Medical imaging and telemedicine - from medical data production, to processing, storing, and sharing: a short outlook.
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Database Management Systems↗
Biomedical subjects
Henning Müller
Publications and source records attributed to Henning Müller.
Assessment of Internet-based tele-medicine in Africa (the RAFT project).
The objectives of this paper on the Réseau Afrique Francophone de Télémédecine (RAFT) project are the evaluation of feasibility, potential, problems and risks of an Internet-based tele-medicine network in developing countries of Africa. The RAFT project was started in Western African countries 5 years ago and has now extended to other regions of Africa as well (i.e. Madagascar, Rwanda). A project for the development of a national tele-medicine network in Mali was initiated in 2001, extended to Mauritania in 2002 and to Morocco in 2003. By 2006, a total of nine countries are connected. The entire technical infrastructure is based on Internet technologies for medical distance learning and tele-consultations. The results are a tele-medicine network that has been in productive use for over 5 years and has enabled various collaboration channels, including North-to-South (from Europe to Africa), South-to-South (within Africa), and South-to-North (from Africa to Europe) distance learning and tele-consultations, plus many personal exchanges between the participating hospitals and Universities. It has also unveiled a set of potential problems: (a) the limited importance of North-to-South collaborations when there are major differences in the available resources or the socio-cultural contexts between the collaborating parties; (b) the risk of an induced digital divide if the periphery of the health system in developing countries is not involved in the development of the network; and (c) the need for the development of local medical content management skills. Particularly point (c) is improved through the collaboration between the various countries as professionals from the medical and the computer science field are sharing courses and resources. Personal exchanges between partners in the project are frequent, and several persons received an education at one of the partner Universities. As conclusion, we can say that the identified risks have to be taken into account when designing large-scale tele-medicine projects in developing countries. These problems can be mitigated by fostering South-South collaboration channels, by the use of satellite-based Internet connectivity in remote areas, the appreciation of local knowledge and its publication on-line. The availability of such an infrastructure also facilitates the development of other projects, courses, and local content creation.
Africa↗
Advancing biomedical image retrieval: development and analysis of a test collection.
OBJECTIVE: Develop and analyze results from an image retrieval test collection. METHODS: After participating research groups obtained and assessed results from their systems in the image retrieval task of Cross-Language Evaluation Forum, we assessed the results for common themes and trends. In addition to overall performance, results were analyzed on the basis of topic categories (those most amenable to visual, textual, or mixed approaches) and run categories (those employing queries entered by automated or manual means as well as those using visual, textual, or mixed indexing and retrieval methods). We also assessed results on the different topics and compared the impact of duplicate relevance judgments. RESULTS: A total of 13 research groups participated. Analysis was limited to the best run submitted by each group in each run category. The best results were obtained by systems that combined visual and textual methods. There was substantial variation in performance across topics. Systems employing textual methods were more resilient to visually oriented topics than those using visual methods were to textually oriented topics. The primary performance measure of mean average precision (MAP) was not necessarily associated with other measures, including those possibly more pertinent to real users, such as precision at 10 or 30 images. CONCLUSIONS: We developed a test collection amenable to assessing visual and textual methods for image retrieval. Future work must focus on how varying topic and run types affect retrieval performance. Users' studies also are necessary to determine the best measures for evaluating the efficacy of image retrieval systems.
Abstracting and Indexing↗
Expression of CDX2 and MUC2 in Barrett's mucosa.
