1. A Deep Learning Approach to Bacterial Colony Segmentation
Paolo Andreini, Simone Bonechi, Monica Bianchini, Alessandro Mecocci, Franco Scarselli
DIISM, Department of Information Engineering and Mathematics
University of Siena
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2. Outline Petri Plate Analysis Synthetic Data Generation
Bacterial Segmentation
Results
Conclusions and Future Perspectives
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3. Petri Plate Analysis – Standard Protocol
Cylindrical glass or plastic dish used to culture cells
Filled with a growing media designed to allow the bacterial growth
Food and beverage safety, environmental control, clinical analyses, etc.
Sample
Seeding
Incubation
Visual analysis
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4. Petri Plate Analysis - Why bother?
Hundreds of plates each day in big labs, e.g in Careggi Hospital, Florence, urine samples only
Highly repetitive and prone to error
Costly in term of both time and resources
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5. Automatic Petri Plate Analysis
Automatic Petri Plate Analysis Pipeline [1]:
Image Acquisition
Bacterial Segmentation
Colony Count
Bacterial Classification
[1] P. Andreini, S. Bonechi, M. Bianchini, A. Garzelli, A. Mecocci. Automatic Image Classification for the Urinoculture Screening. Computers in Biology and Medicine, vol. 70, pp. 12–22, 2016
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6. Bacterial Segmentation
CNN
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7. CNNs Need Data
A unique existing public dataset labeled for segmentation - Haemolysis Dataset [2]:
Only one type of culturing substrate
Only few hundreds annotated data
But…
[2] M. Savardi, A. Ferrari, A. Signoroni. Automatic hemolysis identification on aligned dual–lighting images of cultured blood agar plates. Computer Methods and Programs in Biomedicine, vol. 156, pp. 13–24, 2018
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8. Synthetic Data Generation
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9. Streaking Simulation
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10. Colony Modeling
𝑚 𝑥,𝑦 = 𝑝 𝑥,𝑦 − argmin 𝑟 𝑘 ( 𝑝 𝑥,𝑦 − 𝑟 𝑘 )
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11. Opacity Simulation
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12. Rendering and Blending
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13. Advantages Suitable for different seeding path
Just need different probability matrix
Suitable for different growing media and colony types
Just need new colony patches
Free Annotations
Generator
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14. Examples of Synthetic Plates
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15. Bacterial Segmentation
PSP-Network [3] based on ResNet with 50 layers
Syntetic dataset: synthetic images ( training and 1000 validation)
MicrobIA Dataset: 324 images (221 training and 103 test images)
Experimental setups:
Training on synthetic data only
Training on real data from the MicrobIA dataset only
Training on synthetic data and fine–tuning on real data from the MicrobIA dataset
[3] H. Zhao, J. Shi, X. Qi, X. Wang, J. Jia. Pyramid Scene Parsing Network. Proc. of CVPR ’17 pp. 6230–6239, 2017.
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16. Results Experimental Setup Mean IoU Pixel Accuracy Synthetic Images
82,79 %
98,29 %
Real Images
85,30 %
99,19 %
Synthetic and Real Images
86,33 %
99,26 %
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17. Real and Synthetic Images
Results
Original Image
Train on
Synthetic Images
Train on
Real Images
Train on both
Real and Synthetic Images
Target
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18. Conclusions and Future Perspectives
The proposed synthetic image generator allows to:
Create images that resemble the originals
Create large datasets for bacterial segmentation
Train a deep neural network even if a public dataset of real images is not available
Future Perspectives
Better estimation of the seeding distribution
Use of Generative Adversarial Networks to improve the quality of the simulated images
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