Updated on 2022/10/01

写真a

 
DAOUD BILEL
 
Organization
Faculty of Natural Science and Technology Special-Appointment Assistant Professor
Position
Special-Appointment Assistant Professor
External link

Degree

  • Ph.D. (Engineering) (Kyushu University, Graduate School of Information Science and Electrical Engineering, Advanced Information technology ) ( 2020.12 )

Research Interests

  • Medical Image Processing Computational anatomy Computer aided system for therapy and surgery

  • Computer aided system for therapy and surgery

  • Medical Image Processing

  • Computational anatomy

  • Radiation therapy planning and treatment

Research Areas

  • Informatics / Life, health and medical informatics  / Medical Image, Radiotherapy

  • Informatics / Perceptual information processing  / Image Information Processing

  • Life Science / Medical systems  / Computer Aided Surgery, real-time image-guided therapy

Education

  • Kyushu University   大学院システム情報科学府   情報知能工学専攻博士後期課程

    2017.4 - 2020.12

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    Country: Japan

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Research History

  • Okayama University   The Graduate School of Natural Science and Technology   Special-Appointed Assistant Professor   Special-Appointed Assistant Professor

    2021.4

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    Country:Japan

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  • Okayama University   The Graduate School of Natural Science and Technology   Part-time researcher   Part-time researcher

    2020.7 - 2021.3

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    Country:Japan

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Professional Memberships

 

Papers

  • A Deep Learning-Based Method for Predicting Volumes of Nasopharyngeal Carcinoma for Adaptive Radiation Therapy Treatment Invited Reviewed International coauthorship

    Bilel Daoud, Ken'ichi Morooka, Shoko Miyauchi, Ryo Kurazume, Wafa Mnejja, Leila Farhat, Jamel Daoud

    2020 25th International Conference on Pattern Recognition (ICPR)   3256 - 3263   2021.1

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    Authorship:Corresponding author   Language:English   Publishing type:Research paper (international conference proceedings)  

  • Dose distribution prediction for optimal treamtment of modern external beam radiation therapy for nasopharyngeal carcinoma Invited Reviewed International coauthorship

    Bilel Daoud, Ken’ichi Morooka, Shoko Miyauchi, Ryo Kurazume, Wafa Mnejja, Leila Farhat, Jamel Daoud

    Workshop on Artificial Intelligence in Radiation Therapy MICCAI 2019   128 - 136   2019.10

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    Authorship:Corresponding author   Language:English   Publishing type:Research paper (international conference proceedings)  

  • 3D segmentation of nasopharyngeal carcinoma from CT images using cascade deep learning

    Bilel Daoud, Ken’ichi Morooka, Ryo Kurazume, Farhat Leila, Wafa Mnejja, Jamel Daoud

    Computerized Medical Imaging and Graphics   77   101644   2019.10

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    Authorship:Corresponding author   Language:English   Publishing type:Research paper (scientific journal)  

  • A Deep Learning-Based Method for Predicting Volumes of Nasopharyngeal Carcinoma for Adaptive Radiation Therapy Treatment

    Bilel Daoud, Ken'ichi Morooka, Shoko Miyauchi, Ryo Kurazume, Wafa Mnejja, Leila Farhat, Jamel Daoud

    2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR)   3256 - 3263   2021

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    Language:English   Publishing type:Research paper (international conference proceedings)   Publisher:IEEE COMPUTER SOC  

    This paper presents a new system for predicting the spatial change of Nasopharyngeal carcinoma(NPC) and organ-at-risks (OARs) volumes over the course of the radiation therapy (RT) treatment for facilitating the workflow of adaptive radiotherapy. The proposed system, called " Tumor Evolution Prediction (TEP-Net)", predicts the spatial distributions of NPC and 5 OARs, separately, in response to RT in the coming week, week n. Here, TEP-Net has (n-1)-inputs that are week 1 to week n-1 of CT axial, coronal or sagittal images acquired once the patient complete the planned RT treatment of the corresponding week. As a result, three predicted results of each target region are obtained from the three-view CT images. To determine the final prediction of NPC and 5 OARs, two integration methods, weighted fully connected layers and weighted voting methods, are introduced. From the experiments using weekly CT images of 140 NPC patients, our proposed system achieves the best performance for predicting NPC and OARs compared with conventional methods.