Barrett's mucosa is a risk factor for esophageal adenocarcinoma and should be detected at an early stage. It is defined by the presence of columnar epithelium with goblet cells in the lower esophagus, but histologic diagnosis can be uncertain in the absence of distinct goblet cells. We investigated 55 biopsies from 48 patients with endoscopically plain Barrett's esophagus and performed immunohistochemistry for CDX2 and MUC2. In addition, alcian blue (pH 2,5)/PAS staining was done. In histologically unequivocal Barrett's mucosa, nuclear expression of CDX2 in goblet cells and many columnar cells, as well as cytoplasmic positivity for MUC2 in goblet cells, could be observed. Alcian blue (pH 2,5)/PAS stained acidic mucins in goblet cells and in some non-goblet columnar cells. In six cases, no definite Barrett's mucosa was present, and no expression of MUC2 could be observed. In these biopsies, there was granular cytoplasmic and/or focal nuclear staining for CDX2 in non-goblet columnar epithelial cells, indicating their intestinal differentiation. We suggest that this peculiar mucosa is the precursor of unequivocal Barrett's mucosa and would designate it early Barrett's mucosa. Alcian blue for acidic mucins is inconsistent in this epithelium and does not reliably indicate early intestinal differentiation.
Aged↗
Lung CT Analysis and Retrieval as a Diagnostic Aid.
Image retrieval is currently a very active research field due to the large amount of visual data being produced in most modern hospitals. Most often, the goal is to aid the diagnostic process. Unfortunately, only very few medical image retrieval systems are currently used in clinical routine. One of the application domains for image retrieval is the analysis and retrieval of lung CTs. A first user study in the United States shows that these systems allow improving the diagnostic quality.This article describes the approach to an aid for lung CT diagnostics. The analysis incorporates several steps and the goal is to automate the process as much as possible for easy integration into diagnostics. Thus, several automatic steps are proposed from a selection of the most characteristic slices, to an automatic segmentation of the lung tissue and a classification on the segmented area into diagnostic classes. Feedback to the MD will be given in the form of marked regions in the images that appear to be different from the norm of healthy tissue. We are currently working on a small set of training images with marked and annotated regions but a larger set of images for the evaluation of our algorithm is in work. For this reason, the article does currently not contain much quantitative evaluation.For several tasks we use existing open source software such as Weka and itk. This allows an easy reproduction of the search results.
Algorithms↗
A reference data set for the evaluation of medical image retrieval systems.
Content-based image retrieval is starting to become an increasingly important factor in medical imaging research and image management systems. Several retrieval systems and methodologies exist and are used in a large variety of applications from automatic labelling of images to diagnostic aid and image classification. Still, it is very hard to compare the performance of these systems as the used databases often contain copyrighted or private images and are thus not interchangeable between research groups, also for patient privacy. Most of the currently used databases for evaluating systems are also fairly small which is partly due to the high cost in obtaining a gold standard or ground truth that is necessary for evaluation. Several large image databases, though without a gold standard, start to be available publicly, for example by the NIH (National Institutes for Health). This article describes the creation of a large medical image database that is used in a teaching file containing more than 8,700 varied medical images. The images are anonymised and can be exchanged free of charge and copyright. Ground truth (a gold standard) has been obtained for a set of 26 images being selected as query topics for content-based query by image example. To reduce the time for the generation of ground truth, pooling methods well known from the text or information retrieval field have been used. Such a database is a good starting point for comparing the current image retrieval systems and to measure the retrieval quality, especially within the context of teaching files, image case databases and the support of teaching. For a comparison of retrieval systems for diagnostic aid, specialised image databases, including the diagnosis and a case description will need to be made available, as well, including gold standards for a proper system evaluation. A first evaluation event for image retrieval is foreseen at the 2004 CLEF conference (Cross Language Evaluation Forum) to compare text-and content-based access mechanism to images.
Databases, Factual↗
A review of content-based image retrieval systems in medical applications-clinical benefits and future directions.
