Search

Your search keyword '"Kermi, Adel"' showing total 10 results
Start Over Author "Kermi, Adel"

Refine your results

more »

10 results on '"Kermi, Adel"'

1. Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Author

Bakas, Spyridon, Reyes, Mauricio, Jakab, Andras, Bauer, Stefan, Rempfler, Markus, Crimi, Alessandro, Shinohara, Russell Takeshi, Berger, Christoph, Ha, Sung Min, Rozycki, Martin, Prastawa, Marcel, Alberts, Esther, Lipkova, Jana, Freymann, John, Kirby, Justin, Bilello, Michel, Fathallah-Shaykh, Hassan, Wiest, Roland, Kirschke, Jan, Wiestler, Benedikt, Colen, Rivka, Kotrotsou, Aikaterini, Lamontagne, Pamela, Marcus, Daniel, Milchenko, Mikhail, Nazeri, Arash, Weber, Marc-Andre, Mahajan, Abhishek, Baid, Ujjwal, Gerstner, Elizabeth, Kwon, Dongjin, Acharya, Gagan, Agarwal, Manu, Alam, Mahbubul, Albiol, Alberto, Albiol, Antonio, Albiol, Francisco J., Alex, Varghese, Allinson, Nigel, Amorim, Pedro H. A., Amrutkar, Abhijit, Anand, Ganesh, Andermatt, Simon, Arbel, Tal, Arbelaez, Pablo, Avery, Aaron, Azmat, Muneeza, B., Pranjal, Bai, W, Banerjee, Subhashis, Barth, Bill, Batchelder, Thomas, Batmanghelich, Kayhan, Battistella, Enzo, Beers, Andrew, Belyaev, Mikhail, Bendszus, Martin, Benson, Eze, Bernal, Jose, Bharath, Halandur Nagaraja, Biros, George, Bisdas, Sotirios, Brown, James, Cabezas, Mariano, Cao, Shilei, Cardoso, Jorge M., Carver, Eric N, Casamitjana, Adrià, Castillo, Laura Silvana, Catà, Marcel, Cattin, Philippe, Cerigues, Albert, Chagas, Vinicius S., Chandra, Siddhartha, Chang, Yi-Ju, Chang, Shiyu, Chang, Ken, Chazalon, Joseph, Chen, Shengcong, Chen, Wei, Chen, Jefferson W, Chen, Zhaolin, Cheng, Kun, Choudhury, Ahana Roy, Chylla, Roger, Clérigues, Albert, Colleman, Steven, Colmeiro, Ramiro German Rodriguez, Combalia, Marc, Costa, Anthony, Cui, Xiaomeng, Dai, Zhenzhen, Dai, Lutao, Daza, Laura Alexandra, Deutsch, Eric, Ding, Changxing, Dong, Chao, Dong, Shidu, Dudzik, Wojciech, Eaton-Rosen, Zach, Egan, Gary, Escudero, Guilherme, Estienne, Théo, Everson, Richard, Fabrizio, Jonathan, Fan, Yong, Fang, Longwei, Feng, Xue, Ferrante, Enzo, Fidon, Lucas, Fischer, Martin, French, Andrew P., Fridman, Naomi, Fu, Huan, Fuentes, David, Gao, Yaozong, Gates, Evan, Gering, David, Gholami, Amir, Gierke, Willi, Glocker, Ben, Gong, Mingming, González-Villá, Sandra, Grosges, T., Guan, Yuanfang, Guo, Sheng, Gupta, Sudeep, Han, Woo-Sup, Han, Il