    DOI: 10.1109/ICPR48806.2021.9412924

    Web of Science

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  • PO-1668: Can we use cascade deep learning for GTV delineation in adaptive radiotherapy for NPC?

    B Daoud, K Morooka, R Kurazume, N Fourati, W Mnejja, L Farhat, J Daoud

    152   S916 - S916   2020.11

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    Authorship:Corresponding author   Language:English   Publishing type:Research paper (international conference proceedings)  

  • PO-1668: Can we use cascade deep learning for GTV delineation in adaptive radiotherapy for NPC?

    B. Daoud, K. Morooka, R. Kurazume, N. Fourati, W. Mnejja, L. Farhat, J. Daoud

    Radiotherapy and Oncology   152   S916 - S916   2020.11

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    Publishing type:Research paper (scientific journal)   Publisher:Elsevier BV  

    DOI: 10.1016/s0167-8140(21)01686-8

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  • A Method for Predicting Dose Distribution of Nasopharyngeal Carcinoma Cases by Multiple Deep Neural Networks Invited Reviewed International coauthorship

    Bilel Daoud, Ken'ichi Morooka, Shoko Miyauchi, Ryo Kurazume, Wafa Mnejja, Leila Farhat, Jamel Daoud

    2020 Joint 9th International Conference on Informatics, Electronics & Vision (ICIEV) and 2020 4th International Conference on Imaging, Vision & Pattern Recognition (icIVPR)   1 - 6   2020.8

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    Authorship:Corresponding author   Language:English   Publishing type:Research paper (international conference proceedings)  

  • Automatic segmentation of nasopharyngeal carcinoma from CT images

    Bilel Daoud, Ali Khalfallah, Leila Farhat, Wafa Mnejja, Ken'ichi Morooka, Med Salim Bouhlel, Jamel Daoud

    International Journal of Biomedical Engineering and Technology   33 ( 3 )   240 - 257   2020.7

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    Authorship:Corresponding author   Language:English   Publishing type:Research paper (scientific journal)  

  • A Method for Predicting Dose Distribution of Nasopharyngeal Carcinoma Cases by Multiple Deep Neural Networks

    Bilel Daoud, Ken'ichi Morooka, Shoko Miyauchi, Ryo Kurazume, Wafa Mnejja, Leila Farhat, Jamel Daoud

    2020 JOINT 9TH INTERNATIONAL CONFERENCE ON INFORMATICS, ELECTRONICS & VISION (ICIEV) AND 2020 4TH INTERNATIONAL CONFERENCE ON IMAGING, VISION & PATTERN RECOGNITION (ICIVPR)   2020

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    Language:English   Publishing type:Research paper (international conference proceedings)   Publisher:IEEE  

    In this paper, we propose a method for predicting dose distribution images of patients with Nasopharyngeal carcinoma (NPC) from contoured computer tomography (CT) images. The proposed system is based on our previous method In The first phase is to obtain the feature maps of 2D dose images of each beam from contoured CT images of a patient by convolutional deep neural network model. In the second phase, dose distribution images are predicted from the obtained feature maps by the integration network. Our modified system predicted dose distribution images accurately. From the experimental results using 80 NPC patients' images, the average number of pixels that satisfy the dose constraints of tumors and OARs regions is 81.9% and 86.1%, respectively. The proposed system had a global 3D gamma passing rates varying from 82.1% to 97.2% for all regions and an overall mean absolute errors (MAEs) was 1.0 +/- 1.2. From the obtained results, our modified system is superior to the results obtained in our previous system results and conventional methods.Contribution-The use of the predicted 7-beam weights, as input, into our CNN network leads to improve the predicted dose distribution.

