Multispectral imaging methods for the diagnosis of skin cancer lesions

En col·laboració amb la Universitat Autònoma de Barcelona (UAB) i la Universitat de Barcelona (UB).Skin cancer is the most prevalent form of cancer, and melanoma is one of the most threat disease of it. But it can be cured if it is detected early enough. Multispectral imaging is a potential method to differenciate melanoma from nevi as it provides spectral images with information of absorbance and reflectance. With this aim, spectral images along the visible and near infrared range (from 415nm to 995nm) of 165 lesions including nevi, melanomas and basal cell carcinomas were processed in this master thesis. After obtaining all data in terms of reflectance and absorbance and other related parameters for each pixel of the segmented lesions, a statistical analysis was carried out to quantify their spatial distribution all over each lesion. Algorithms such as Support vector machine (SVM) and Discriminant Analysis (DA) were used as a means of classifying the lesions. The results show that DA linear classifier provides a better diagnosis than the SVM. BCCs are easier to discriminate from nevi than melanomas

Box-level Segmentation Supervised Deep Neural Networks for Accurate and Real-time Multispectral Pedestrian Detection

Effective fusion of complementary information captured by multi-modal sensors (visible and infrared cameras) enables robust pedestrian detection under various surveillance situations (e.g. daytime and nighttime). In this paper, we present a novel box-level segmentation supervised learning framework for accurate and real-time multispectral pedestrian detection by incorporating features extracted in visible and infrared channels. Specifically, our method takes pairs of aligned visible and infrared images with easily obtained bounding box annotations as input and estimates accurate prediction maps to highlight the existence of pedestrians. It offers two major advantages over the existing anchor box based multispectral detection methods. Firstly, it overcomes the hyperparameter setting problem occurred during the training phase of anchor box based detectors and can obtain more accurate detection results, especially for small and occluded pedestrian instances. Secondly, it is capable of generating accurate detection results using small-size input images, leading to improvement of computational efficiency for real-time autonomous driving applications. Experimental results on KAIST multispectral dataset show that our proposed method outperforms state-of-the-art approaches in terms of both accuracy and speed

The Data Big Bang and the Expanding Digital Universe: High-Dimensional, Complex and Massive Data Sets in an Inflationary Epoch

Recent and forthcoming advances in instrumentation, and giant new surveys, are creating astronomical data sets that are not amenable to the methods of analysis familiar to astronomers. Traditional methods are often inadequate not merely because of the size in bytes of the data sets, but also because of the complexity of modern data sets. Mathematical limitations of familiar algorithms and techniques in dealing with such data sets create a critical need for new paradigms for the representation, analysis and scientific visualization (as opposed to illustrative visualization) of heterogeneous, multiresolution data across application domains. Some of the problems presented by the new data sets have been addressed by other disciplines such as applied mathematics, statistics and machine learning and have been utilized by other sciences such as space-based geosciences. Unfortunately, valuable results pertaining to these problems are mostly to be found only in publications outside of astronomy. Here we offer brief overviews of a number of concepts, techniques and developments, some "old" and some new. These are generally unknown to most of the astronomical community, but are vital to the analysis and visualization of complex datasets and images. In order for astronomers to take advantage of the richness and complexity of the new era of data, and to be able to identify, adopt, and apply new solutions, the astronomical community needs a certain degree of awareness and understanding of the new concepts. One of the goals of this paper is to help bridge the gap between applied mathematics, artificial intelligence and computer science on the one side and astronomy on the other.Comment: 24 pages, 8 Figures, 1 Table. Accepted for publication: "Advances in Astronomy, special issue "Robotic Astronomy

Near-infrared imaging of demineralization on the occlusal surfaces of teeth without the interference of stains.