Song, Harmuth, Konstantin, He, Huiguang, Hernández-Sabaté, Aura, Herrmann, Evelyn, Himthani, Naveen, Hsu, Winston, Hsu, Cheyu, Hu, Xiaojun, Hu, Xiaobin, Hu, Yan, Hu, Yifan, Hua, Rui, Huang, Teng-Yi, Huang, Weilin, Van Huffel, Sabine, Huo, Quan, HV, Vivek, Iftekharuddin, Khan M., Isensee, Fabian, Islam, Mobarakol, Jackson, Aaron S., Jambawalikar, Sachin R., Jesson, Andrew, Jian, Weijian, Jin, Peter, Jose, V Jeya Maria, Jungo, Alain, Kainz, B, Kamnitsas, Konstantinos, Kao, Po-Yu, Karnawat, Ayush, Kellermeier, Thomas, Kermi, Adel, Keutzer, Kurt, Khadir, Mohamed Tarek, Khened, Mahendra, Kickingereder, Philipp, Kim, Geena, King, Nik, Knapp, Haley, Knecht, Urspeter, Kohli, Lisa, Kong, Deren, Kong, Xiangmao, Koppers, Simon, Kori, Avinash, Krishnamurthi, Ganapathy, Krivov, Egor, Kumar, Piyush, Kushibar, Kaisar, Lachinov, Dmitrii, Lambrou, Tryphon, Lee, Joon, Lee, Chengen, Lee, Yuehchou, Lee, M, Lefkovits, Szidonia, Lefkovits, Laszlo, Levitt, James, Li, Tengfei, Li, Hongwei, Li, Wenqi, Li, Hongyang, Li, Xiaochuan, Li, Yuexiang, Li, Heng, Li, Zhenye, Li, Xiaoyu, Li, Zeju, Li, XiaoGang, Lin, Zheng-Shen, Lin, Fengming, Lio, Pietro, Liu, Chang, Liu, Boqiang, Liu, Xiang, Liu, Mingyuan, Liu, Ju, Liu, Luyan, Llado, Xavier, Lopez, Marc Moreno, Lorenzo, Pablo Ribalta, Lu, Zhentai, Luo, Lin, Luo, Zhigang, Ma, Jun, Ma, Kai, Mackie, Thomas, Madabushi, Anant, Mahmoudi, Issam, Maier-Hein, Klaus H., Maji, Pradipta, Mammen, CP, Mang, Andreas, Manjunath, B. S., Marcinkiewicz, Michal, McDonagh, S, McKenna, Stephen, McKinley, Richard, Mehl, Miriam, Mehta, Sachin, Mehta, Raghav, Meier, Raphael, Meinel, Christoph, Merhof, Dorit, Meyer, Craig, Miller, Robert, Mitra, Sushmita, Moiyadi, Aliasgar, Molina-Garcia, David, Monteiro, Miguel A. B., Mrukwa, Grzegorz, Myronenko, Andriy, Nalepa, Jakub, Ngo, Thuyen, Nie, Dong, Ning, Holly, Niu, Chen, Nuechterlein, Nicholas K, Oermann, Eric, Oliveira, Arlindo, Oliveira, Diego D. C., Oliver, Arnau, Osman, Alexander F. I., Ou, Yu-Nian, Ourselin, Sebastien, Paragios, Nikos, Park, Moo Sung, Paschke, Brad, Pauloski, J. Gregory, Pawar, Kamlesh, Pawlowski, Nick, Pei, Linmin, Peng, Suting, Pereira, Silvio M., Perez-Beteta, Julian, Perez-Garcia, Victor M., Pezold, Simon, Pham, Bao, Phophalia, Ashish, Piella, Gemma, Pillai, G. N., Piraud, Marie, Pisov, Maxim, Popli, Anmol, Pound, Michael