    Web of Science

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  • Automatic segmentation of Nasopharyngeal carcinoma from CT images

    Daoud, Bilel, Khalfallah, Ali, Farhat, Leila, Mnejja, Wafa, Morooka, Ken'ichi, Bouhlel, Med Salim, Daoud, Jamel

    International Journal of Biomedical Engineering and Technology   33 ( 3 )   240 - 257   2020

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    Language:English   Publishing type:Research paper (scientific journal)   Publisher:Inderscience Publishers (IEL)  

    This paper presents an automatic segmentation technique for identifying nasopharyngeal carcinoma regions in CT images. The proposed technique is based on the region growing method by which an initial seed is automatically generated. A probabilistic map representing a chance of being the tumor pixel in each CT image will be created and used for initial seed determination. This map is generated from three probabilistic functions established upon location of the tumor considered, intensities of the tumor pixels, and asymmetry of organs respectively. A representative of potential tumor pixels will be selected as an initial seed. The experimental results showed that seeds were correctly determined with the percent accuracy of 84.32%. These seeds were grown in preprocessed CT images for identifying the nasopharyngeal carcinoma regions subsequently. The results showed that, for no metastasis cases, perfect match and corresponding ratio were 85.03% and 52.44% respectively and 29.26% and 28.03% correspondingly for metastasis cases. This resulted from a single seed generated in each CT image. It was unable to identify more than one tumor region.

    DOI: 10.1504/ijbet.2017.10016628

    Web of Science

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  • 3D segmentation of nasopharyngeal carcinoma from CT images using cascade deep learning

    Daoud, Bilel, Morooka, Ken’ichi, Kurazume, Ryo, Leila, Farhat, Mnejja, Wafa, Daoud, Jamel

    Computerized Medical Imaging and Graphics   77   101644 - 101644   2019.10

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    Language:English   Publishing type:Research paper (scientific journal)   Publisher:Elsevier  

    In the paper, we propose a new deep learning-based method for segmenting nasopharyngeal carcinoma (NPC) in the nasopharynx from three orthogonal CT images. The proposed method introduces a cascade strategy composed of two-phase manners. In CT images, there are organs, called non-target organs, which NPC never invades. Therefore, the first phase is to detect and eliminate non-target organ regions from the CT images. In the second phase, NPC is extracted from the remained regions in the CT images. Convolutional neural networks (CNNs) are applied to detect non-target organs and NPCs. The proposed system determines the final NPC segmentation by integrating three results obtained from coronal, axial and sagittal images. Moreover, we construct two CNN-based NPC detection systems using one kind of overlapping patches with a fixed size and various overlapping patches with different sizes. From the experiments using CT images of 70 NPC patients, our proposed systems, especially the system using various patches, achieves the best performance for detecting NPC compared with conventional NPC detection methods.

    DOI: 10.1016/j.compmedimag.2019.101644

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    PubMed

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  • Dose Distribution Prediction for Optimal Treamtment of Modern External Beam Radiation Therapy for Nasopharyngeal Carcinoma

    Daoud, Bilel, Morooka, Ken’ichi, Miyauchi, Shoko, Kurazume, Ryo, Mnejja, Wafa, Farhat, Leila, Daoud, Jamel

    Artificial Intelligence in Radiation Therapy   128 - 136   2019

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    Publishing type:Research paper (international conference proceedings)   Publisher:Springer  

    DOI: 10.1007/978-3-030-32486-5_16

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    Other Link: https://dblp.uni-trier.de/db/conf/miccai/airt2019.html#DaoudMMKMFD19

  • Analyse des fichiers dynalog-Varian pour le contrôle de qualité des radiothérapies dynamiques Invited Reviewed

    L Farhat, T Sahnoun, B Daoud, J Daoud

    Cancer/Radiothérapie   21 ( 6-7 )   719 - 720   2017.10

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    Authorship:Lead author   Language:French   Publishing type:Research paper (international conference proceedings)  

  • Analyse des fichiers dynalog-Varian pour le contrôle de qualité des radiothérapies dynamiques

    Farhat, L, Sahnoun, T, Daoud, B, Daoud, J

    Cancer/Radiothérapie   21 ( 6-7 )   719 - 720   2017.10

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    Language:French   Publishing type:Research paper (scientific journal)   Publisher:Elsevier  

    DOI: 10.1016/j.canrad.2017.08.093

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