Prototypes for Content-Based Image Retrieval in Clinical Practice

Depeursinge, Adrien; Fischer, Benedikt; Müller, Henning; Deserno, Thomas M.

doi:10.2174/1874431101105010058

Cited by 17 publications

(10 citation statements)

References 49 publications

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“…1. computer-aided diagnostics (CAD): CBIR methods have been proposed as an effective technology for CAD systems, which have the capacity of relieving the workload of doctors and to offer more reliable and consist analysis of medical images (Akgül et al, 2011;Depeursinge et al, 2011). Despite most retrieval systems are not routinely used, CBIR based CAD are rather research prototypes for medical image analytics.…”

Section: Opportunitiesmentioning

confidence: 99%

Large-scale retrieval for medical image analytics: A comprehensive review

Zhang

Müller

et al. 2018

Medical Image Analysis

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199

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Over the past decades, medical image analytics was greatly facilitated by the explosion of digital imaging techniques, where huge amounts of medical images were produced with ever-increasing quality and diversity. However, conventional methods for analyzing medical images have achieved limited success, as they are not capable to tackle the huge amount of image data. In this paper, we review state-of-the-art approaches for large-scale medical image analysis, which are mainly based on recent advances in computer vision, machine learning and information retrieval. Specifically, we first present the general pipeline of large-scale retrieval, summarize the challenges/opportunities of medical image analytics on a large-scale. Then, we provide a comprehensive review of algorithms and techniques relevant to major processes in the pipeline, including feature representation, feature indexing, searching, etc. On the basis of existing work, we introduce the evaluation protocols and multiple applications of large-scale medical image retrieval, with a variety of exploratory and diagnostic scenarios. Finally, we discuss future directions of large-scale retrieval, which can further improve the performance of medical image analysis.

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Section: Opportunitiesmentioning

confidence: 99%

Large-scale retrieval for medical image analytics: A comprehensive review

Zhang

Müller

et al. 2018

Medical Image Analysis

Self Cite

199

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“…Doctors learn the relationships between a specific disease and anatomical phenotypes; such knowledge is not always reproducible or specific, but it may provide an important clue about a patient’s pathological state. To achieve similar connections using an MRI brain database, machine-learning approaches, such as linear discriminant analysis (LDA) or support vector machines (57–59), can be used to index the anatomical data with diagnostic labelings from the groups; for example, in Figure 5, we entered information about clinical labels (clinically normal for PPA) and calculated the anatomical features that were associated with a specific disease. In Figure 6, the results of discriminant analysis are shown, and PPA patients are differentiated from controls with high sensitivity and specificity.…”

Section: Technologies Required For Next-generation Image-based Diamentioning

confidence: 99%

Atlas-Based Neuroinformatics via MRI: Harnessing Information from Past Clinical Cases and Quantitative Image Analysis for Patient Care

Mori

Oishi

Faria

et al. 2013

Annu. Rev. Biomed. Eng.

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With the ever-increasing amount of anatomical information radiologists have to evaluate for routine diagnoses, computational support that facilitates more efficient education and clinical decision making is highly desired. Despite the rapid progress of image analysis technologies for magnetic resonance imaging of the human brain, these methods have not been widely adopted for clinical diagnoses. To bring computational support into the clinical arena, we need to understand the decision-making process employed by well-trained clinicians and develop tools to simulate that process. In this review, we discuss the potential of atlas-based clinical neuroinformatics, which consists of annotated databases of anatomical measurements grouped according to their morphometric phenotypes and coupled with the clinical informatics upon which their diagnostic groupings are based. As these are indexed via parametric representations, we can use image retrieval tools to search for phenotypes along with their clinical metadata. The review covers the current technology, preliminary data, and future directions of this field.

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“…Mandal et al [1] put efforts into combining pragmatic solutions to palliate the lack of cardiologists in remote rural areas in India, which results in a portable cardiac sound analysis system to detect heart anomalies and subsequently determines the importance of getting further assistance. Other researchers have systematically analyzed the usability requirements of image-based computer-aided diagnosis systems in clinical routine [4,5] with the aim of reducing the gap between Sensors, Medical Images and Signal Processing: Ubiquitous Personalized Health Monitoring…”

Section: Focusing On the Clinical Impactmentioning

confidence: 99%

Sensors, Medical Images and Signal Processing: Ubiquitous Personalized Health Monitoring

Müller

Foncubierta–Rodríguez²,

Depeursinge³

2012

Yearb Med Inform

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SummaryTo summarize excellent research in the field of medical sensor, signal and imaging informatics published in the year 2011.Synopsis of the articles selected for the IMIA (International Medical Informatics Association) Yearbook 2012 through a manual initial selection and a peer review process to find the best paper in this domain published in 2011.Current research in the field of sensors, signal, and imaging informatics is characterized by theoretically sound techniques and evaluations with focus in imaging informatics. An increased number of systems with embedded signal processing where sensors include signal processing were observed in 2011. In all domains, pragmatic solutions with the goal of clinical impact have grown, including in developing countries where simple, robust techniques are combined to address primary and simple medical problems with potentially high impact. Finally, recent advances in image and signal processing are moving towards patient-based modeling.The best paper selection of articles on sensors, signal, and imaging informatics shows examples of excellent research on methods concerning theoretically sound original development in this field in the year 2012.

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Prototypes for Content-Based Image Retrieval in Clinical Practice

Cited by 17 publications

References 49 publications

Large-scale retrieval for medical image analytics: A comprehensive review

Large-scale retrieval for medical image analytics: A comprehensive review

Atlas-Based Neuroinformatics via MRI: Harnessing Information from Past Clinical Cases and Quantitative Image Analysis for Patient Care

Sensors, Medical Images and Signal Processing: Ubiquitous Personalized Health Monitoring

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