Hyperspectral imaging (HSI) has the potential to enhance surgical tissue detection and diagnostics. Definite utilization of intraoperative HSI guidance demands validated machine learning and public datasets that currently do not exist. Moreover, current imaging conventions are dispersed, and evidence-based paradigms for neurosurgical HSI have not been declared. We presented the rationale and a detailed clinical paradigm for establishing microneurosurgical HSI guidance. In addition, a systematic literature review was conducted to summarize the current indications and performance of neurosurgical HSI systems, with an emphasis on machine learning-based methods. The published data comprised a few case series or case reports aiming to classify tissues during glioma operations. For a multitissue classification problem, the highest overall accuracy of 80% was obtained using deep learning. Our HSI system was capable of intraoperative data acquisition and visualization with minimal disturbance to glioma surgery. In a limited number of publications, neurosurgical HSI has demonstrated unique capabilities in contrast to the established imaging techniques. Multidisciplinary work is required to establish communicable HSI standards and clinical impact. Our HSI paradigm endorses systematic intraoperative HSI data collection, which aims to facilitate the related standards, medical device regulations, and value-based medical imaging systems.

The most common imaging methods used in dentistry are X-ray imaging and RGB color photography. However, both imaging methods provide only a limited amount of information on the wavelength-dependent optical properties of the hard and soft tissues in the mouth. Spectral imaging, on the other hand, provides significantly more information on the medically relevant dental and oral features (e.g. caries, calculus, and gingivitis). Due to this, we constructed a spectral imaging setup and acquired 316 oral and dental reflectance spectral images, 215 of which are annotated by medical experts, of 30 human test subjects. Spectral images of the subjects’ faces and other areas of interest were captured, along with other medically relevant information (e.g., pulse and blood pressure). We collected these oral, dental, and face spectral images, their annotations and metadata into a publicly available database that we describe in this paper. This oral and dental spectral image database (ODSI-DB) provides a vast amount of data that can be used for developing, e.g., pattern recognition and machine vision applications for dentistry.

In optical imaging, optical filters can be used to enhance the visibility of features-of-interest and thus aid in visualization. Optical filter design based on hyperspectral imaging employs various statistical methods to find an optimal design. Some methods, like principal component analysis, produce vectors that can be interpreted as filters that have a partially negative transmission spectrum. These filters, however, are not directly implementable optically. Earlier implementations of partially negative filters have concentrated on spectral reconstruction. Here we show a novel method for implementing partially negative optical filters for contrast-enhancement purposes in imaging applications. We describe the method and its requirements, and show its feasibility with color chart and dental imaging examples. The results are promising: visual comparison of computational color chart render and optical measurement show matching images, and visual inspection of dental images show increased contrast.

The aim of this work is automatic and efficient detection of medically-relevant features from oral and dental hyperspectral images by applying up-to-date deep learning convolutional neural network techniques. This will help dentists to identify and classify unhealthy areas automatically and to prevent the progression of diseases. Hyperspectral imaging approach allows one to do so without exposing the patient to ionizing X-ray radiation. Spectral imaging provides information in the visible and near-infrared wavelength ranges. The dataset used in this paper contains 116 hyperspectral images from 18 patients taken from different viewing angles. Image annotation (ground truth) includes 38 classes in six different sub-groups assessed by dental experts. Mask region-based convolutional neural network (Mask R-CNN) is used as a deep learning model, for instance segmentation of areas. Preliminary results show high potential and accuracy for classification and segmentation of different classes.

Clinically interesting low-contrast dental and oral features can be challenging to detect. In visual observation and clinical photographs, identification of low-contrast features can be hard or even impossible. Imaging methods, e.g., X-ray and magnetic resonance imaging, provide more information but often require use of ionizing radiation, expensive equipment, and specialized personnel to operate the devices. A cost-effective, non-ionizing, contrast-enhancing imaging method that can be used at any dental clinic is in great demand. Here we show a dental and oral feature visibility-enhancement based on a portable spectral camera and computational filters derived from principal component analysis. By applying computational filters on oral and dental spectral images, selected features of clinical interest can be highlighted against their surroundings. Due to the lack of information available in standard color images, this visibility-enhancement technique can only be realized using spectral images. Oral and dental spectral imaging does not use ionizing radiation, and modern spectral cameras are small, portable, and can be used without specialized training. In this paper, spectral image-based visibility-enhancement is demonstrated for the following cases: gingival recession, calculus, gingivitis, root caries, secondary caries, Fordyce’s granules, leukoplakia, and pigmentous lesions. The results gained with spectral images and computational filters from principal component analysis are compared against regular color images and grayscale images computed with band-pass filters from our earlier work. The results are promising as the visibility and contrast of the features of interests are enhanced in all the studied cases. This study provides a starting point for future research and demonstrates the applicability of spectral imaging-based methods for practical use at dental clinics.

The dataset consists of 101 hyperspectral images of four human placentas and six hyperspectral images of contrast dyes (i.e., indocyanine green and red and blue food colorant) that were captured in the range 515–900 nm, step = 5 nm. The hyperspectral images were manually annotated, delineating the key anatomical structures: arteries, veins, stroma, and the umbilical cord. Standard reference materials were used for flat-field correction. The dataset is instrumental for advancing machine-learning algorithms and automated classification of anatomical structures, particularly the classification of superficial and deep vessels and transparent tissue layers.