MRI Breast Tumor Segmentation Using Different Encoder and Decoder CNN Architectures

[en] Breast tumor segmentation in medical images is a decisive step for diagnosis and treatment follow-up. Automating this challenging task helps radiologists to reduce the high manual workload of breast cancer analysis. In this paper, we propose two deep learning approaches to automate the breast tumor segmentation in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) by building two fully convolutional neural networks (CNN) based on SegNet and U-Net. The obtained models can handle both detection and segmentation on each single DCE-MRI slice. In this study, we used a dataset of 86 DCE-MRIs, acquired before and after two cycles of chemotherapy, of 43 patients with local advanced breast cancer, a total of 5452 slices were used to train and validate the proposed models. The data were annotated manually by an experienced radiologist. To reduce the training time, a high-performance architecture composed of graphic processing units was used. The model was trained and validated, respectively, on 85% and 15% of the data. A mean intersection over union (IoU) of 68.88 was achieved using SegNet and 76.14% using U-Net architecture.

Disciplines :

Computer science
Electrical & electronics engineering
Radiology, nuclear medicine & imaging
Library & information sciences

Author, co-author :

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

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

LARHMAM, Mohamed Amine

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

Language :

English

Title :

MRI Breast Tumor Segmentation Using Different Encoder and Decoder CNN Architectures

Publication date :

29 June 2019

Journal title :

Computers

ISSN :

2073-431X

Publisher :

MDPI AG

Volume :

Issue :

Pages :

52-63

Peer reviewed :

Peer Reviewed verified by ORBi

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 01 July 2019

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Bibliography

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