DICOM’s Standardization in Histo-Pathology

Background: The Digital Imaging and Communications in Medicine (DICOM) standard helps to represent, store, and to exchange healthcare images associated with its data. DICOM develops over time and is continuously adapted to match the rigors of new clinical demands and technologies. An uphill battle in this regard is to conciliate new software programs with legacy systems. Methods: This work discusses the essential aspects of the standard and assesses its capabilities and limitations in a multisite, multivendor healthcare system aiming at Whole Slicing Image (WSI) procedures. Selected relevant DICOM attributes help to develop and organize WSI applications that extract and handle image data, integrated patient records, and metadata. DICOM must also interface with proprietary file formats, clinical metadata and from different laboratory information systems. Standard DICOM validation tools to measure encoding, storing, querying and retrieval of medical data can verify the generated DICOM files over the web. Results: This work investigates the current regulations and recommendations for the use of DICOM with WSI data. They rely mostly on the EU guidelines that help envision future needs and extensions based on new examination modalities like concurrent use of WSI with in-vitro imaging and 3D WSI. Conclusion: A DICOM file format and communication protocol for pathology has been defined. However, adoption by vendors and in the field is pending. DICOM allows efficient access and prompt availability of WSI data as well as associated metadata. By leveraging a wealth of existing infrastructure solutions, the use of DICOM facilitates enterprise integration and data exchange for digital pathology. In the future, the DICOM standard will have to address several issues due to the way samples are gathered and encompassing new imaging technologies

The Rediscovery of the Social Side of Medicine: Philosophy and Value of the International Classification of Functioning, Disability and Health (ICF)

oai:ojs2.medtech.ichsmt.org:article/2Medicine is at a risk to slide into a sole repair service for the malfunction of organs. But the patients’ hopes and confidence toward doctors practicing this repair work go far beyond that: after acute medical treatment, many patients suffer from chronic impairments due to the natural course of disease or as a result of medical interventions. Despite the resulting handicaps, patients aim toward participating in family and social life, retaining a workplace, and receiving support to remain a valued member of family and community. Doctors should therefore not only concentrate on the natural science and technological part of medicine but also consider the background of their patients and their involvement in life situations, including environmental and personal factors, as these may influence functioning and disability as facilitators or barriers. Health insurances must organize, finance, and control the achievements of the post-acute treatment process with the goal of participation. Public health must combine and assess individual views to prepare reasonable population-based social, economic, and political decisions. The philosophy and structure of the International Classification of Functioning, Disability and Health (ICF) is supporting this attitude of medicine to complement the International Classification of Diseases (ICD) as a basis for health reports

Medical Technologies Journal: Re-launching the publication

Welcome to the fifth volume of the international peer reviewed journal: Medical Technologies Journal. We are proud to announce, in this editorial, the re-launching of publication on the journal. The journal receives all medical domains: trials and, synthesis; findings and innovations.  After a period of silence due to several technical problems principally the COVID19 crises, we publish, for our community, this issue. It contains four scientific articles one review in ophthalmology and three in medical technologies

The American Academy of Anti-Aging Medicine Looks Ahead Towards the 28th Annual World Congress

This editorial report the 28th Annual World Congress ongranized by The American Academy of Anti-Aging Medicine. The congress will be taking place entirely online from December 12-13, 2020. The rest of this edtorial discribs the details and contents oft he congress.    &nbsp

Computer-aided detection of simultaneous abdominal organ from CT images based on iterative watershed transform

The interpretation of medical images benefits from anatomical and physiological priors to optimize computer-aided diagnosis applications. Segmentation of the liver, spleen, and kidneys is regarded as a major primary step in computer-aided diagnosis of abdominal organ diseases. In this paper, a semi-automated method for medical image data is presented for abdominal organ segmentation data using mathematical morphology. Our proposed method is based on a hierarchical segmentation and watershed algorithm. In our approach, a powerful technique has been designed to suppress over-segmentation based on a mosaic image and on the computation of the watershed transform. Our algorithm is currently in two parts. In the first, we seek to improve the quality of the gradient-mosaic image. In this step, we propose a method for improving the gradient-mosaic image by applying the anisotropic diffusion filter followed by the morphological filters. Thereafter, we proceed to the hierarchical segmentation of the liver, spleen, and kidney. To validate the segmentation technique proposed, we have tested it on several images. Our segmentation approach is evaluated by comparing our results with the manual segmentation performed by an expert. The experimental results are described in the last part of this work

