Research Thomas De Kerf

Abstract

This project scopes the detection of moisture in historical buildings using hyperspectral imaging technology. Moisture in buildings can originate from wind-driven rain, rising damp, flooding, leaking infrastructure and condensation and periodic changes in moisture content are the main driver for several decay mechanisms. Traditional methods for the detection of moisture, like gravimetric or electrical approaches, are typically labour intensive, invasive and have limited coverage. The development of a hyperspectral scanning system for in-situ applications will allow the detection of anomalies in large-scale structures like buildings. Such anomalies include the presence of water which results in a specific absorption range in the short wave infrared spectrum. The application will capitalize on recent advances in spectral unmixing, to estimate the moisture content of several porous media, including natural stone and brick. The developments will be validated in case studies on pilot sites. This will fundamentally change the methodology of building conservation and restoration, as a more holistic understanding can be developed from whole building images, which will result in more accurate and detailed sampling strategies.

Researcher(s)

• Promoter: De Kock Tim
• Co-promoter: De Kerf Thomas
• Co-promoter: Koirala Bikram
• Fellow: Sunil John

Research team(s)

• Antwerp Cultural Heritage Sciences (ARCHES)

Project type(s)

• Research Project

Abstract

The impending 31% surge in cancer incidences by 2030, coupled with a critical shortage of histopathologists, underscores an urgent need for innovations in diagnostic methodologies. A time-intensive aspect of histopathology is the staining of tissue slices, a pivotal step for disease diagnosis and research. Recently, hyperspectral imaging has been proposed to generate virtual stains on unstained tissues, a technique that could revolutionize tissue analysis. This method promises reduced errors, increased efficiency, multi-staining capabilities, and sample conservation. However, the technique is currently limited by small sample sizes, undefined wavelength band efficacy, and restricted data accessibility. This research project, aims to expand the sample size to 100 slices across four cancer types, employing three different hyperspectral cameras. We will create a comprehensive database, initially using the H&E stain as a reference. The project's second objective is to deploy deep learning algorithms to transform hyperspectral data into virtual stains and to ascertain the most effective wavelength bands. Finally, we aim to share our findings and dataset openly to encourage collaborative advancements. At InVilab, our infrastructure features an extensive array of imaging equipment, including a quantum cascade laser, enhancing our research capabilities in hyperspectral imaging. However, we currently face a shortfall in high-magnification lenses essential for detailed mid-to-long-wave infrared microscopy. An integral component for advancing our research. Securing funding of the BOF SRG will enable the acquisition of these critical lenses. This enhancement is imperative for integrating hyperspectral imaging into clinical practice, offering a strategic solution to the histopathologist shortage and advancing patient care outcomes.

Researcher(s)

• Promoter: De Kerf Thomas

Research team(s)

• Industrial Vision Lab (InViLab)

Project type(s)

• Research Project