School of Bioinformatics · AI & machine learning
Bioimage Analysis: Microscopy Images with Fiji & Python
Microscopy produces images; experiments need measurements. This course teaches how to segment, measure and quantify cells and structures reproducibly, from a single image in Fiji to thousands of images in an automated pipeline.
Course overview
Bioimage Analysis covers the full path from image acquisition choices to statistics: file formats and metadata, preprocessing, thresholding and segmentation, measurement, and the checks that keep an image analysis honest.
You build macros in Fiji, high-throughput pipelines in CellProfiler and Python workflows with scikit-image, and use pretrained deep-learning models (Cellpose and StarDist) for difficult segmentation. Every lab ends with a reproducible script, not a series of clicks.
Learning outcomes
By the end of the course you will be able to:
- Handle microscopy formats and metadata correctly
- Preprocess images and segment nuclei, cells and organelles
- Measure intensity, morphology and counts reproducibly
- Automate analysis with Fiji macros, CellProfiler and Python
- Apply pretrained deep-learning segmentation and check its quality
Skills you will practise
Eligibility and prerequisites
Who can apply
- Students and graduates of B.Sc., M.Sc., B.Tech., M.Tech., B.Pharm., M.Pharm., MBBS, BDS, B.V.Sc. or a PhD programme in the life sciences, biotechnology, bioinformatics, pharmacy, medicine, agriculture, chemistry, statistics or computer science
- Researchers, faculty and industry professionals moving into data-driven biology
- Final-year students may apply before their results are declared
Prerequisites
- Basic cell biology
- Python basics are helpful but not required for the Fiji modules
Best suited to
- Cell biologists, microbiologists and neuroscientists who image their samples
- Imaging-facility staff
- Students moving into computational cell biology
Module-wise syllabus
6 modules. Each pairs instructor-led sessions with a hands-on lab on public data.
Topics
- Pixels, bit depth and channels
- Formats, metadata and Bio-Formats
- Acquisition choices that affect analysis
Topics
- Background correction and filtering
- Thresholding and watershed
- Segmentation quality
Topics
- Intensity, area and shape features
- Colocalisation and its pitfalls
- Counting and tracking basics
Topics
- Fiji macros
- CellProfiler pipelines
- Python with scikit-image
Topics
- Cellpose and StarDist
- When to retrain
- Validating against ground truth
Topics
- Pipeline
- Statistics
- Figure and methods
Practical sessions and projects
Capstone project
Quantify an imaging experiment
Build a reproducible pipeline that segments and measures cells across a public image set, and report the result with quality checks and statistics.
Assessed on the correctness of the analysis, the quality of the code and the reasoning in the write-up. A pass is required for the certificate.
- Images as data
Inspect a multi-channel fluorescence dataset and document the metadata needed for quantification.
- Preprocessing and segmentation
Segment nuclei in Fiji and compare automatic thresholds against manual annotation.
- Measurement
Measure per-cell intensity in two conditions and test the difference statistically.
- Automation at scale
Build a pipeline that processes a full plate of images and exports tidy per-cell tables.
- Deep-learning segmentation
Segment crowded cells with Cellpose and score the result against annotated masks.
- Project: a quantified imaging experiment
Analyse a public imaging dataset end to end and report the pipeline, measurements and statistics.
Software, tools and datasets
Software and tools
All software is free and open source or free for academic use. Recommended computer: 8 GB RAM, 30 GB free disk, Linux, macOS or Windows with WSL2.
Datasets
- Broad Bioimage Benchmark Collection: Segmentation and counting with ground truth.bbbc.broadinstitute.org
- Image Data Resource studies: High-content screening analysis.idr.openmicroscopy.org
Core reading
- Schindelin J et al. (2012). Fiji: an open-source platform for biological-image analysis. Nature Methods 9:676–682.
- Stringer C, Wang T, Michaelos M, Pachitariu M (2021). Cellpose: a generalist algorithm for cellular segmentation. Nature Methods 18:100–106.
Duration, format and fees
- Next cohort
- Dates are sent with your admission offer
- Timetable
- Three evening or weekend sessions a week (IST), plus a lab clinic; intensive and weekend-only schedules on request
- Payment
- Secure online payment link with your admission offer (UPI, cards, net banking). Instalments on request for fees above ₹15,000.
- Refunds
- See the refund and cancellation policy.
Instructor
The instructor for each cohort is named in your admission offer and on this page when the cohort is announced. Vedicgen instructors are selected for hands-on research or industry experience in the subject they teach, and every course is reviewed by a second subject expert.
Questions about who teaches a cohort? [email protected]
Certificate
Learners who complete the course and pass its capstone receive a Vedicgen certificate of completion for Bioimage Analysis: Microscopy Images with Fiji & Python, issued by Vivofold, stating the guided hours and skills assessed. Each certificate has a unique ID and QR code that anyone can check.
Apply for Bioimage Analysis: Microscopy Images with Fiji & Python
Apply
Send the form below. It takes about five minutes.
Review
An adviser confirms your fit and format within two working days.
Offer
You receive dates, timetable and a secure payment link.
Onboard
Set-up guide, pre-reading and joining links.
Frequently asked questions
In the live cohort, modules are taught in instructor-led sessions with a weekly lab clinic where you work on real public data with help at hand; every session is recorded. The self-paced format uses the same lessons, labs and datasets with mentor review of your capstone. Both formats end with an assessed project reviewed for the code and the science.
A laptop with at least 8 GB of RAM and about 30 GB of free disk space, running Linux, macOS, or Windows with WSL2. Fiji and CellProfiler run on Windows, macOS and Linux. Installation instructions are sent before the first session, and lab clinics help with any set-up problems.
Yes. Learners who complete the course and pass its assessment receive a Vedicgen certificate of completion, issued by Vivofold. Every certificate carries a unique ID and a QR code that anyone, including an employer or admissions office, can check on the certificate verification page.
Apply with the form on this page. Live and in-person cohorts run through the year; the dates, timetable and joining details of the next cohort are sent with your admission offer and listed on this page once announced. Self-paced enrolment opens as soon as your application is accepted.
Yes. No course runs longer than four weeks, and the length is customisable: choose the standard schedule, an intensive one- to two-week schedule, weekends only, or a custom timetable. Pick your preference on the application form; colleges and teams can book their own dates.
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