#suremapp #dataset #mammograms #manually-annotated #mammography

Publicly Available SuReMaPP (Suspicious Regions on Mammograms from Palermo Polyclinic)

The SuReMaPP dataset consists of 343 mammograms hand-labeled by expert radiologists dealing with the identification of suspicious regions such as abnormalities (benignant and malignant) and calcifications. SuReMaPP contains mammograms with high spatial resolutions depending on the mammography device used (in order, GIOTTO IMAGE SDL/W generates images with spatial resolution of 3584 x 2816 pixels, FUJIFILM FCR PROFECT CS generates images with spatial resolution of 5928 x 4728). We want to share SuReMaPP hand-labeled dataset with the scientific community to be used as "Gold Standard" for biomedical imaging methods and algorithms. We used SuReMaPP to validate and assess the performance of our method "An Integrated Solution for detecting suspicious regions in Mammogram Images". You are free to use the database in your scientific research but you must abide by the license agreement when using the images The case studies in question are anonymised.

To make access to SuReMaPP go to link down below:

http://www1.unipa.it/dibimel/mammodb/

Details related to SuReMaPP images are given as follows: 1st column SuReMaPP database reference number.

2nd column Laterality Image : CC_DX CC_SX OL_DX OL_SX

3rd column: Character of background tissue FA - Fatty FG - Fatty-Glandular

4rd column Class of abnormality present: OVA - Oval opacity CIRC - Circular opacity NOD - No Defined MISC - Other ASYM - Asymmetry NORM - Normal

5rd column Contour : REG - regular NON_REG - regular SFU - faded POL - polilobati ND - Non definiti

6th column Severity of abnormality : B - Benign M - Malignant N - No Defined

7th, 8th columns x,y image-coordinates of centre of abnormality.

9th column Approximate radius (in pixels) of a circle enclosing the abnormality.

If you use SuReMaPP Dataset, please cite the scientific paper as down below (bibtex reference format):

@article{bruno2020novel, title={A novel solution based on scale invariant feature transform descriptors and deep learning for the detection of suspicious regions in mammogram images}, author={Bruno, Alessandro and Ardizzone, Edoardo and Vitabile, Salvatore and Midiri, Massimo}, journal={Journal of Medical Signals and Sensors}, volume={10}, number={3}, pages={158}, year={2020}, publisher={Wolters Kluwer--Medknow Publications} }