Delineation of malignant skin tumors by hyperspectral imaging using diffusion maps dimensionality reduction

Hyperspectral imaging with machine learning for in vivo skin carcinoma margin assessment: a preliminary study

Surgical excision is the most effective treatment of skin carcinomas (basal cell carcinoma or squamous cell carcinoma). Preoperative assessment of tumoral margins plays a decisive role for a successful result. The aim of this work was to evaluate the possibility that hyperspectral imaging could become a valuable tool in solving this problem. Hyperspectral images of 11 histologically diagnosed carcinomas (six basal cell carcinomas and five squamous cell carcinomas) were acquired prior clinical evaluation and surgical excision. The hyperspectral data were then analyzed using a newly developed method for delineating skin cancer tumor margins. This proposed method is based on a segmentation process of the hyperspectral images into regions with similar spectral and spatial features, followed by a machine learning-based data classification process resulting in the generation of classification maps illustrating tumor margins. The Spectral Angle Mapper classifier was used in the data classification process using approximately 37% of the segments as the training sample, the rest being used for testing. The receiver operating characteristic was used as the method for evaluating the performance of the proposed method and the area under the curve as a metric. The results revealed that the performance of the method was very good, with median AUC values of 0.8014 for SCCs, 0.8924 for BCCs, and 0.8930 for normal skin. With AUC values above 0.89 for all types of tissue, the method was considered to have performed very well. In conclusion, hyperspectral imaging can become an objective aid in the preoperative evaluation of carcinoma margins.

Hyperspectral imaging to accurately segment skin erythema and hyperpigmentation in cutaneous chronic graft-versus-host disease

In 51 lesions from 15 patients with the inflammatory skin condition chronic graft-versus-host-disease, hyperspectral imaging accurately delineated active erythema and post-inflammatory hyperpigmentation. The method was validated by dermatologist-approved confident delineations of only definitely affected and definitely unaffected areas in photographs. A prototype hyperspectral imaging system acquired a 2.5 × 3.5 cm2 area of skin at 120 wavelengths in the 450-850 nm range. Unsupervised extraction of unknown absorbers by endmember analysis achieved a comparable accuracy to that of supervised extraction of known absorbers (melanin, hemoglobin) by chromophore mapping: 0.78 (IQR: 0.39-0.85) vs. 0.83 (0.53-0.91) to delineate erythema and 0.74 (0.57-0.87) vs. 0.73 (0.52-0.84) to delineate hyperpigmentation. Both algorithms achieved higher specificity than sensitivity. Whereas a trained human confidently marked a median of 7% of image pixels, unsupervised and supervised algorithms delineated a median of 14% and 27% pixels. Hyperspectral imaging could overcome a fundamental practice gap of distinguishing active from inactive manifestations of inflammatory skin disease.

Estimating quantitative physiological and morphological tissue parameters of murine tumor models using hyperspectral imaging and optical profilometry

Understanding tumors and their microenvironment are essential for successful and accurate disease diagnosis. Tissue physiology and morphology are altered in tumors compared to healthy tissues, and there is a need to monitor tumors and their surrounding tissues, including blood vessels, non-invasively. This preliminary study utilizes a multimodal optical imaging system combining hyperspectral imaging (HSI) and three-dimensional (3D) optical profilometry (OP) to capture hyperspectral images and surface shapes of subcutaneously grown murine tumor models. Hyperspectral images are corrected with 3D OP data and analyzed using the inverse-adding doubling (IAD) method to extract tissue properties such as melanin volume fraction and oxygenation. Blood vessels are segmented using the B-COSFIRE algorithm from oxygenation maps. From 3D OP data, tumor volumes are calculated and compared to manual measurements using a vernier caliper. Results show that tumors can be distinguished from healthy tissue based on most extracted tissue parameters ( p < 0.05 ). Furthermore, blood oxygenation is 50% higher within the blood vessels than in the surrounding tissue, and tumor volumes calculated using 3D OP agree within 26% with manual measurements using a vernier caliper. Results suggest that combining HSI and OP could provide relevant quantitative information about tumors and improve the disease diagnosis.

Hyperspectral and multispectral image processing for gross-level tumor detection in skin lesions: a systematic review

SIGNIFICANCE

Skin cancer is one of the most prevalent cancers worldwide. In the advent of medical digitization and telepathology, hyper/multispectral imaging (HMSI) allows for noninvasive, nonionizing tissue evaluation at a macroscopic level.

AIM

We aim to summarize proposed frameworks and recent trends in HMSI-based classification and segmentation of gross-level skin tissue.