Content-Based Image Retrieval (CBIR) in Big Histological Image Databases

Background: Automatic analysis of Histopathological Images (HIs) demands image processing and Computational Intelligence (CI) techniques. Both Computer-Aided Diagnosis (CAD) and Content-Based Image-Retrieval (CBIR) systems assist diagnosis, disease discovery, and biological decision-making. Classical tests comprise screening examinations and biopsy. Histopathology slides offer more ample diagnosis data. However, manual examination of microscopic images is labor-intensive and time-consuming and may depend on a subjective assessment by the pathologist, which can be a challenge. Methods: This work discusses a CBIR framework to extract and handle histological data, histological metadata, integrated patient records, specimen metadata, attributes, and similar stored files. This work presents a scalable image-retrieval framework for intelligent HI analysis with real-time retrieval. The potential applications of this framework include image-guided diagnosis, decision support, healthcare education, and efficient biological data management. Results: The considerable amount of biological-related data prompted the development and deployment of large-scale databases and data-driven techniques to bridge the semantic gap between images and diagnostic information. The new cloud computing technologies and the concept of cyber-physical systems have improved the CBIR architectures considerably. The proposed scalable architecture relies on CI and validates performance on several HIs acquired from microscopic tissues. Extensive assessments show improvements in terms of disease classification and retrieval tests. Conclusion: This research effort significant contributions are twofold. 1) Defining a  comprehensive and large-scale CBIR framework to analyze HIs with high-dimensional features and CI methods successfully. 2) high-performance updating and optimization strategies improve the querying while better handling new training samples than traditional methods

Self-Organizing Algorithm for Massive Tractography Datasets Clustering with Outliers Elimination Based on Multiple Species Flocking Model

Background: The study of White Matter (WM) connectivity is of general interest in neuroscience, which is achieved by the analysis and clustering of the streamlines composed of the tractography dataset. The WM streamlines clustering is a challenge, because of the complexity and the vast size of the WM tractography dataset and its composition by various streamlines, in additionally to the presence of outliers. Methods: Several WM clustering methods have been proposed in the literature to overcome these constraints. However, these methods stay statics. Once the clustering streamline is performed, it stays in this one. In this paper, we propose a new framework of distributed multiagent, improving, and adapting a bio-inspired model called Multiple Species Flocking (MSF) for WM streamlines clustering and automatic outlier elimination. Results: The basic MSF rules are modified and adapted to perform streamlines clustering in higher dimensions. Specifically, each streamline is associated with a mobile agent and move onto a virtual space to form a group following the defined rules. Only the agents assigned to similar streamlines form a flock, whereas the agents assigned to dissimilar streamlines are sidelined and considered as outliers. Conclusion: Swarm intelligence features of the approach, such as adaptivity, parallelism, dynamism, and decentralization, make our algorithm scalable to large datasets, very fast and accurate, which are confirmed by experimental results on synthetic and real datasets

Classification of histological images of thyroid nodules based on a combination of Deep Features and Machine Learning

Background: Thyroid nodules are a prevalent worldwide disease with complex pathological types. They can be classified as either benign or malignant. This paper presents a tool for automatically classifying histological images of thyroid nodules, with a focus on papillary carcinoma and follicular adenoma. Methods: In this work, two pre-trained Convolutional Neural Network (CNN) architectures, VGG16 and VGG19, are used to extract deep features. Then, a principal component analysis was used to reduce the dimensionality of the vectors. Then, three machine learning algorithms (Support Vector Machine, K-Nearest Neighbor, and Random Forest) were used for classification. These investigations were applied to our database collection, Results: The proposed investigations have been applied to our private database collection with a total of 112 histological images. The highest results were obtained by the VGG16 transfer deep feature and the SVM classifier with an accuracy rate equal to 100%

How to Write Scientific Journals of the Highest Standards

One of the requirements of a Ph.D. or Master is to write traditional and systematic reviews, critiques, letters to editors (LTE), case reports or case studies, at least one original article, and sometimes a meta-analysis, all with proficiency and inspiration. This presentation focuses on the ideas from research to publication, i.e., how to depict a distinct image in our mind in order to convert our research into various types of academic papers. Also, this presentation provides a general approach and some key points for writing and successfully publishing journal papers. The presentation will also cover some publication ethics guidelines, ethics of reporting questionnaire-based research, authorship criteria, plagiarism detection and how to avoid plagiarism, duplicate publication, and salami publication

Quantitative Analysis in Multimodality Imaging: Challenges and Opportunities

This talk reflects the tremendous ongoing interest in molecular and dual-modality imaging (PET/CT, SPECT/CT and PET/MR) as both clinical and research imaging modalities in the past decade. An overview of molecular multi-modality medical imaging instrumentation as well as simulation, reconstruction, quantification, and related image processing issues with special emphasis on quantitative analysis of nuclear medical images are presented. This tutorial aims to bring the biomedical image processing community a review on the state-of-the-art algorithms used and under development for accurate quantitative analysis in multimodality and multi-parametric molecular imaging and their validation mainly from the developer’s perspective with emphasis on image reconstruction and analysis techniques. It will inform the audience about a series of advanced development recently carried out at the PET instrumentation & Neuroimaging Lab of Geneva University Hospital and other active research groups. Current and prospective future applications of quantitative molecular imaging also are addressed, especially its use prior to therapy for dose distribution modeling and optimization of treatment volumes in external radiation therapy and patient-specific 3D dosimetry in targeted therapy toward the concept of image-guided radiation therapy. &nbsp