MRI Breast Tumor Segmentation Using Different Encoder and Decoder CNN Architectures

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...

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Veröffentlicht in:Computers (Basel) Jg. 8; H. 3; S. 52
Hauptverfasser: El Adoui, Mohammed, Mahmoudi, Sidi Ahmed, Larhmam, Mohamed Amine, Benjelloun, Mohammed
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.09.2019
Schlagworte:
Artificial neural networks
Breast cancer
breast tumor segmentation
Chemotherapy
Classification
Coders
Datasets
Deep learning
encoder–decoder
HPC
Image segmentation
Localization
Magnetic resonance imaging
Mammography
Medical imaging
MRI
Neural networks
SegNet
Software
Tumors
U-Net
ISSN:2073-431X, 2073-431X
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Zusammenfassung: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.
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ISSN:2073-431X
2073-431X
DOI:10.3390/computers8030052