APPROACH

A systematic review was performed, targeting HMSI-based systems for the classification and segmentation of skin lesions during gross pathology, including melanoma, pigmented lesions, and bruises. The review adhered to the 2020 Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. For eligible reports published from 2010 to 2020, trends in HMSI acquisition, preprocessing, and analysis were identified.

RESULTS

HMSI-based frameworks for skin tissue classification and segmentation vary greatly. Most reports implemented simple image processing or machine learning, due to small training datasets. Methodologies were evaluated on heavily curated datasets, with the majority targeting melanoma detection. The choice of preprocessing scheme influenced the performance of the system. Some form of dimension reduction is commonly applied to avoid redundancies that are inherent in HMSI systems.

CONCLUSIONS

To use HMSI for tumor margin detection in practice, the focus of system evaluation should shift toward the explainability and robustness of the decision-making process.

FPI Based Hyperspectral Imager for the Complex Surfaces—Calibration, Illumination and Applications

Hyperspectral imaging (HSI) applications for biomedical imaging and dermatological applications have been recently under research interest. Medical HSI applications are non-invasive methods with high spatial and spectral resolution. HS imaging can be used to delineate malignant tumours, detect invasions, and classify lesion types. Typical challenges of these applications relate to complex skin surfaces, leaving some skin areas unreachable. In this study, we introduce a novel spectral imaging concept and conduct a clinical pre-test, the findings of which can be used to develop the concept towards a clinical application. The SICSURFIS spectral imager concept combines a piezo-actuated Fabry–Pérot interferometer (FPI) based hyperspectral imager, a specially designed LED module and several sizes of stray light protection cones for reaching and adapting to the complex skin surfaces. The imager is designed for the needs of photometric stereo imaging for providing the skin surface models (3D) for each captured wavelength. The captured HS images contained 33 selected wavelengths (ranging from 477 nm to 891 nm), which were captured simultaneously with accordingly selected LEDs and three specific angles of light. The pre-test results show that the data collected with the new SICSURFIS imager enable the use of the spectral and spatial domains with surface model information. The imager can reach complex skin surfaces. Healthy skin, basal cell carcinomas and intradermal nevi lesions were classified and delineated pixel-wise with promising results, but further studies are needed. The results were obtained with a convolutional neural network.

Spectrally Tunable Neural Network-Assisted Segmentation of Microneurosurgical Anatomy

Background

Distinct tissue types are differentiated based on the surgeon’s knowledge and subjective visible information, typically assisted with white-light intraoperative imaging systems. Narrow-band imaging (NBI) assists in tissue identification and enables automated classifiers, but many anatomical details moderate computational predictions and cause bias. In particular, tissues’ light-source-dependent optical characteristics, anatomical location, and potentially hazardous microstructural changes such as peeling have been overlooked in previous literature.

Methods

Narrow-band images of five (n = 5) facial nerves (FNs) and internal carotid arteries (ICAs) were captured from freshly frozen temporal bones. The FNs were split into intracranial and intratemporal samples, and ICAs’ adventitia was peeled from the distal end. Three-dimensional (3D) spectral data were captured by a custom-built liquid crystal tunable filter (LCTF) spectral imaging (SI) system. We investigated the normal variance between the samples and utilized descriptive and machine learning analysis on the image stack hypercubes.

Results

Reflectance between intact and peeled arteries in lower-wavelength domains between 400 and 576 nm was significantly different (p < 0.05). Proximal FN could be differentiated from distal FN in a higher range, 490–720 nm (p < 0.001). ICA with intact tunica differed from proximal FN nearly thorough the VIS range, 412–592 nm (p < 0.001) and 664–720 nm (p < 0.05) as did its distal counterpart, 422–720 nm (p < 0.001). The availed U-Net algorithm classified 90.93% of the pixels correctly in comparison to tissue margins delineated by a specialist.

Conclusion

Selective NBI represents a promising method for assisting tissue identification and computational segmentation of surgical microanatomy. Further multidisciplinary research is required for its clinical applications and intraoperative integration.

Mapping of spectral signatures with snapshot spectral imaging

We propose a snapshot spectral imaging method that enables direct reconstruction of spatial maps for spectral signatures of given materials using a monochromatic image sensor. An image-plane array of dispersive shapers converts an aerial image of an object into a tailored mixture of spectral and spatial data that is sensed and digitally processed to reconstruct weight coefficients of the spectral signatures. The feasibility of the method is proven by computer simulations.

Compressed sensing snapshot spectral imaging by a regular digital camera with an added optical diffuser

We propose a spectral imaging method that allows a regular digital camera to be converted into a snapshot spectral imager by equipping the camera with a dispersive diffuser and with a compressed sensing-based algorithm for digital processing. Results of optical experiments are reported.