|Name||Institution||Mail Address||Social Contacts|
|Luca Clissa||INFN Bolognafirstname.lastname@example.org||Skype: luca.clissa92; Linkedin: https://www.linkedin.com/in/luca-clissa-b3908695/; Medium: https://email@example.com ;|
How to Obtain Support
|Social||Skype: ; Linkedin: ; Twitter: ; Hangouts:|
|ML/DL Technologies||CNN, ResUnet|
|Science Fields||Computer Vision; Microscopic Fluorescence; Semantic Segmentation; Object Detection; Object Counting|
fully annotated; runnable
Software and Tools
|Additional libraries||skimage, opencv|
|Suggested Environments||see requirements and installation instructions|
|Data Creators||Luca Clissa, et al.|
|Data Type||Real data; fluorescence microscopy images|
|Data Size||414 MB|
Short Description of the Use Case
Counting objects is a learning task common to many applications, from video surveillance to agriculture 4.0, not to mention studies in life-sciences and medicine. However, this task is typically performed manually by domain experts, becoming very demanding in terms of time and human resources. Also, this increases the chances of errors due to distraction or fatigue.
This use-case deals with an approach to automate recognition and counting of objects in images. Specifically, we frame the problem as a semantic segmentation task and we use the c-Resunet network architecture, taking the Fluorescent Neuronal Cells dataset as a benchmark.
The material is organized into notebooks that cover every stage of a realistic data analysis pipeline. In particular, a great deal of attention is devoted to EDA both to expose the challenges of the dataset and to evaluate the results. Likewise, technical aspects of the fastai implementation are detailed.
For a full course, please check: https://deeplearningitalia.com/corsi/cell-counting-resunet-c0043/
How to execute it
Simply clone the repository https://github.com/clissa/object-counting-ML-INFN and follow the instructions in
installation_instructions.txt to set up your workspace. Then download the data as described in the notebook
01. Exploratory Data Analysis.ipynb. Each step of the analysis is detailed in a dedicated notebook under the folder
- 01. Eploratory Data Analysis.ipynb: instructions for downloading the dataset and setting up the workspace; data exploration: formats, peculiarities and challenges
- 02. fastai building blocks.ipynb: quick start with fastai library; Dataloaders, Learners and training loop
- 03. Experiments - Dice loss.ipynb: model training and experiments
- 04. Results - Visual inspection.ipynb: performance assessment by visual inspection; qualitative evaluation
- 05. Results - Detection & counting performance.ipynb: performance assessment with detection and counting metrics; quantitative evaluation
Morelli, R., Clissa, L., Amici, R. et al. Automating cell counting in fluorescent microscopy through deep learning with c-ResUnet. Sci Rep 11, 22920 (2021).
Clissa, L. et al. Fluorescent Neuronal Cells. AMS Acta (2021)