OPTIMAM Mammography Image Database: A Large-Scale Resource of                    Mammography Images and Clinical Data

Halling‐Brown, Mark; Warren, Lucy M.; Ward, Dominic; Lewis, Emma; Mackenzie, Alistair; Wallis, Matthew; Wilkinson, Louise; Given-Wilson, Rosalind; McAvinchey, Rita; Young, Kenneth C.

doi:10.1148/ryai.2020200103

Cited by 86 publications

(60 citation statements)

References 9 publications

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“…OPTIMAM (OMI-DB) is an on-going project collected over 2.5 million breast cancer screening mammography images [85]. EchoNet-Dynamic is another resource with over 10 K echocardiography videos with corresponding labelled clinical measurements and human expert annotations [86].…”

Section: Medical Image Repositoriesmentioning

confidence: 99%

Data preparation for artificial intelligence in medical imaging: A comprehensive guide to open-access platforms and tools

et al. 2021

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The vast amount of data produced by today's medical imaging systems has led medical professionals to turn to novel technologies in order to efficiently handle their data and exploit the rich information present in them. In this context, artificial intelligence (AI) is emerging as one of the most prominent solutions, promising to revolutionise every day clinical practice and medical research. The pillar supporting the development of reliable and robust AI algorithms is the appropriate preparation of the medical images to be used by the AI-driven solutions. Here, we provide a comprehensive guide for the necessary steps to prepare medical images prior to developing or applying AI algorithms. The main steps involved in a typical medical image preparation pipeline include: (i) image acquisition at clinical sites, (ii) image de-identification to remove personal information and protect patient privacy, (iii) data curation to control for image and associated information quality, (iv) image storage, and (v) image annotation. There exists a plethora of open access tools to perform each of the aforementioned tasks and are hereby reviewed. Furthermore, we detail medical image repositories covering different organs and diseases. Such repositories are constantly increasing and enriched with the advent of big data. Lastly, we offer directions for future work in this rapidly evolving field.

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Section: Medical Image Repositoriesmentioning

confidence: 99%

Data preparation for artificial intelligence in medical imaging: A comprehensive guide to open-access platforms and tools

et al. 2021

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“…A total of 45 mammographic cases, with breast density varying from fatty to dense, and with and without lesions, were selected and retrieved, under license, from the OMI-DB anonymised database of mammograms, which is part of the OPTIMAM project [19]. Given the use of this existing anonymised database, no ethics approval was needed for this study.…”

Section: Digital Mammography Casesmentioning

confidence: 99%

How does image quality affect radiologists’ perceived ability for image interpretation and lesion detection in digital mammography?

et al. 2021

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Objectives To study how radiologists’ perceived ability to interpret digital mammography (DM) images is affected by decreases in image quality. Methods One view from 45 DM cases (including 30 cancers) was degraded to six levels each of two acquisition-related issues (lower spatial resolution and increased quantum noise) and three post-processing-related issues (lower and higher contrast and increased correlated noise) seen during clinical evaluation of DM systems. The images were shown to fifteen breast screening radiologists from five countries. Aware of lesion location, the radiologists selected the most-degraded mammogram (indexed from 1 (reference) to 7 (most degraded)) they still felt was acceptable for interpretation. The median selected index, per degradation type, was calculated separately for calcification and soft tissue (including normal) cases. Using the two-sided, non-parametric Mann-Whitney test, the median indices for each case and degradation type were compared. Results Radiologists were not tolerant to increases (medians: 1.5 (calcifications) and 2 (soft tissue)) or decreases (median: 2, for both types) in contrast, but were more tolerant to correlated noise (median: 3, for both types). Increases in quantum noise were tolerated more for calcifications than for soft tissue cases (medians: 3 vs. 4, p = 0.02). Spatial resolution losses were considered less acceptable for calcification detection than for soft tissue cases (medians: 3.5 vs. 5, p = 0.001). Conclusions Perceived ability of radiologists for image interpretation in DM was affected not only by image acquisition-related issues but also by image post-processing issues, and some of those issues affected calcification cases more than soft tissue cases. Key Points • Lower spatial resolution and increased quantum noise affected the radiologists’ perceived ability to interpret calcification cases more than soft tissue lesion or normal cases. • Post-acquisition image processing-related effects, not only image acquisition-related effects, also impact the perceived ability of radiologists to interpret images and detect lesions. • In addition to current practices, post-acquisition image processing-related effects need to also be considered during the testing and evaluation of digital mammography systems.

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“…In the ROC study, a total of 80 DM cases with breast density varying from fatty to dense, with and without lesions, acquired using Lorad Selenia (Hologic, Inc., Bedford, MA, USA) DM systems, were selected and retrieved, under license, from the OMI-DB anonymised database of mammograms, which is part of the OPTIMAM project [30]. Each case consisted of two views (craniocaudal and mediolateral oblique) of each breast.…”

Section: Digital Mammography Imagesmentioning

confidence: 99%

Validation of a candidate instrument to assess image quality in digital mammography using ROC analysis

Boita

Engen

Mackenzie

et al. 2021

European Journal of Radiology

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To validate a candidate instrument, to be used by different professionals to assess image quality in digital mammography (DM), against detection performance results. Methods: A receiver operating characteristics (ROC) study was conducted to assess the detection performance in DM images with four different image quality levels due to different quality issues. Fourteen expert breast radiologists from five countries assessed a set of 80 DM cases, containing 60 lesions (40 cancers, 20 benign findings) and 20 normal cases. A visual grading analysis (VGA) study using a previously-described candidate instrument was conducted to evaluate a subset of 25 of the images used in the ROC study. Eight radiologists that had participated in the ROC study, and seven expert breast-imaging physicists, evaluated this subset. The VGA score (VGAS) and the ROC and visual grading characteristics (VGC) areas under the curve (AUC ROC and AUC VGC ) were compared. Results: No large differences in image quality among the four levels were detected by either ROC or VGA studies. However, the ranking of the four levels was consistent: level 1 (partial AUC ROC : 0.070, VGAS: 6.77) performed better than levels 2 (0.066, 6.15), 3 (0.061, 5.82), and 4 (0.062, 5.37). Similarity between radiologists' and physicists' assessments was found (average VGAS difference of 10 %). Conclusions: The results from the candidate instrument were found to correlate with those from ROC analysis, when used by either observer group. Therefore, it may be used by different professionals, such as radiologists, radiographers, and physicists, to assess clinically-relevant image quality variations in DM.

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OPTIMAM Mammography Image Database: A Large-Scale Resource of Mammography Images and Clinical Data

Cited by 86 publications

References 9 publications

Data preparation for artificial intelligence in medical imaging: A comprehensive guide to open-access platforms and tools

Data preparation for artificial intelligence in medical imaging: A comprehensive guide to open-access platforms and tools

How does image quality affect radiologists’ perceived ability for image interpretation and lesion detection in digital mammography?

Validation of a candidate instrument to assess image quality in digital mammography using ROC analysis

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