Deep Learning to Detect Bacterial Colonies for the Production of Vaccines
During the development of vaccines, bacterial colony forming units (CFUs) are counted in order to quantify the yield in the fermentation process. This manual task is time-consuming and error-prone. In this work we test multiple segmentation algorithms based on the U-Net CNN architecture and show tha...
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| creator | Beznik, Thomas Smyth, Paul de Lannoy, Gaël Lee, John A |
| description | During the development of vaccines, bacterial colony forming units (CFUs) are
counted in order to quantify the yield in the fermentation process. This manual
task is time-consuming and error-prone. In this work we test multiple
segmentation algorithms based on the U-Net CNN architecture and show that these
offer robust, automated CFU counting. We show that the multiclass
generalisation with a bespoke loss function allows distinguishing virulent and
avirulent colonies with acceptable accuracy. While many possibilities are left
to explore, our results show the potential of deep learning for separating and
classifying bacterial colonies. |
| doi_str_mv | 10.48550/arxiv.2009.00926 |
| format | Article |
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counted in order to quantify the yield in the fermentation process. This manual
task is time-consuming and error-prone. In this work we test multiple
segmentation algorithms based on the U-Net CNN architecture and show that these
offer robust, automated CFU counting. We show that the multiclass
generalisation with a bespoke loss function allows distinguishing virulent and
avirulent colonies with acceptable accuracy. While many possibilities are left
to explore, our results show the potential of deep learning for separating and
classifying bacterial colonies.</description><identifier>DOI: 10.48550/arxiv.2009.00926</identifier><language>eng</language><subject>Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Learning ; Quantitative Biology - Quantitative Methods</subject><creationdate>2020-09</creationdate><rights>http://creativecommons.org/licenses/by-nc-sa/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2009.00926$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2009.00926$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Beznik, Thomas</creatorcontrib><creatorcontrib>Smyth, Paul</creatorcontrib><creatorcontrib>de Lannoy, Gaël</creatorcontrib><creatorcontrib>Lee, John A</creatorcontrib><title>Deep Learning to Detect Bacterial Colonies for the Production of Vaccines</title><description>During the development of vaccines, bacterial colony forming units (CFUs) are
counted in order to quantify the yield in the fermentation process. This manual
task is time-consuming and error-prone. In this work we test multiple
segmentation algorithms based on the U-Net CNN architecture and show that these
offer robust, automated CFU counting. We show that the multiclass
generalisation with a bespoke loss function allows distinguishing virulent and
avirulent colonies with acceptable accuracy. While many possibilities are left
to explore, our results show the potential of deep learning for separating and
classifying bacterial colonies.</description><subject>Computer Science - Computer Vision and Pattern Recognition</subject><subject>Computer Science - Learning</subject><subject>Quantitative Biology - Quantitative Methods</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotz81OwzAQBGBfOKDCA3BiXyDBiRPXe4SUn0qR4FBxjdbpurUU7MoxCN6eUnoYzW00nxA3lSwb07byjtK3_yprKbE8ptaXYr1iPkDPlIIPO8gRVpx5zPBAY-bkaYIuTjF4nsHFBHnP8Jbi9nPMPgaIDt5pHH3g-UpcOJpmvj73QmyeHjfdS9G_Pq-7-74gvdSFslVNrmKUGtFqa7GR7MigodYqw9JskdE4QrJakbNVU9vjU6XRSVySWojb_9mTZTgk_0HpZ_gzDSeT-gWZS0a0</recordid><startdate>20200902</startdate><enddate>20200902</enddate><creator>Beznik, Thomas</creator><creator>Smyth, Paul</creator><creator>de Lannoy, Gaël</creator><creator>Lee, John A</creator><scope>AKY</scope><scope>ALC</scope><scope>GOX</scope></search><sort><creationdate>20200902</creationdate><title>Deep Learning to Detect Bacterial Colonies for the Production of Vaccines</title><author>Beznik, Thomas ; Smyth, Paul ; de Lannoy, Gaël ; Lee, John A</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a676-3b12af1e90699b6bb940efa898a5b38e08d9e98fa9ab63afb142b926369f097a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Computer Science - Computer Vision and Pattern Recognition</topic><topic>Computer Science - Learning</topic><topic>Quantitative Biology - Quantitative Methods</topic><toplevel>online_resources</toplevel><creatorcontrib>Beznik, Thomas</creatorcontrib><creatorcontrib>Smyth, Paul</creatorcontrib><creatorcontrib>de Lannoy, Gaël</creatorcontrib><creatorcontrib>Lee, John A</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv Quantitative Biology</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Beznik, Thomas</au><au>Smyth, Paul</au><au>de Lannoy, Gaël</au><au>Lee, John A</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Deep Learning to Detect Bacterial Colonies for the Production of Vaccines</atitle><date>2020-09-02</date><risdate>2020</risdate><abstract>During the development of vaccines, bacterial colony forming units (CFUs) are
counted in order to quantify the yield in the fermentation process. This manual
task is time-consuming and error-prone. In this work we test multiple
segmentation algorithms based on the U-Net CNN architecture and show that these
offer robust, automated CFU counting. We show that the multiclass
generalisation with a bespoke loss function allows distinguishing virulent and
avirulent colonies with acceptable accuracy. While many possibilities are left
to explore, our results show the potential of deep learning for separating and
classifying bacterial colonies.</abstract><doi>10.48550/arxiv.2009.00926</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Computer Vision and Pattern Recognition Computer Science - Learning Quantitative Biology - Quantitative Methods |
| title | Deep Learning to Detect Bacterial Colonies for the Production of Vaccines |
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