The early detection of precancerous cervical lesions is essential to improve patient treatment and prognosis. Hyperspectral (HS) imaging (HSI) has demonstrated a high potential to become a new non-invasive and label-free imaging technique in the medical field for performing quick diagnosis of different diseases. This study presents the research and development process to integrate and characterize a KURIOS-XE2 filter (Thorlabs, Inc., NJ, USA), based in a liquid crystal tunable filter (LCTF) technology, into an existing colposcope (C5, OPTOMIC, Spain). The main goal was to capture spectral information in the near infrared range (650 to 1100nm) by using a monochrome camera and acquiring 90 spectral wavelengths with a spectral resolution of 5nm. Two different integration strategies were studied: i) filtering the emitted light by the sensor and ii) filtering the received light by the sensor, evaluating their respective benefits and limitations. Furthermore, a custom software was developed for HS image acquisition, integrating a variable acquisition time per wavelength, which allows improving the signal-to-noise ratio at wavelengths where the system presents lower quantum efficiency. The proposed system simplifies the adaptation of existing optical systems to HSI technology, improving the signal-to-noise ratio in the studied spectral range respect to other approaches. The results were compared against a previous custom implementation based on a Snapscan camera (IMEC, Belgium), covering the visual and near infrared and highlighting the advantages and limitations of both technologies for the development of a HS colposcope system targeting early detection of precancerous cervical lesions during routine clinical practice.
The incidence of skin cancer has increased in the last decades, being one of the most common cancers, but can have a five-year survival rate of over 99% if treated early. This work describes a novel hyperspectral dermoscope for early skin cancer detection, able to capture spatial and spectral information in the Visible (VIS) and Near Infrared (NIR) ranges by using Liquid Crystal Tunable Filters (LCTFs). KURIOS-VB1 and KURIOS-XE2 filters were used for VIS and NIR ranges, respectively, providing 136 wavelengths with 5 nm of spectral resolution. A dichroic mirror combines output light paths, illuminating the skin's surface via a fiber optic ring light. Reflected light is captured by a 1.3-megapixel monochrome camera. Additionally, a custom hand-held 3D printed part integrates optics and control circuitry. The proposed characterization method used to optimize the camera exposure time for each wavelength has proven effective in obtaining a flat white reference and gathering information in the range of 450 to 1050 nm and, especially, at critical wavelengths such as the test wavelengths evaluated closer to the limit bands of the LCFTs (450 and 600 nm for VIS, and 750 and 900 nm for NIR).
KEYWORDS: RGB color model, Tumors, Principal component analysis, Tissues, Cancer detection, Object detection, Visualization, Hyperspectral imaging, Data modeling
The current advances in Whole-Slide Imaging (WSI) scanners allow for more and better visualization of histological slides. However, the analysis of histological samples by visual inspection is subjective and could be challenging. State-of-the-art object detection algorithms can be trained for cell spotting in a WSI. In this work, a new framework for the detection of tumor cells in high-resolution and high-detail using both RGB and Hyperspectral (HS) imaging is proposed. The framework introduces techniques to be trained on partially labeled data, since labeling at the cellular level is a time and energy-consuming task. Furthermore, the framework has been developed for working with RGB and HS information reduced to 3 bands. Current results are promising, showcasing in RGB similar performance as reference works (F1-score = 66.2%) and high possibilities for the integration of reduced HS information into current state-of-art deep learning models, with current results improving the mean precision a 6.3% from synthetic RGB images.
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