Automated Breast Tumor Segmentation in DCE-MRI Using Deep Learning

Mohammed Benjelloun; Mohammed El Adoui; Mohamed Amine Larhmam; Sidi Ahmed Mahmoudi

doi:10.1109/cloudtech.2018.8713352

Automated Breast Tumor Segmentation in DCE-MRI Using Deep Learning

Mohammed Benjelloun, Mohammed El Adoui, Mohamed Amine Larhmam, Sidi Ahmed Mahmoudi

Résultats de recherche: Contribution dans un livre/un catalogue/un rapport/dans les actes d'une conférence › Article dans les actes d'une conférence/un colloque

Résumé

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%.

langue originale	Anglais
titre	2018 4th International Conference on Cloud Computing Technologies and Applications, Cloudtech 2018
Editeur	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronique)	9781728116372
Les DOIs	https://doi.org/10.1109/cloudtech.2018.8713352
Etat de la publication	Publié - 2 juil. 2018
Modification externe	Oui
Evénement	4th International Conference on Cloud Computing Technologies and Applications, Cloudtech 2018 - Brussels, Belgique Durée: 26 nov. 2018 → 28 nov. 2018

Série de publications

Nom	2018 4th International Conference on Cloud Computing Technologies and Applications, Cloudtech 2018

Une conférence

Une conférence	4th International Conference on Cloud Computing Technologies and Applications, Cloudtech 2018
Pays/Territoire	Belgique
La ville	Brussels
période	26/11/18 → 28/11/18

SDG des Nations Unies

Ce résultat contribue à ou aux Objectifs de développement durable suivants

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10.1109/cloudtech.2018.8713352

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Benjelloun, M., El Adoui, M., Larhmam, M. A., & Mahmoudi, S. A. (2018). Automated Breast Tumor Segmentation in DCE-MRI Using Deep Learning. Dans 2018 4th International Conference on Cloud Computing Technologies and Applications, Cloudtech 2018 Article 8713352 (2018 4th International Conference on Cloud Computing Technologies and Applications, Cloudtech 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/cloudtech.2018.8713352

Benjelloun, Mohammed ; El Adoui, Mohammed ; Larhmam, Mohamed Amine et al. / Automated Breast Tumor Segmentation in DCE-MRI Using Deep Learning. 2018 4th International Conference on Cloud Computing Technologies and Applications, Cloudtech 2018. Institute of Electrical and Electronics Engineers Inc., 2018. (2018 4th International Conference on Cloud Computing Technologies and Applications, Cloudtech 2018).

@inproceedings{e490a50f2b544db3a790ea5fac3dd5cb,

title = "Automated Breast Tumor Segmentation in DCE-MRI Using Deep Learning",

abstract = "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%.",

keywords = "Breast cancer, DCE-MRI, Deep Learning, Tumor segmentation",

author = "Mohammed Benjelloun and {El Adoui}, Mohammed and Larhmam, {Mohamed Amine} and Mahmoudi, {Sidi Ahmed}",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 4th International Conference on Cloud Computing Technologies and Applications, Cloudtech 2018 ; Conference date: 26-11-2018 Through 28-11-2018",

year = "2018",

month = jul,

day = "2",

doi = "10.1109/cloudtech.2018.8713352",

language = "English",

series = "2018 4th International Conference on Cloud Computing Technologies and Applications, Cloudtech 2018",

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Benjelloun, M, El Adoui, M, Larhmam, MA & Mahmoudi, SA 2018, Automated Breast Tumor Segmentation in DCE-MRI Using Deep Learning. Dans 2018 4th International Conference on Cloud Computing Technologies and Applications, Cloudtech 2018., 8713352, 2018 4th International Conference on Cloud Computing Technologies and Applications, Cloudtech 2018, Institute of Electrical and Electronics Engineers Inc., 4th International Conference on Cloud Computing Technologies and Applications, Cloudtech 2018, Brussels, Belgique, 26/11/18. https://doi.org/10.1109/cloudtech.2018.8713352

Automated Breast Tumor Segmentation in DCE-MRI Using Deep Learning. / Benjelloun, Mohammed; El Adoui, Mohammed; Larhmam, Mohamed Amine et al.
2018 4th International Conference on Cloud Computing Technologies and Applications, Cloudtech 2018. Institute of Electrical and Electronics Engineers Inc., 2018. 8713352 (2018 4th International Conference on Cloud Computing Technologies and Applications, Cloudtech 2018).

Résultats de recherche: Contribution dans un livre/un catalogue/un rapport/dans les actes d'une conférence › Article dans les actes d'une conférence/un colloque

TY - GEN

T1 - Automated Breast Tumor Segmentation in DCE-MRI Using Deep Learning

AU - Benjelloun, Mohammed

AU - El Adoui, Mohammed

AU - Larhmam, Mohamed Amine

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PY - 2018/7/2

Y1 - 2018/7/2

N2 - 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%.

AB - 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%.

KW - Breast cancer

KW - DCE-MRI

KW - Deep Learning

KW - Tumor segmentation

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Benjelloun M, El Adoui M, Larhmam MA, Mahmoudi SA. Automated Breast Tumor Segmentation in DCE-MRI Using Deep Learning. Dans 2018 4th International Conference on Cloud Computing Technologies and Applications, Cloudtech 2018. Institute of Electrical and Electronics Engineers Inc. 2018. 8713352. (2018 4th International Conference on Cloud Computing Technologies and Applications, Cloudtech 2018). doi: 10.1109/cloudtech.2018.8713352

Automated Breast Tumor Segmentation in DCE-MRI Using Deep Learning

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