Pytri: A multi-weight detection system for biological entities

Mehdi, Ali; Vu, Kieu-Nhi; Lambert, Felix

doi:10.1101/2022.05.11.491558

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…The enumeration of biological entities is time-consuming and prone to inaccuracies when performed manually or with OpenCV-based software. Mehdi et al (2022) proposed an online platform utilizing multiple trained machine-learning weights to detect yeast colonies, bacterial colonies, and melanoma clusters. Their Pytri model achieved median relative error rates of 7.56% for bacterial and yeast colonies on Petri dishes, 6.58% for colonies on 96-well plates, and 10.28% for melanoma cluster microscopy images.…”

Section: Yolov5 For Automated Bacteria Colony Countingmentioning

confidence: 99%

Automated Bacteria Colony Counting using Hybrid Image Segmentation Algorithm and YOLOv5 Transfer Learning Model

Galope,

Lisondra,

Nanual

2024

International Conference on Innovative Practices in Management, Engineering &Amp; Social Sciences

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Bacteria colony counting is a critical process in microbiology research, but manual colony counting remains tedious and error-prone, motivating the need for automation. This study aimed to develop an automated system for accurately detecting and counting E. coli colonies on agar plates. The research objectives were achieved by generating a dataset of E. coli colony images, developing a hybrid image segmentation algorithm, and training a YOLOv5 transfer learning model. The dataset was created by capturing images of E. coli colonies on agar plates under controlled conditions. The cultured agar plates were generated by Davao Oriental State University (DOrSU). A novel framework for a hybrid image segmentation algorithm combining Watershed and Falling-Ball was developed to address the challenge of accurately segmenting colonies from complex backgrounds. The algorithm utilized the output of the Watershed Algorithm to create a binary mask, which the Falling-Ball Algorithm further refined to improve edge detection and fill gaps. The YOLOv5 transfer learning model was trained using the generated dataset to detect and count E. coli colonies. The model achieved a detection accuracy of up to 75%, providing a reliable automated solution for colony counting. Performance evaluation metrics such as precision, recall, and mAP_0.5 were utilized to assess the model's performance. However, training the model using the dataset that underwent the framework could not proceed due to its resource-intensive requirements.

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Section: Yolov5 For Automated Bacteria Colony Countingmentioning

confidence: 99%

Automated Bacteria Colony Counting using Hybrid Image Segmentation Algorithm and YOLOv5 Transfer Learning Model

Galope,

Lisondra,

Nanual

2024

International Conference on Innovative Practices in Management, Engineering &Amp; Social Sciences

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“…Image processing has further been used as an alternative method in solving other specific biological challenges such as counting mammalian cell colonies [10,11] or monitoring the growth of cancer cells [12]. The application and correct integration of mathematical algorithms, boundary selection, filters distance transforms, segmentation, pattern recognition and object labelling can modify a digital image to extract valuable information from a region of interest for a very specific purpose [5,7,13,14]. Some of the outlined existent solutions exhibit excellent accuracy results and are seen as potential solutions that contribute to the laboratory automation and efficiency.…”

Section: Introductionmentioning

confidence: 99%

Image Analysis Semi-Automatic System for Colony-Forming-Unit Counting

2022

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Background: Accurate quantitative analysis of microorganisms is recognized as an essential tool for gauging safety and quality in microbiology settings in a wide range of fields. The enumeration process of viable microorganisms via traditional culturing techniques are methodically convenient and cost-effective, conferring high applicability worldwide. However, manual counting can be time-consuming, laborious and imprecise. Furthermore, particular cases require an urgent and accurate response for effective processing. Methods: To reduce time limitations and discrepancies, this work introduces an image processing method capable of semi-automatically quantifying the number of colony forming units (CFUs). This rapid enumeration technique enables the technician to provide an expeditious assessment of the microbial load of a given sample. To test and validate the system, three bacterial species were cultured, and a labeled database was created, with subsequent image acquisition. Results: The system demonstrated acceptable classification measures; the mean values of Accuracy, Recall and F-measure were: (1) 95%, 95% and 0.95 for E. coli; (2) 91%, 91% and 0.90 for P. aeruginosa; and (3) 84%, 86% and 0.85 for S. aureus. Conclusions: Evidence related to the time-saving potential of the system was achieved; the time spent on quantification tasks of plates with a high number of colonies might be reduced to a half and occasionally to a third.

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Pytri: A multi-weight detection system for biological entities

Cited by 2 publications

References 16 publications

Automated Bacteria Colony Counting using Hybrid Image Segmentation Algorithm and YOLOv5 Transfer Learning Model

Automated Bacteria Colony Counting using Hybrid Image Segmentation Algorithm and YOLOv5 Transfer Learning Model

Image Analysis Semi-Automatic System for Colony-Forming-Unit Counting

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