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Summary
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Contrast-enhanced spectral mammography (CESM) is done using the standard digital mammography equipment, with additional software that performs dual-energy image acquisition. The dataset is a collection of 2006 CESM images all high resolution with an average of 2355 x 1315 pixels. Each image with its corresponding manual annotation (breast composition, mass shape, mass margin, mass density, architectural distortion, asymmetries, calcification type, calcification distribution, mass enhancement pattern, non-mass enhancement pattern, non-mass enhancement distribution, and overall BIRADS assessment) is compiled into 1 Excel file. Moreover, full medical reports are provided for each case (DOCX) along with manual segmentation annotation for the abnormal findings in each image (CSV file). Deep learning (DL) has a promising potential in helping radiologists provide a more accurate diagnosis. However, fully annotated and large-sized datasets are required. In the past couple of years, a few public mammography datasets were released. These datasets contain digital mammography images only, and none include CESM images. Acquisition protocol: Two minutes after intravenously injecting the patient with non-ionic low-osmolar iodinated contrast material (dose: 1.5 mL/kg), craniocaudal (CC) and mediolateral oblique (MLO) views are obtained. Each view comprises two exposures, one with low energy (peak kilo-voltage values ranging from 26 to 31kVp) and one with high energy (45 to 49 kVp). A complete examination is carried out in about 5-6 minutes. Image preprocessing: The images were converted from DICOM to JPEG using RadiAnt with best 100% image quality (lossless). |
Acknowledgements
We would like to acknowledge the individuals and institutions that have provided data for this collection:
National Cancer Institute, Cairo University, Cairo, Egypt : Special thanks to Dr. Rana Khaled, M.Sc, Prof. Maha Helal, MD, Prof. Omnia Mokhtar, MD and Dr. Hebatalla El Kassas, MD from the Department of Radiology.
Faculty of Computers and Artificial Intelligence, Cairo University, Cairo, Egypt – Special thanks to Omar Alfarghaly, Prof. Abeer Elkorany, and Prof. Aly Fahmy from the Department of Computer Science.
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