Automated Breast Tumor Segmentation in DCE-MRI Using Deep Learning

[en] Segmentation of breast tumor is an important step for breast cancer follow-up and treatment. Automating this challenging task can help radiologists to reduce the high workload of breast cancer analysis. In this paper, we propose a deep learning approach to automate the segmentation of breast tumors in DCE-MRI data. We build an architecture based on U-net fully convolutional neural network. The trained model can handle both detection and segmentation on each single breast slice. In this study, we used a dataset of 86 DCE-MRI, acquired before and after chemotherapy, of 43 patients with local breast cancer, a total of 5452 slices. The data have been annotated manually by an experienced radiologist. The model was trained and validated on 85% and 15% of the data and achieved a mean IoU of 76,14%.

Disciplines :

Computer science

Author, co-author :

Benjelloun, Mohammed ; Université de Mons > Faculté Polytechnique > Service Informatique, Logiciel et Intelligence artificielle

El Adoui, Mohammed ; Université de Mons > Faculté Polytechnique > Service Informatique, Logiciel et Intelligence artificielle

LARHMAM, Mohamed Amine

Mahmoudi, Sidi ; Université de Mons > Faculté Polytechnique > Service Informatique, Logiciel et Intelligence artificielle

Language :

English

Title :

Automated Breast Tumor Segmentation in DCE-MRI Using Deep Learning

Publication date :

13 May 2019

Event name :

The 4th IEEE International Conference on Cloud Computing Technologies and Applications

Event place :

Brussels, Belgium

Event date :

2018

Research unit :

F114 - Informatique, Logiciel et Intelligence artificielle

Research institute :

R300 - Institut de Recherche en Technologies de l'Information et Sciences de l'Informatique

Available on ORBi UMONS :

since 26 September 2018

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Bibliography

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