107/S1399004714017581 |
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author | Ng, Jia Tsing Dekker, Carien Kroemer, Markus Osborne, Michael von Delft, Frank |
author_facet | Ng, Jia Tsing Dekker, Carien Kroemer, Markus Osborne, Michael von Delft, Frank |
author_sort | Ng, Jia Tsing |
collection | PubMed |
description | The visual inspection of crystallization experiments is an important yet time-consuming and subjective step in X-ray crystallography. Previously published studies have focused on automatically classifying crystallization droplets into distinct but ultimately arbitrary experiment outcomes; here, a method is described that instead ranks droplets by their likelihood of containing crystals or microcrystals, thereby prioritizing for visual inspection those images that are most likely to contain useful information. The use of textons is introduced to describe crystallization droplets objectively, allowing them to be scored with the posterior probability of a random forest classifier trained against droplets manually annotated for the presence or absence of crystals or microcrystals. Unlike multi-class classification, this two-class system lends itself naturally to unidirectional ranking, which is most useful for assisting sequential viewing because images can be arranged simply by using these scores: this places droplets with probable crystalline behaviour early in the viewing order. Using this approach, the top ten wells included at least one human-annotated crystal or microcrystal for 94% of the plates in a data set of 196 plates imaged with a Minstrel HT system. The algorithm is robustly transferable to at least one other imaging system: when the parameters trained from Minstrel HT images are applied to a data set imaged by the Rock Imager system, human-annotated crystals ranked in the top ten wells for 90% of the plates. Because rearranging images is fundamental to the approach, a custom viewer was written to seamlessly support such ranked viewing, along with another important output of the algorithm, namely the shape of the curve of scores, which is itself a useful overview of the behaviour of the plate; additional features with known usefulness were adopted from existing viewers. Evidence is presented that such ranked viewing of images allows faster but more accurate evaluation of drops, in particular for the identification of microcrystals. |
format | Online Article Text |
id | pubmed-4188010 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | International Union of Crystallography |
record_format | MEDLINE/PubMed |
spelling | pubmed-41880102014-10-24 Using textons to rank crystallization droplets by the likely presence of crystals Ng, Jia Tsing Dekker, Carien Kroemer, Markus Osborne, Michael von Delft, Frank Acta Crystallogr D Biol Crystallogr Research Papers The visual inspection of crystallization experiments is an important yet time-consuming and subjective step in X-ray crystallography. Previously published studies have focused on automatically classifying crystallization droplets into distinct but ultimately arbitrary experiment outcomes; here, a method is described that instead ranks droplets by their likelihood of containing crystals or microcrystals, thereby prioritizing for visual inspection those images that are most likely to contain useful information. The use of textons is introduced to describe crystallization droplets objectively, allowing them to be scored with the posterior probability of a random forest classifier trained against droplets manually annotated for the presence or absence of crystals or microcrystals. Unlike multi-class classification, this two-class system lends itself naturally to unidirectional ranking, which is most useful for assisting sequential viewing because images can be arranged simply by using these scores: this places droplets with probable crystalline behaviour early in the viewing order. Using this approach, the top ten wells included at least one human-annotated crystal or microcrystal for 94% of the plates in a data set of 196 plates imaged with a Minstrel HT system. The algorithm is robustly transferable to at least one other imaging system: when the parameters trained from Minstrel HT images are applied to a data set imaged by the Rock Imager system, human-annotated crystals ranked in the top ten wells for 90% of the plates. Because rearranging images is fundamental to the approach, a custom viewer was written to seamlessly support such ranked viewing, along with another important output of the algorithm, namely the shape of the curve of scores, which is itself a useful overview of the behaviour of the plate; additional features with known usefulness were adopted from existing viewers. Evidence is presented that such ranked viewing of images allows faster but more accurate evaluation of drops, in particular for the identification of microcrystals. International Union of Crystallography 2014-09-27 /pmc/articles/PMC4188010/ /pubmed/25286854 http://dx.doi.org/10.1107/S1399004714017581 Text en © Ng et al. 2014 http://creativecommons.org/licenses/by/2.0/uk/ This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited. |
spellingShingle | Research Papers Ng, Jia Tsing Dekker, Carien Kroemer, Markus Osborne, Michael von Delft, Frank Using textons to rank crystallization droplets by the likely presence of crystals |
title | Using textons to rank crystallization droplets by the likely presence of crystals |
title_full | Using textons to rank crystallization droplets by the likely presence of crystals |
title_fullStr | Using textons to rank crystallization droplets by the likely presence of crystals |
title_full_unstemmed | Using textons to rank crystallization droplets by the likely presence of crystals |
title_short | Using textons to rank crystallization droplets by the likely presence of crystals |
title_sort | using textons to rank crystallization droplets by the likely presence of crystals |
topic | Research Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4188010/ https://www.ncbi.nlm.nih.gov/pubmed/25286854 http://dx.doi.org/10.1107/S1399004714017581 |
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