P., Pourreza, Reza, Prasanna, Prateek, Prkovska, Vesna, Pridmore, Tony P., Puch, Santi, Puybareau, Élodie, Qian, Buyue, Qiao, Xu, Rajchl, Martin, Rane, Swapnil, Rebsamen, Michael, Ren, Hongliang, Ren, Xuhua, Revanuru, Karthik, Rezaei, Mina, Rippel, Oliver, Rivera, Luis Carlos, Robert, Charlotte, Rosen, Bruce, Rueckert, Daniel, Safwan, Mohammed, Salem, Mostafa, Salvi, Joaquim, Sanchez, Irina, Sánchez, Irina, Santos, Heitor M., Sartor, Emmett, Schellingerhout, Dawid, Scheufele, Klaudius, Scott, Matthew R., Scussel, Artur A., Sedlar, Sara, Serrano-Rubio, Juan Pablo, Shah, N. Jon, Shah, Nameetha, Shaikh, Mazhar, Shankar, B. Uma, Shboul, Zeina, Shen, Haipeng, Shen, Dinggang, Shen, Linlin, Shen, Haocheng, Shenoy, Varun, Shi, Feng, Shin, Hyung Eun, Shu, Hai, Sima, Diana, Sinclair, M, Smedby, Orjan, Snyder, James M., Soltaninejad, Mohammadreza, Song, Guidong, Soni, Mehul, Stawiaski, Jean, Subramanian, Shashank, Sun, Li, Sun, Roger, Sun, Jiawei, Sun, Kay, Sun, Yu, Sun, Guoxia, Sun, Shuang, Suter, Yannick R, Szilagyi, Laszlo, Talbar, Sanjay, Tao, Dacheng, Teng, Zhongzhao, Thakur, Siddhesh, Thakur, Meenakshi H, Tharakan, Sameer, Tiwari, Pallavi, Tochon, Guillaume, Tran, Tuan, Tsai, Yuhsiang M., Tseng, Kuan-Lun, Tuan, Tran Anh, Turlapov, Vadim, Tustison, Nicholas, Vakalopoulou, Maria, Valverde, Sergi, Vanguri, Rami, Vasiliev, Evgeny, Ventura, Jonathan, Vera, Luis, Vercauteren, Tom, Verrastro, C. A., Vidyaratne, Lasitha, Vilaplana, Veronica, Vivekanandan, Ajeet, Wang, Guotai, Wang, Qian, Wang, Chiatse J., Wang, Weichung, Wang, Duo, Wang, Ruixuan, Wang, Yuanyuan, Wang, Chunliang, Wen, Ning, Wen, Xin, Weninger, Leon, Wick, Wolfgang, Wu, Shaocheng, Wu, Qiang, Wu, Yihong, Xia, Yong, Xu, Yanwu, Xu, Xiaowen, Xu, Peiyuan, Yang, Tsai-Ling, Yang, Xiaoping, Yang, Hao-Yu, Yang, Junlin, Yang, Haojin, Yang, Guang, Yao, Hongdou, Ye, Xujiong, Yin, Changchang, Young-Moxon, Brett, Yu, Jinhua, Yue, Xiangyu, Zhang, Songtao, Zhang, Angela, Zhang, Kun, Zhang, Xuejie, Zhang, Lichi, Zhang, Xiaoyue, Zhang, Yazhuo, Zhang, Lei, Zhang, Jianguo, Zhang, Xiang, Zhang, Tianhao, Zhao, Sicheng, Zhao, Yu, Zhao, Xiaomei, Zhao, Liang, Zheng, Yefeng, Zhong, Liming, Zhou, Chenhong, Zhou, Xiaobing, Zhou, Fan, Zhu, Hongtu, Zhu, Jin, Zhuge, Ying, Zong, Weiwei, Kalpathy-Cramer, Jayashree, Farahani, Keyvan, Davatzikos, Christos, van Leemput, Koen, and Menze, Bjoern

