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2019-05-13 - Colloque/Article dans les actes avec comité de lecture - Anglais - 6 page(s)

Benjelloun Mohammed , El Adoui Mohammed , LARHMAM Mohamed Amine, Mahmoudi Sidi , "Automated Breast Tumor Segmentation in DCE-MRI Using Deep Learning" in The 4th IEEE International Conference on Cloud Computing Technologies and Applications , Brussel, Belgique, 2018

  • Codes CREF : Techniques d'imagerie et traitement d'images (DI2770)
  • Unités de recherche UMONS : Informatique (F114)
  • Instituts UMONS : Institut de Recherche en Technologies de l’Information et Sciences de l’Informatique (InforTech)
Texte intégral :

Abstract(s) :

(Anglais) 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%.
   
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