Improving knowledge management through the support of image examination and data annotation using DICOM structured reporting

Salavert-Torres, José; Quilis, J. Damiàn Segrelles; Espert, Ignacio Blanquer; García, Vicente Hernández

doi:10.1016/j.jbi.2012.07.004

Cited by 8 publications

(4 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Vendors should take these considerations seriously and collaborate for the adoption of a common, standard VCE media format that is capable of accommodating not only videos but also structured annotations, such as standards proposed for other medical imaging modalities. 131 The requirements of both the clinical and IT communities need to be taken into account.…”

Section: Enhancement Of Video Qualitymentioning

confidence: 99%

Software for enhanced video capsule endoscopy: challenges for essential progress

Iakovidis

Koulaouzidis

2015

Nat Rev Gastroenterol Hepatol

186

113

View full text Add to dashboard Cite

Video capsule endoscopy (VCE) has revolutionized the diagnostic work-up in the field of small bowel diseases. Furthermore, VCE has the potential to become the leading screening technique for the entire gastrointestinal tract. Computational methods that can be implemented in software can enhance the diagnostic yield of VCE both in terms of efficiency and diagnostic accuracy. Since the appearance of the first capsule endoscope in clinical practice in 2001, information technology (IT) research groups have proposed a variety of such methods, including algorithms for detecting haemorrhage and lesions, reducing the reviewing time, localizing the capsule or lesion, assessing intestinal motility, enhancing the video quality and managing the data. Even though research is prolific (as measured by publication activity), the progress made during the past 5 years can only be considered as marginal with respect to clinically significant outcomes. One thing is clear-parallel pathways of medical and IT scientists exist, each publishing in their own area, but where do these research pathways meet? Could the proposed IT plans have any clinical effect and do clinicians really understand the limitations of VCE software? In this Review, we present an in-depth critical analysis that aims to inspire and align the agendas of the two scientific groups.

show abstract

Section: Enhancement Of Video Qualitymentioning

confidence: 99%

Software for enhanced video capsule endoscopy: challenges for essential progress

Iakovidis

Koulaouzidis

2015

Nat Rev Gastroenterol Hepatol

186

113

View full text Add to dashboard Cite

show abstract

“…DICOM-SR contains text with links to other data such as images, waveforms and spatial or temporal coordinates. Although DICOM-SR is not as widespread as DICOM for digital images, its use has many advantages [ 50 , 51 ]. DICOM-SR documents can be stored and sent along with the images belonging to the same study in PACS.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, DICOM-SR supports unified lexicons such as RadLex, ICD-10 and SNOMED. Finally, DICOM-SR templates have been defined to constrain the possible structures and to provide the basic codes that can be used to encode specific reports [ 50 ]. Specifically, a DICOM-SR template is applied to the document content to harmonize its structure.…”

Section: Introductionmentioning

confidence: 99%

How does DICOM support big data management? Investigating its use in medical imaging community

Aiello

Esposito²,

Pagliari³

et al. 2021

Insights Imaging

View full text Add to dashboard Cite

The diagnostic imaging field is experiencing considerable growth, followed by increasing production of massive amounts of data. The lack of standardization and privacy concerns are considered the main barriers to big data capitalization. This work aims to verify whether the advanced features of the DICOM standard, beyond imaging data storage, are effectively used in research practice. This issue will be analyzed by investigating the publicly shared medical imaging databases and assessing how much the most common medical imaging software tools support DICOM in all its potential. Therefore, 100 public databases and ten medical imaging software tools were selected and examined using a systematic approach. In particular, the DICOM fields related to privacy, segmentation and reporting have been assessed in the selected database; software tools have been evaluated for reading and writing the same DICOM fields. From our analysis, less than a third of the databases examined use the DICOM format to record meaningful information to manage the images. Regarding software, the vast majority does not allow the management, reading and writing of some or all the DICOM fields. Surprisingly, if we observe chest computed tomography data sharing to address the COVID-19 emergency, there are only two datasets out of 12 released in DICOM format. Our work shows how the DICOM can potentially fully support big data management; however, further efforts are still needed from the scientific and technological community to promote the use of the existing standard, encouraging data sharing and interoperability for a concrete development of big data analytics.

show abstract

“…Such terms can be directly derived from the terminology provided by the radiologists in their reports [13] or automatically predicted from computational imaging features [14]. The semantic terms can be used to describe a variety of information about the image content ( e.g.…”

Section: Introductionmentioning

confidence: 99%

A hierarchical knowledge-based approach for retrieving similar medical images described with semantic annotations

Kurtz

Beaulieu

Napel

et al. 2014

Journal of Biomedical Informatics

View full text Add to dashboard Cite

Computer-assisted image retrieval applications could assist radiologist interpretations by identifying similar images in large archives as a means to providing decision support. However, the semantic gap between low-level image features and their high level semantics may impair the system performances. Indeed, it can be challenging to comprehensively characterize the images using low-level imaging features to fully capture the visual appearance of diseases on images, and recently the use of semantic terms has been advocated to provide semantic descriptions of the visual contents of images. However, most of the existing image retrieval strategies do not consider the intrinsic properties of these terms during the comparison of the images beyond treating them as simple binary (presence/absence) features. We propose a new framework that includes semantic features in images and that enables retrieval of similar images in large databases based on their semantic relations. It is based on two main steps: (1) annotation of the images with semantic terms extracted from an ontology, and (2) evaluation of the similarity of image pairs by computing the similarity between the terms using the Hierarchical Semantic-Based Distance (HSBD) coupled to an ontological measure. The combination of these two steps provides a means of capturing the semantic correlations among the terms used to characterize the images that can be considered as a potential solution to deal with the semantic gap problem. We validate this approach in the context of the retrieval and the classification of 2D regions of interest (ROIs) extracted from computed tomographic (CT) images of the liver. Under this framework, retrieval accuracy of more than 0.96 was obtained on a 30-images dataset using the Normalized Discounted Cumulative Gain (NDCG) index that is a standard technique used to measure the effectiveness of information retrieval algorithms when a separate reference standard is available. Classification results of more than 95% were obtained on a 77-images dataset. For comparison purpose, the use of the Earth Mover's Distance (EMD), which is an alternative distance metric that considers all the existing relations among the terms, led to results retrieval accuracy of 0.95 and classification results of 93% with a higher computational cost. The results provided by the presented framework are competitive with the state-of-the-art and emphasize the usefulness of the proposed methodology for radiology image retrieval and classification.

show abstract

Improving knowledge management through the support of image examination and data annotation using DICOM structured reporting

Cited by 8 publications

References 30 publications

Software for enhanced video capsule endoscopy: challenges for essential progress

Software for enhanced video capsule endoscopy: challenges for essential progress

How does DICOM support big data management? Investigating its use in medical imaging community

A hierarchical knowledge-based approach for retrieving similar medical images described with semantic annotations

Contact Info

Product

Resources

About