Improved accuracy in colorectal cancer tissue decomposition through refinement of established deep learning solutions.

Hematoxylin and eosin-stained biopsy slides are regularly available for colorectal cancer patients. These slides are often not used to define objective biomarkers for patient stratification and treatment selection. Standard biomarkers often pertain to costly and slow genetic tests.

Domain-specific transfer learning in the automated scoring of tumor-stroma ratio from histopathological images of colorectal cancer.

Tumor-stroma ratio (TSR) is a prognostic factor for many types of solid tumors. In this study, we propose a method for automated estimation of TSR from histopathological images of colorectal cancer. The method is based on convolutional neural networks which were trained to classify colorectal cancer tissue in hematoxylin-eosin stained samples into three classes: stroma, tumor and other.

Reflectance Measurement Method Based on Sensor Fusion of Frame-Based Hyperspectral Imager and Time-of-Flight Depth Camera.

Hyperspectral imaging and distance data have previously been used in aerial, forestry, agricultural, and medical imaging applications. Extracting meaningful information from a combination of different imaging modalities is difficult, as the image sensor fusion requires knowing the optical properties of the sensors, selecting the right optics and finding the sensors' mutual reference frame through calibration. In this research we demonstrate a method for fusing data from Fabry-Perot interferometer hyperspectral camera and a Kinect V2 time-of-flight depth sensing camera.

Hyperspectral Imaging for Non-invasive Diagnostics of Melanocytic Lesions.

Malignant melanoma poses a clinical diagnostic problem, since a large number of benign lesions are excised to find a single melanoma. This study assessed the accuracy of a novel non-invasive diagnostic technology, hyperspectral imaging, for melanoma detection. Lesions were imaged prior to excision and histopathological analysis.

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.

Differentiating Malignant from Benign Pigmented or Non-Pigmented Skin Tumours-A Pilot Study on 3D Hyperspectral Imaging of Complex Skin Surfaces and Convolutional Neural Networks.

Several optical imaging techniques have been developed to ease the burden of skin cancer disease on our health care system. Hyperspectral images can be used to identify biological tissues by their diffuse reflected spectra. In this second part of a three-phase pilot study, we used a novel hand-held SICSURFIS Spectral Imager with an adaptable field of view and target-wise selectable wavelength channels to provide detailed spectral and spatial data for lesions on complex surfaces.

Rapid Quantification of Microalgae Growth with Hyperspectral Camera and Vegetation Indices.

Spectral cameras are traditionally used in remote sensing of microalgae, but increasingly also in laboratory-scale applications, to study and monitor algae biomass in cultures. Practical and cost-efficient protocols for collecting and analyzing hyperspectral data are currently needed. The purpose of this study was to test a commercial, easy-to-use hyperspectral camera to monitor the growth of different algae strains in liquid samples.

A Do-It-Yourself Hyperspectral Imager Brought to Practice with Open-Source Python.

Commercial hyperspectral imagers (HSIs) are expensive and thus unobtainable for large audiences or research groups with low funding. In this study, we used an existing do-it-yourself push-broom HSI design for which we provide software to correct for spectral smile aberration without using an optical laboratory. The software also corrects an aberration which we call tilt.

Hyperspectral Imaging Reveals Spectral Differences and Can Distinguish Malignant Melanoma from Pigmented Basal Cell Carcinomas: A Pilot Study.

Pigmented basal cell carcinomas can be difficult to distinguish from melanocytic tumours. Hyperspectral imaging is a non-invasive imaging technique that measures the reflectance spectra of skin in vivo. The aim of this prospective pilot study was to use a convolutional neural network classifier in hyperspectral images for differential diagnosis between pigment-ed basal cell carcinomas and melanoma.

Generating Hyperspectral Skin Cancer Imagery using Generative Adversarial Neural Network.

In this study we develop a proof of concept of using generative adversarial neural networks in hyperspectral skin cancer imagery production. Generative adversarial neural network is a neural network, where two neural networks compete. The generator tries to produce data that is similar to the measured data, and the discriminator tries to correctly classify the data as fake or real.

Erratum: Multilabel segmentation of cancer cell culture on vascular structures with deep neural networks (Erratum).

[This corrects the article DOI: 10.1117/1.JMI.

Multilabel segmentation of cancer cell culture on vascular structures with deep neural networks.

New increasingly complex cancer cell models are being developed. These new models seem to represent the cell behavior more accurately and have better physiological relevance than prior models. An efficient testing method for selecting the most optimal drug treatment does not exist to date.

Uncertainty quantification on a spatial Markov-chain model for the progression of skin cancer.

A spatial Markov-chain model is formulated for the progression of skin cancer. The model is based on the division of the computational domain into nodal points, that can be in a binary state: either in 'cancer state' or in 'non-cancer state'. The model assigns probabilities for the non-reversible transition from 'non-cancer' state to the 'cancer state' that depend on the states of the neighbouring nodes.

Delineating margins of lentigo maligna using a hyperspectral imaging system.

Lentigo maligna (LM) is an in situ form of melanoma which can progress into invasive lentigo maligna melanoma (LMM). Variations in the pigmentation and thus visibility of the tumour make assessment of lesion borders challenging. We tested hyperspectral imaging system (HIS) in in vivo preoperative delineation of LM and LMM margins.

Detecting field cancerization using a hyperspectral imaging system.

Background: Field cancerization denotes subclinical abnormalities in a tissue chronically exposed to UV radiation. These abnormalities can be found surrounding the clinically visible actinic keratoses.

Objectives: The aim of this study was to test the feasibility of a hyperspectral imaging system in the detection of multiple clinical and subclinical AKs for early treatment of the affected areas.