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 |
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| Abstract | 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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| AbstractList | 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. |
| Author | Benjelloun, Mohammed Larhmam, Mohamed Amine Mahmoudi, Sidi Ahmed El Adoui, Mohammed |
| Author_xml | – sequence: 1 givenname: Mohammed orcidid: 0000-0003-1891-2318 surname: El Adoui fullname: El Adoui, Mohammed – sequence: 2 givenname: Sidi Ahmed orcidid: 0000-0002-1530-9524 surname: Mahmoudi fullname: Mahmoudi, Sidi Ahmed – sequence: 3 givenname: Mohamed Amine orcidid: 0000-0002-0832-6852 surname: Larhmam fullname: Larhmam, Mohamed Amine – sequence: 4 givenname: Mohammed surname: Benjelloun fullname: Benjelloun, Mohammed |
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| SubjectTerms | 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 |
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