In a standard mammography study, two views are acquired per breast, the Cranio-Caudal (CC) and Mediolateral-Oblique (MLO). Due to the projective nature of 2D mammography, tissue superposition may both mask or mimic the presence of lesions. Therefore, integrating information from both views is paramount to increase diagnostic confidence for both radiologists and computer-aided detection systems. This emphasizes the importance of automatically matching regions from the two views. We here propose a deep convolutional neural network for the registration of mammography images. The network is trained to predict the affine transformation that minimizes the mean squared error between the MLO and the registered CC view. However, due to the complex nature of the breast glandular pattern, deformations due to compression and the paucity of natural anatomic landmarks, optimizing the mean squared error alone yields suboptimal results. Hence, we propose a weakly supervised approach in which existing annotated lesions are used as landmarks to further optimize the registration. To this aim, the recently proposed Generalized Intersection over Union (GIoU) is exploited as loss. Experiments on the public CBIS-DDSM dataset show that the network was able to correctly realign the images in most cases; corresponding bounding boxes were spatially matched in 68% of the cases. Further improvements can be expected by incorporating an elastic de- formation field in the registration network. Results are promising and support the feasibility of our approach.

A deep learning approach for efficient registration of dual view mammography / Famouri, Sina; Morra, Lia; Lamberti, Fabrizio. - STAMPA. - 12294:(2020), pp. 162-172. ((Intervento presentato al convegno 9th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR 2020) tenutosi a Winterthur, Switzerland nel September 2-4, 2020 [10.1007/978-3-030-58309-5_13].

A deep learning approach for efficient registration of dual view mammography

Sina Famouri;Lia Morra;Fabrizio Lamberti
2020

Abstract

In a standard mammography study, two views are acquired per breast, the Cranio-Caudal (CC) and Mediolateral-Oblique (MLO). Due to the projective nature of 2D mammography, tissue superposition may both mask or mimic the presence of lesions. Therefore, integrating information from both views is paramount to increase diagnostic confidence for both radiologists and computer-aided detection systems. This emphasizes the importance of automatically matching regions from the two views. We here propose a deep convolutional neural network for the registration of mammography images. The network is trained to predict the affine transformation that minimizes the mean squared error between the MLO and the registered CC view. However, due to the complex nature of the breast glandular pattern, deformations due to compression and the paucity of natural anatomic landmarks, optimizing the mean squared error alone yields suboptimal results. Hence, we propose a weakly supervised approach in which existing annotated lesions are used as landmarks to further optimize the registration. To this aim, the recently proposed Generalized Intersection over Union (GIoU) is exploited as loss. Experiments on the public CBIS-DDSM dataset show that the network was able to correctly realign the images in most cases; corresponding bounding boxes were spatially matched in 68% of the cases. Further improvements can be expected by incorporating an elastic de- formation field in the registration network. Results are promising and support the feasibility of our approach.
File in questo prodotto:
File Dimensione Formato  
Famouri2020_Chapter_ADeepLearningApproachForEffici.pdf

non disponibili

Tipologia: 2a Post-print versione editoriale / Version of Record
Licenza: Non Pubblico - Accesso privato/ristretto
Dimensione 2.21 MB
Formato Adobe PDF
2.21 MB Adobe PDF   Visualizza/Apri   Richiedi una copia
A_deep_learning_approach_for_efficient_registration_of_dual_view_mammography (4).pdf

accesso aperto

Tipologia: 2. Post-print / Author's Accepted Manuscript
Licenza: PUBBLICO - Tutti i diritti riservati
Dimensione 726.34 kB
Formato Adobe PDF
726.34 kB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

Caricamento pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2839529