Hyperspectral placenta dataset: Hyperspectral image acquisition, annotations, and processing of biological tissues in microsurgical training.

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.

Hyperspectral Imaging in Brain Tumor Surgery-Evidence of Machine Learning-Based Performance.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Optical implementation of partially negative filters using a spectrally tunable light source, and its application to contrast enhanced oral and dental imaging.

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.