Bacterial colony counting with Convolutional Neural Networks in Digital Microbiology Imaging
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- 作者：Alessandro Ferraria ; bAuthor Vitae ; Stefano LombardiaAuthor Vitae ; Alberto Signoronia ; alberto.signoroni@unibs.it" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae
- 关键词：Convolutional Neural Networks ; Deep learning ; Image classification ; Handcrafted feature extraction ; Image analysis ; Bacterial colony counting ; Digital Microbiology Imaging ; Full Laboratory Automation
- 刊名：Pattern Recognition
- 出版年：2017
- 出版时间：January 2017
- 年：2017
- 卷：61
- 期：Complete
- 页码：629-640
- 全文大小：6005 K
文摘
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Bacterial colony counting on solid agar is an essential but challenging computer vision task in lab automation.
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Comparison between classification solutions for cardinality estimation of colony aggregates is proposed.
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Large and quality dataset (28.5k images) created and fully labeled for training and validation.
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Deep neural network compared to handcrafted feature approach and watershed count.
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Results are unique and relevant in the emerging field of Digital Microbiology Imaging.