Bacterial colony counting with Convolutional Neural Networks in Digital Microbiology Imaging
作者：
Highlights：
• Bacterial colony counting on solid agar is an essential but challenging computer vision task in lab automation.
• Comparison between classification solutions for cardinality estimation of colony aggregates is proposed.
• Large and quality dataset (28.5k images) created and fully labeled for training and validation.
• Deep neural network compared to handcrafted feature approach and watershed count.
• Results are unique and relevant in the emerging field of Digital Microbiology Imaging.
摘要
Highlights•Bacterial colony counting on solid agar is an essential but challenging computer vision task in lab automation.•Comparison between classification solutions for cardinality estimation of colony aggregates is proposed.•Large and quality dataset (28.5k images) created and fully labeled for training and validation.•Deep neural network compared to handcrafted feature approach and watershed count.•Results are unique and relevant in the emerging field of Digital Microbiology Imaging.
论文关键词：Convolutional Neural Networks,Deep learning,Image classification,Handcrafted feature extraction,Image analysis,Bacterial colony counting,Digital Microbiology Imaging,Full Laboratory Automation
论文评审过程：Received 30 January 2016, Revised 1 June 2016, Accepted 7 July 2016, Available online 9 July 2016, Version of Record 13 October 2016.
论文官网地址：https://doi.org/10.1016/j.patcog.2016.07.016
