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Detection of Microcalcifications in Digital Breast Tomosynthesis using Faster R-CNN and 3D Volume Rendering

2022Journal article Open access
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dc.contributor.authorMatela, Nuno
dc.contributor.authorAlmeida, Pedro
dc.contributor.authorClarkson, Matthew
dc.contributor.authorMota, Ana
dc.date.accessioned2025-03-13T18:22:06Z
dc.date.available2025-03-13T18:22:06Z
dc.date.issued2022
dc.description.abstractMicrocalcification clusters (MCs) are one of the most important biomarkers for breast cancer and Digital Breast Tomosynthesis (DBT) has consolidated its role in breast cancer imaging. As there are mixed observations about MCs detection using DBT, it is important to develop tools that improve this task. Furthermore, the visualization mode of MCs is also crucial, as their diagnosis is associated with their 3D morphology. In this work, DBT data from a public database were used to train a faster region-based convolutional neural network (R-CNN) to locate MCs in entire DBT. Additionally, the detected MCs were further analyzed through standard 2D visualization and 3D volume rendering (VR) specifically developed for DBT data. For MCs detection, the sensitivity of our Faster R-CNN was 60% with 4 false positives. These preliminary results are very promising and can be further improved. On the other hand, the 3D VR visualization provided important information, with higher quality and discernment of the detected MCs. The developed pipeline may help radiologists since (1) it indicates specific breast regions with possible lesions that deserve additional attention and (2) as the rendering of the MCs is similar to a segmentation, a detailed complementary analysis of their 3D morphology is possible.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.5220/0010938800003123pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.5/99321
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.relationUniversidade de Lisboa (PhD grant)pt_PT
dc.relationFundação para a Ciência e Tecnologia Grant No. SFRH/BD/135733/2018pt_PT
dc.relationFundação para a Ciência e Tecnologia FCT-IBEB Strategic Project UIDB/00645/2020pt_PT
dc.subjectDigital Breast Tomosynthesispt_PT
dc.subjectFaster R-CNNpt_PT
dc.subjectVolume Renderingpt_PT
dc.subjectMicrocalcification Clusterspt_PT
dc.titleDetection of Microcalcifications in Digital Breast Tomosynthesis using Faster R-CNN and 3D Volume Renderingpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage89pt_PT
oaire.citation.startPage80pt_PT
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT

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