Content-based visual information retrieval (CBVIR) or content-based image retrieval (CBIR) has been one on the most vivid research areas in the field of computer vision over the last 10 years. The availability of large and steadily growing amounts of visual and multimedia data, and the development of the Internet underline the need to create thematic access methods that offer more than simple text-based queries or requests based on matching exact database fields. Many programs and tools have been developed to formulate and execute queries based on the visual or audio content and to help browsing large multimedia repositories. Still, no general breakthrough has been achieved with respect to large varied databases with documents of differing sorts and with varying characteristics. Answers to many questions with respect to speed, semantic descriptors or objective image interpretations are still unanswered. In the medical field, images, and especially digital images, are produced in ever-increasing quantities and used for diagnostics and therapy. The Radiology Department of the University Hospital of Geneva alone produced more than 12,000 images a day in 2002. The cardiology is currently the second largest producer of digital images, especially with videos of cardiac catheterization ( approximately 1800 exams per year containing almost 2000 images each). The total amount of cardiologic image data produced in the Geneva University Hospital was around 1 TB in 2002. Endoscopic videos can equally produce enormous amounts of data. With digital imaging and communications in medicine (DICOM), a standard for image communication has been set and patient information can be stored with the actual image(s), although still a few problems prevail with respect to the standardization. In several articles, content-based access to medical images for supporting clinical decision-making has been proposed that would ease the management of clinical data and scenarios for the integration of content-based access methods into picture archiving and communication systems (PACS) have been created. This article gives an overview of available literature in the field of content-based access to medical image data and on the technologies used in the field. Section 1 gives an introduction into generic content-based image retrieval and the technologies used. Section 2 explains the propositions for the use of image retrieval in medical practice and the various approaches. Example systems and application areas are described. Section 3 describes the techniques used in the implemented systems, their datasets and evaluations. Section 4 identifies possible clinical benefits of image retrieval systems in clinical practice as well as in research and education. New research directions are being defined that can prove to be useful. This article also identifies explanations to some of the outlined problems in the field as it looks like many propositions for systems are made from the medical domain and research prototypes are developed in computer science departments using medical datasets. Still, there are very few systems that seem to be used in clinical practice. It needs to be stated as well that the goal is not, in general, to replace text-based retrieval methods as they exist at the moment but to complement them with visual search tools.
Databases, Factual↗
Integrating content-based visual access methods into a medical case database.
In the computer vision domain, content-based access methods to all forms of multimedia data are a hot research topic. A large number of tools have been developed to find documents in multimedia repositories and to manage the (visual) information that has been created, for example by the Internet. Although no general breakthrough has been achieved with respect to searching diverse databases with automatically extracted features, the techniques have gained acceptance in a few well-defined domains such as image agencies (Corbis) and trademark research. In the medical domain, the number of digital images produced and used for teaching, diagnostics and therapy is rising in a similar way as in other domains. Still, there are only a few image retrieval systems that use automatically extracted visual features for content-based access to medical image databases. This article describes the use of an open source image retrieval system (GIFT) that has been adapted for the use with medical images using the image case database CasImage that has been developed and maintained by the University Hospital of Geneva and that is in routine use. The first results show the potential of this technique to retrieve similar cases from a case database. This is an important task for teaching and might also become important for diagnostics using case-based reasoning, for example. For the use as a diagnostic tool, it is foreseen to specialize the visual features for the domain of lung image retrieval, using high resolution computed tomography (HRCT) images.
Database Management Systems↗
Informatics in radiology (infoRAD): benefits of content-based visual data access in radiology.
The field of medicine is often cited as an area for which content-based visual retrieval holds considerable promise. To date, very few visual image retrieval systems have been used in clinical practice; the first applications of image retrieval systems in medicine are currently being developed to complement conventional text-based searches. An image retrieval system was developed and integrated into a radiology teaching file system, and the performance of the retrieval system was evaluated, with use of query topics that represent the teaching database well, against a standard of reference generated by a radiologist. The results of this evaluation indicate that content-based image retrieval has the potential to become an important technology for the field of radiology, not only in research, but in teaching and diagnostics as well. However, acceptance of this technology in the clinical domain will require identification and implementation of clinical applications that use content-based access mechanisms, necessitating close cooperation between medical practitioners and medical computer scientists. Nevertheless, content-based image retrieval has the potential to become an important technology for radiology practice.
Humans↗