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset., Comment: The International Multimodal Brain Tumor Segmentation (BraTS) Challenge

Published
2018

3. Deep Convolutional Neural Networks Using U-Net for Automatic Brain Tumor Segmentation in Multimodal MRI Volumes

Author

Kermi, Adel, Mahmoudi, Issam, Khadir, Mohamed Tarek, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Crimi, Alessandro, editor, Bakas, Spyridon, editor, Kuijf, Hugo, editor, Keyvan, Farahani, editor, Reyes, Mauricio, editor, and van Walsum, Theo, editor

Published
2019
Full Text
View/download PDF

8. Reconstructions faciales à partir d'images tridimensionnelles de crânes humains par recalage et modèle déformable pour l'identification de personnes

Author

Kermi, Adel, Laboratoire Traitement et Communication de l'Information (LTCI), Télécom ParisTech-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Télécom ParisTech, Isabelle Bloch, Mohamed Tayeb Laskri, and Télécom ParisTech, Ecole

Subjects
Crâne, 3D computerized facial reconstruction, Skull, [MATH] Mathematics [math], [MATH]Mathematics [math], Reconstruction faciale 3D informatisée
Abstract

Facial reconstruction from a cranial skeleton is an important technique in several scientific areas, especially in forensic sciences, archaeology, and palaeontology. It is used for skull identification and recognition of people. It can be classified, among reconstructive identification methods. It is most often used as a last resort, when all other techniques fail to produce a person presumed identity. This thesis addresses the facial reconstruction problem from three-dimensional (3D) images of human skulls, considered as unknown. We present a 3D computerized facial reconstruction method based on recent techniques of medical imaging, where the main objectives are the processing speed and the elimination of the subjectivity by leaning particularly on mathematical criteria for evaluating the results. Our method is based on a model deformable approach constrained by the knowledge of soft tissues thicknesses, at a certain number of characteristic landmarks. It uses, for each facial reconstruction, a 3D image of a reference head from which skull and skin are extracted, and a 3D image of the skull of the unknown head for which the skin should be reconstructed. The facial reconstruction process is divided into two main stages. An initialization step of the deformable model, based on a non-linear registration technique guided by B-Spline Free-Form Deformations (FFD) model. We therefore, propose an automatic initialization that is achieved only from a reference skull and skin set as well as an unknown skull. At first, a transformation of the reference skull image into the unknown skull image is computed. Then, we apply the same transformation to deform the reference skin image in order to obtain a new skin, which is considered close to the unknown skin and that will be used as initialization of the final facial reconstruction. In the second step, the initial skin, resulting from the transformation computed previously, is refined by means of a 3D deformable model based on simplexes meshes that is attracted by some characteristic landmarks previously computed by a calculation of Mean and Gaussian curvatures. The landmarks are fixed according the positions of the anthropological landmarks of Rhine and Campbell [Rhine and Campbell, 1980]. The evolution of our deformable model is done according to different internal and external forces among which the Gradient Vectors Flow force (GVF) and a balloon force. This method has been tested on thirteen sets of skull/skin data, extracted from 3D-MRI of individual heads of children and adults. For each facial reconstruction, two skull/skin sets corresponding to a reference head and a head with unknown skin are selected according to similar anthropological characteristics. By this method, encouraging results are obtained. The reconstructed shapes remain faces, visibly acceptable, and are relatively close to the real faces., La reconstruction faciale à partir d'un squelette crânien est une technique importante dans plusieurs domaines scientifiques, en particulier dans les sciences médico-légales, l'archéologie et la paléontologie pour l'identification de crânes et la reconnaissance de personnes. Elle fait partie des méthodes d'identification reconstructive et est utilisée le plus souvent en dernier recours, lorsqu'aucune autre technique ne permet de présumer l'identité inconnue de la personne. Cette thèse aborde la problématique de la reconstruction faciale à partir d'images tridimensionnelles (3D) de crânes humains considérés comme étant inconnus. Nous présentons une méthode de reconstruction faciale 3D informatisée reposant sur des techniques récentes d'imagerie médicale avec comme principaux objectifs la rapidité du traitement et l'élimination de la subjectivité en s'appuyant en particulier sur des critères mathématiques pour évaluer les résultats. Notre méthode est fondée sur une approche par modèle déformable contraint par la connaissance des épaisseurs des tissus mous en un certain nombre de points de repère caractéristiques. Elle utilise, pour chaque reconstruction faciale, une image 3D d'une tête de référence dont nous extrayons la peau et le crâne, et une image 3D du crâne d'une tête inconnue dont nous voulons reconstruire la peau. La procédure de la reconstruction faciale est divisée en deux principales étapes. Une étape d'initialisation du modèle déformable est fondée sur une technique de recalage non linéaire guidé par un modèle de déformations de forme libre (FFD) à base de B-splines. Nous proposons donc une initialisation automatique, réalisée uniquement à partir d'un ensemble crâne et peau de référence et du crâne inconnu. Nous calculons, dans un premier temps, une transformation de l'image 3D d'un crâne de référence vers celle du crâne inconnu. Ensuite, nous appliquons la même transformation pour déformer l'image de la peau de référence vers une nouvelle peau que nous considérons proche de la peau inconnue et qui servira d'initialisation à la reconstruction faciale finale. Dans une seconde étape, la peau initiale, résultant de la transformation calculée précédemment, est raffinée à l'aide d'un modèle déformable 3D à base de maillages simplexes qui est attiré par un ensemble des points de repère caractéristiques préalablement calculés par un calcul des courbures moyenne et gaussienne, et fixés selon les positions des repères anthropologiques de Rhine et Campbell [Rhine et Campbell, 1980]. L'évolution de notre modèle déformable est effectuée suivant différentes forces internes et externes dont la force de champ de vecteurs de gradients (GVF) et une force de pression. Cette méthode a été testée sur treize ensembles de données crâne/peau issus d'IRM-3D de têtes d'individus enfants et adultes. Pour chaque reconstruction faciale, deux ensembles crâne/peau correspondant à une tête de référence et à une tête dont la peau est à reconstruire sont sélectionnés selon des caractéristiques anthropologiques similaires. Par cette méthode, nous obtenons des résultats encourageants. Les formes reconstruites restent des visages, visiblement acceptables, et sont relativement proches des visages réels.

Published
2008

Catalog

Books, media, physical & digital resources
See catalog results