Department Imaging Physics
Principal investigator Miriam Menzel
E-mail address m.menzel@tudelft.nl
Website http://menzellab.gitlab.io
Development of a combined fluorescence and scattered light imaging system
Supervisor: Hamed Abbasi, h.abbasi@tudelft.nl
Computational Scattered Light Imaging (ComSLI) exploits the scattering of visible light to reconstruct intricately entangled fibers (nerves, collagen, etc.) and their intersections at micrometer scale.
Whole-slide fluorescence microscopy is a specialized form of fluorescence microscopy that allows for the scanning and digitization of an entire microscope slide with high throughput. It has significant applications in pathology, allowing for the rapid screening and analysis of tissue samples.
To date, no device is capable of measuring both fluorescence and ComSLI simultaneously. In this MEP, spectral filters will be added to the optical path of the ComSLI setup to combine fluorescence and scattered light imaging in one imaging system.
Techniques
- Optical system development (scattered light imaging, fluorescence imaging)
- Automated image acquisition with Python
Further reading
M. Menzel et al. “Scattered Light Imaging: Resolving the substructure of nerve fiber crossings in whole brain sections with micrometer resolution.” NeuroImage 233 (2021): 117952.
Y. Rivenson et al. “Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning.” Nature biomedical engineering 3.6 (2019): 466-477.
Distinguishing muscle, collagen, and nerve fibers in scattered light microscopy data
Computational Scattered Light Imaging is a novel microscopy technique that exploits the scattering of light to reconstruct interwoven fiber networks. Up to now, it has mostly been used to reconstruct nerve fiber pathways in brain tissue. However, it can also be applied to other fibrous structures like muscle or collagen fibers.
In this project, we will study ComSLI measurements of various biological tissue samples, and compare the scattering signals obtained from nerve, muscle, and collagen fibers. In this way, we want to better understand how these different types of fibers and tumor/healthy tissue can be distinguished.
Depending on your interests, the project can be software-focused (using machine learning for tissue classification) or experimentally-focused (modifying measurement parameters for enhancing scattering contrast).
Techniques
- Scattered light microscopy
- Optical imaging
- Signal analysis
- Machine learning
- Python programming
Further reading
https://doi.org/10.1101/2024.03.26.586745
Analyzing nerve fiber sizes in brain tissue samples using scattered light of different wavelengths
Computational Scattered Light Imaging (ComSLI) resolves densely interwoven nerve fibers and their crossings with micrometer resolution, by exploiting scattering of visible light. While other techniques require dedicated equipment and time-consuming raster-scanning, ComSLI can be performed with a simple LED light source and camera.
It is expected that the scattering of light depends on the feature size relative to the wavelength. In this project, we will systematically compare scattering signals obtained from ComSLI measurements with different wavelengths on various brain tissue sections, to better understand how they are related to the underlying nerve fiber sizes, and how these measurements can be used to estimate the fiber sizes. We will compare our results to simulated data and measurements of 3D-nanoprinted tissue phantoms.
Techniques
- Scattered light microscopy (using existing setups)
- Automated signal and image analysis (using ImageJ/Python)
Further reading
Menzel et al. Front. Neuroanat. 15:767223 (2021). DOI: https://doi.org/10.3389/fnana.2021.767223
Confidence map for scattered light imaging measurements of fibrous tissue samples
Currently, measurement results from Computational Scattered Light Imaging (ComSLI) are mostly compared qualitatively because a quality measure is missing. This makes it difficult to optimize measurement parameters or make informed statements about differences between samples. Also, the measured scattering signals are only interpreted by a simple peak-finding algorithm – the strength and clarity of the signals are not taken into account. In this project, we will develop a quality measure to better quantify the results of ComSLI (scattering patterns, line profiles, etc.), and develop a confidence map to indicate how reliable the computed fiber orientations are. We will identify questionable outliers by taking regional information and information from surrounding pixels into account.
Techniques
Signal and image analysis (using ImageJ and Python)
Further reading
Menzel, M., et al. (2021). NeuroImage, 233, 117952. DOI: 10.1016/j.neuroimage.2021.117952.