Region Adjacency Graph Approach for Acral Melanocytic Lesion Segmentation

Malignant melanoma is among the fastest increasing malignancies in many countries. Due to its propensity to metastasize and lack of effective therapies for most patients with advanced disease, early detection of melanoma is a clinical imperative. In non-Caucasian populations, melanomas are frequently located in acral volar areas and their dermoscopic appearance differs from the non-acral ones. Although lesion segmentation is a natural preliminary step towards its further analysis, so far virtually no acral skin lesion segmentation method has been proposed. Our goal was to develop an effective segmentation algorithm dedicated for acral lesions

Characterization of Seamless CdTe Photon Counting X-Ray Detector

Spectrally selective X-ray imaging provides improved material and tissue discrimination in comparison with the state-of-the-art dual energy technologies that are commonly used in medical, industrial, and security applications. Cadmium telluride (CdTe)- and cadmium zinc telluride (CdZnTe)-based line scanners and small size two-dimensional X-ray sensors are emerging to the market, but the need for large-scale panels is axiomatic. In this study, a seamless CdTe tile was developed that enables the implementation of large-sized, energy selective X-ray detector panels. The developed tile consists of a 64 x 64 pixel array (with 150 mu m pitch) with a necessary substrate, ASIC, and CdTe crystal. The performance of the constructed seamless tile was characterized by focusing on spectral resolution and stability. In addition, a simple pixel trimming method that automates the equalization of each energy selective pixel was developed and analyzed. The obtained results suggest that the proposed concept of seamless (tileable) detector structures is a feasible approach to scale up panel sizes. The seamless tile shows comparable spectral resolution and stability performance with commercial CdTe sensors. The effect of tile to tile variation, the realization of a large-scale panel, as well as the charge sharing performance were left out of the scope and are to be studied in the next phase.Peer reviewe

Characterization of Seamless CdTe Photon Counting X-Ray Detector

Spectrally selective X-ray imaging provides improved material and tissue discrimination in comparison with the state-of-the-art dual energy technologies that are commonly used in medical, industrial, and security applications. Cadmium telluride (CdTe)- and cadmium zinc telluride (CdZnTe)-based line scanners and small size two-dimensional X-ray sensors are emerging to the market, but the need for large-scale panels is axiomatic. In this study, a seamless CdTe tile was developed that enables the implementation of large-sized, energy selective X-ray detector panels. The developed tile consists of a 64 x 64 pixel array (with 150 mu m pitch) with a necessary substrate, ASIC, and CdTe crystal. The performance of the constructed seamless tile was characterized by focusing on spectral resolution and stability. In addition, a simple pixel trimming method that automates the equalization of each energy selective pixel was developed and analyzed. The obtained results suggest that the proposed concept of seamless (tileable) detector structures is a feasible approach to scale up panel sizes. The seamless tile shows comparable spectral resolution and stability performance with commercial CdTe sensors. The effect of tile to tile variation, the realization of a large-scale panel, as well as the charge sharing performance were left out of the scope and are to be studied in the next phase.Peer reviewe

eSkin: Study on the Smartphone Application for Early Detection of Malignant Melanoma

Background. Malignant melanoma is among the fastest increasing malignancies in many countries. With the help of new tools, such as teledermoscopy referrals between primary healthcare and dermatology clinics, the diagnosis of these patients could be made more efficient. The introduction of a high-quality smartphone with a built-in digital camera may make the early detection more convenient. This study presents novel directions for early detection of malignant melanoma based on a smartphone application. Objectives and Methods. In this study, we concentrate on a precise description of a complex infrastructure of a fully automated computer-aided diagnostic system for early detection of malignant melanoma. The framework has been customized for a dermoscope that is customized to attach to the smartphone to be able to carry out mobile teledermoscopy. The application requirements, architecture, and computational methods as well as behavioral and dynamic aspects have been presented in this paper. Conclusion. This paper presents a broad application architecture, which can be easily customized for rapid deployment of a sophisticated health application. Mobile teledermoscopy is a new horizon that might become in the future the basis of the early detection of pigmented skin lesions as a screening tool for primary care doctors and inexperienced dermatologists

A New Approach to Border Irregularity Assessment with Application in Skin Pathology

The border irregularity assessment of tissue structures is an important step in medical diagnostics (e.g., in dermatoscopy, pathology, and cardiology). The diagnostic criteria based on the degree of uniformity and symmetry of border irregularities are particularly vital in dermatopathology, to distinguish between benign and malignant skin lesions. We propose a new method for the segmentation of individual border projections and measuring their morphometry. It is based mainly on analyzing the curvature of the object’s border to identify endpoints of projection bases, and on analyzing object’s skeleton in the graph representation to identify bases of projections and their location along the object’s main axis. The proposed segmentation method has been tested on 25. skin whole slide images of common melanocytic lesions. In total, 825. out of 992. (83.%) manually segmented retes (projections of epidermis) were detected correctly and the Jaccard similarity coefficient for the task of detecting retes was 0.798. Experimental results verified the effectiveness of the proposed approach. Our method is particularly well suited for assessing the border irregularity of human epidermis and thus could help develop computer-aided diagnostic algorithms for skin cancer detection

Pre-Trained Deep Convolutional Neural Network for Clostridioides Difficile Bacteria Cytotoxicity Classification Based on Fluorescence Images

Clostridioides difficile infection (CDI) is an enteric bacterial disease that is increasing in incidence worldwide. Symptoms of CDI range from mild diarrhea to severe life-threatening inflammation of the colon. While antibiotics are standard-of-care treatments for CDI, they are also the biggest risk factor for development of CDI and recurrence. Therefore, novel therapies that successfully treat CDI and protect against recurrence are an unmet clinical need. Screening for novel drug leads is often tested by manual image analysis. The process is slow, tedious and is subject to human error and bias. So far, little work has focused on computer-aided screening for drug leads based on fluorescence images. Here, we propose a novel method to identify characteristic morphological changes in human fibroblast cells exposed to C. difficile toxins based on computer vision algorithms supported by deep learning methods. Classical image processing algorithms for the pre-processing stage are used together with an adjusted pre-trained deep convolutional neural network responsible for cell classification. In this study, we take advantage of transfer learning methodology by examining pre-trained VGG-19, ResNet50, Xception, and DenseNet121 convolutional neural network (CNN) models with adjusted, densely connected classifiers. We compare the obtained results with those of other machine learning algorithms and also visualize and interpret them. The proposed models have been evaluated on a dataset containing 369 images with 6112 cases. DenseNet121 achieved the highest results with a 93.5% accuracy, 92% sensitivity, and 95% specificity, respectively