Most new caries lesions are found in the pits and fissures of the occlusal surface. Radiographs have extremely low sensitivity for early occlusal decay, and by the time the lesion is severe enough to appear on a radiograph, it typically has penetrated well into the dentin and surgical intervention is required. The occlusal surfaces are often heavily stained, and visual and tactile detection have poor sensitivity and specificity. Previous near-infrared imaging studies at wavelengths beyond 1300 nm have demonstrated that stains are not visible and demineralization on the occlusal surfaces can be viewed without interference from stains. The objective of our study is to determine how the contrast between sound and lesion areas on occlusal surfaces varies with wavelength from the visible to 2350 nm and determine to what degree stains interfere with that contrast. The lesion contrast for reflectance is measured in 55 extracted teeth with suspected occlusal lesions from 400 to 2350 nm employing silicon and indium gallium arsenide imaging arrays. In addition, the lesion contrast is measured on 25 extracted teeth with suspected occlusal lesions from 400 to 1600 nm in reflectance and from 830 to 1400 nm in transillumination before and after stains are removed using a ultrasonic scaler. The highest lesion contrast in reflectance is measured at wavelengths >1700  nm. Stains interfere significantly at wavelengths <1150  nm (400 to 1150) for both reflectance and transillumination measurements. Our study suggests that the optimum wavelengths for imaging decay in the occlusal surfaces are >1700  nm for reflectance (1700 to 2350 nm) and near 1300 nm (1250 to 1350 nm) for transillumination

High resolution tumor targeting in living mice by means of multispectral optoacoustic tomography

BACKGROUND: Tumor targeting is of high clinical and biological relevance, and major efforts have been made to develop molecular imaging technologies for visualization of the disease markers in tissue. Of particular interest is apoptosis which has a profound role within tumor development and has significant effect on cancer malignancy. METHODS: Herein, we report on targeting of phosphatidylserine-exposing cells within live tumor allograft models using a synthetic near infrared zinc(II)-dipicolylamine probe. Visualization of the probe biodistribution is performed with whole body multispectral optoacoustic tomography (MSOT) system and subsequently compared to results attained by planar and tomographic fluorescence imaging systems. RESULTS: Compared to whole body optical visualization methods, MSOT attains remarkably better imaging capacity by delivering high-resolution scans of both disease morphology and molecular function in real time. Enhanced resolution of MSOT clearly showed that the probe mainly localizes in the vessels surrounding the tumor, suggesting that its tumor selectivity is gained by targeting the phosphatidylserine exposed on the surface of tumor vessels. CONCLUSIONS: The current study demonstrates the high potential of MSOT to broadly impact the fields of tumor diagnostics and preclinical drug development

Enhancing intraoperative tumor delineation with multispectral short-wave infrared fluorescence imaging and machine learning

SIGNIFICANCE: Fluorescence-guided surgery (FGS) provides specific real-time visualization of tumors, but intensity-based measurement of fluorescence is prone to errors. Multispectral imaging (MSI) in the short-wave infrared (SWIR) has the potential to improve tumor delineation by enabling machine-learning classification of pixels based on their spectral characteristics. AIM: Determine whether MSI can be applied to FGS and combined with machine learning to provide a robust method for tumor visualization. APPROACH: A multispectral SWIR fluorescence imaging device capable of collecting data from six spectral filters was constructed and deployed on neuroblastoma (NB) subcutaneous xenografts ( n = 6 ) after the injection of a NB-specific NIR-I fluorescent probe (Dinutuximab-IRDye800). We constructed image cubes representing fluorescence collected from ∼ 850 to 1450 nm and compared the performance of seven learning-based methods for pixel-by-pixel classification, including linear discriminant analysis, k -nearest neighbor classification, and a neural network. RESULTS: The spectra of tumor and non-tumor tissue were subtly different and conserved between individuals. In classification, a combine principal component analysis and k -nearest-neighbor approach with area under curve normalization performed best, achieving 97.5% per-pixel classification accuracy (97.1%, 93.5%, and 99.2% for tumor, non-tumor tissue and background, respectively). CONCLUSIONS: The development of dozens of new imaging agents provides a timely opportunity for multispectral SWIR imaging to revolutionize next-generation FGS