NameInstitutionMail AddressSocial Contacts
Luca ClissaINFN Bolognaluca.clissa@bo.infn.itSkype: luca.clissa92; Linkedin:; Medium: ;

How to Obtain Support
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General Information

ML/DL TechnologiesCNN, ResUnet
Science FieldsComputer Vision; Microscopic Fluorescence; Semantic Segmentation; Object Detection; Object Counting



fully annotated; runnable

Software and Tools

Programming LanguagePython
ML Toolset

PyTorch, fastai

Additional librariesskimage, opencv
Suggested Environmentssee requirements and installation instructions

Needed datasets

Data CreatorsLuca Clissa, et al.
Data TypeReal data; fluorescence microscopy images
Data Size414 MB
Data Source

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:

How to execute it

Simply clone the repository 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 notebooks .

Annotated Description


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. Supporting Scientific Research Through Machine and Deep Learning: Fluorescence Microscopy and Operational Intelligence Use Cases. PhD Thesis (2022)

Clissa, L. et al. Fluorescent Neuronal Cells. AMS Acta (2021)

Fluorescent Neuronal Cells dataset – part I, TDS Blog

Fluorescent Neuronal Cells dataset – part II, TDS Blog

Fluorescent Neuronal Cells dataset – part III, TDS Blog

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