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Menzel Lab

Department                             Imaging Physics

Principal investigator          Miriam Menzel

E-mail address                       m.menzel@tudelft.nl

Website                                   http://menzellab.gitlab.io

 

Analyzing fiber sizes in biological samples using scattered light of different wavelengths

Suitable as a BEP? No

Suitable as a MEP? Yes

Suitable as an Academic Research Project? No

Techniques:

  • Scattered light imaging measurements
  • Automated signal analysis (using ImageJ/Python)
  • Automated image analysis (using ImageJ/Python)

Computational Scattered Light Imaging (ComSLI) is a recently developed imaging technique that visualizes densely interwoven fiber pathways in biological tissue sections with micrometer resolution, by exploiting the 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. However, until now, the technique reveals only the orientations of fibrous structures; the type of the fiber (nerve, muscle, collagen) or the fiber size remain unknown.

It is expected that the scattering of light depends on the feature size relative to the wavelength. In this project, we will perform ComSLI measurements on different biological samples (containing nerve, muscle, or collagen fibers) with different wavelengths, and systematically compare the resulting scattering signals in order to better understand how these are related to the underlying fiber size. The final goal is to use these measurements to estimate the fiber sizes and distinguish between the different types of fibers.

Further reading (click to link to article)

Menzel, M., et al. (2021). Frontiers in Neuroanatomy, 15, 767223. DOI: 10.3389/fnana.2021.767223.

Development of a combined fluorescence and scattered light imaging system

Suitable as a BEP? No

Suitable as a MEP? Yes

Suitable as an Academic Research Project? No

Techniques:

  • Optical system development
  • Scattered light imaging
  • Fluorescence imaging
  • Automated image acquisition (e.g., using Python)

Computational Scattered Light Imaging (ComSLI) stands as a novel and emerging imaging method capable of distinguishing intricately entangled fibers (nerves, collagen, etc.) and their intersections at a micrometer scale, utilizing the scatter of visible light.

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.
In this project, we will develop an imaging system that combines fluorescence and scattered light imaging.

Further reading

Menzel, M., et al. (2021). NeuroImage, 233, 117952. DOI: 10.1016/j.neuroimage.2021.117952. Rivenson, Y., et al. (2019). Nature biomedical engineering, 3, 466- 477. DOI: 10.1038/s41551-019-0362-y.

Exploiting tissue composition with polarized light scattering

Suitable as a BEP? No

Suitable as a MEP? Yes

Suitable as an Academic Research Project? No

Techniques:

  • Polarization microscopy
  • Scattered light imaging
  • Tissue histology
  • Image analysis

In brain tissue sections, an interesting effect has been observed: Some regions let more light through when the light is polarized parallel to the nerve fibers. Other regions let more light through when the light is polarized perpendicular to the nerve fibers.

In this project, we will study to what degree this effect is caused by scattering and how it can be used to distinguish between different tissue compositions. Apart from brain, we will measure biological tissues with other types of fibers (muscle, collagen) to see if they show similar effects.

Further reading

Menzel, M., et al. (2019) Scientific Reports, 9, 1939. DOI: https://doi.org/10.1038/s41598-019-38506-w

(Example) projects submitted by lab in past years

(2024-2025) 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

 

(2024-2025) 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

 

(2024-2025) 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.