Ishrat Zaman
Ishrat Zaman is a scientist working in the field of medical research as a bioimage analyst. She is passionate about the intersection of neuroscience, image analysis, and open-source software as well as developing analysis pipelines that can address complex scientific and biological questions. With a background in both wet lab-based biology and computational science, Ishrat is passionate about bridging domains, making complex analysis pipelines more accessible, and looking at as many pretty microscopy images as possible.
She/her
Session
How do you reproducibly identify and count individual neurons in a brain region that’s tiny, diffuse, and surrounded by lookalike regions, especially when each 3D brain image is multiple terabytes in size?
This talk explores this very question by diving into the development of a Python-based, end-to-end pipeline for analysing whole mouse brains imaged using light-sheet fluorescence microscopy. The goal is to quantify the number of individual dopaminergic neurons in the substantia nigra pars compacta (SNpc), a small but clinically significant midbrain region implicated in Parkinson’s disease.
Built entirely with open-source Python tools, the workflow combines brainreg (from the BrainGlobe ecosystem) for atlas-based registration, dask for scalable image processing, and a custom-trained Cellpose model for 3D cell segmentation. To address the complexity of region extraction and alignment uncertainty, the pipeline includes parameter sweeps, pre-processing optimisation, and quantitative evaluation using expert-labelled ground truth masks.
This talk will also highlight how the integration of multiple Python open-source packages supports scalable, reproducible neuroimaging analysis, from parallel execution on HPC clusters to image registration and deep learning-based segmentation pipelines, as well as quantitative methods for assessing alignment fidelity.