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Carlos Martin Isla; Victor M Campello; Cristian Izquierdo; Kaisar Kushibar; Carla Sendra Balcells; Polyxeni Gkontra; Alireza Sojoudi; Mitchell J Fulton; Tewodros Weldebirhan Arega; Kumaradevan Punithakumar; Lei Li; Xiaowu Sun; Yasmina Al Khalil; Di Liu; Sana Jabbar; Sandro Queiros; Francesco Galati; Moona Mazher; Zheyao Gao; Marcel Beetz; Lennart Tautz; Christoforos Galazis; Marta Varela; Markus Hullebrand; Vicente Grau; Xiahai Zhuang; Domenec Puig; Maria A Zuluaga; Hassan Mohy Ud Din; Dimitris Metaxas; Marcel Breeuwer; Rob J van der Geest; Michelle Noga; Stephanie Bricq; Mark E Rentschler; Andrea Guala; Steffen E Petersen; Sergio Escalera; Jose F Rodriguez Palomares; Karim Lekadir |
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Title |
Deep Learning Segmentation of the Right Ventricle in Cardiac MRI: The M&ms Challenge |
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2023 |
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IEEE Journal of Biomedical and Health Informatics |
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JBHI |
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27 |
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7 |
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3302-3313 |
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In recent years, several deep learning models have been proposed to accurately quantify and diagnose cardiac pathologies. These automated tools heavily rely on the accurate segmentation of cardiac structures in MRI images. However, segmentation of the right ventricle is challenging due to its highly complex shape and ill-defined borders. Hence, there is a need for new methods to handle such structure's geometrical and textural complexities, notably in the presence of pathologies such as Dilated Right Ventricle, Tricuspid Regurgitation, Arrhythmogenesis, Tetralogy of Fallot, and Inter-atrial Communication. The last MICCAI challenge on right ventricle segmentation was held in 2012 and included only 48 cases from a single clinical center. As part of the 12th Workshop on Statistical Atlases and Computational Models of the Heart (STACOM 2021), the M&Ms-2 challenge was organized to promote the interest of the research community around right ventricle segmentation in multi-disease, multi-view, and multi-center cardiac MRI. Three hundred sixty CMR cases, including short-axis and long-axis 4-chamber views, were collected from three Spanish hospitals using nine different scanners from three different vendors, and included a diverse set of right and left ventricle pathologies. The solutions provided by the participants show that nnU-Net achieved the best results overall. However, multi-view approaches were able to capture additional information, highlighting the need to integrate multiple cardiac diseases, views, scanners, and acquisition protocols to produce reliable automatic cardiac segmentation algorithms. |
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Admin @ si @ MCI2023 |
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3880 |
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Carlos Martin-Isla; Maryam Asadi-Aghbolaghi; Polyxeni Gkontra; Victor M. Campello; Sergio Escalera; Karim Lekadir |
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Title |
Stacked BCDU-net with semantic CMR synthesis: application to Myocardial Pathology Segmentation challenge |
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2020 |
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MYOPS challenge and workshop |
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Virtual; October 2020 |
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MICCAIW |
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Admin @ si @ MAG2020 |
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3518 |
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Carme Julia |
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Title |
Missig Data Matrix Factorization Addressing the Structure from Motion Problem |
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Miscellaneous |
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2008 |
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CVC–UAB |
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978–84–935251–6–3 |
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Admin @ si @ Jul2008 |
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1104 |
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Carme Julia |
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Motion segmentation through factorization. Application to night driving assistance |
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2004 |
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CVC Technical Report #79 |
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Admin @ si @ 38169 |
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506 |
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Author |
Carme Julia; Angel Sappa; Felipe Lumbreras |
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Title |
Aprendiendo a recrear la realidad en 3D |
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2008 |
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UAB Divulga, Revista de divulgacion cientifica |
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spreading;ADAS |
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ADAS @ adas @ JSL2008b |
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1472 |
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Carme Julia; Angel Sappa; Felipe Lumbreras; Antonio Lopez |
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Title |
Recovery of Surface Normals and Reflectance from Different Lighting Conditions |
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Conference Article |
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2008 |
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5th International Conference on Image Analysis and Recognition |
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5112 |
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315–325 |
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ADAS @ adas @ JSL2008c |
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1014 |
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Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat |
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Title |
Photometric Stereo through and Adapted Alternation Approach |
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Conference Article |
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2008 |
Publication |
IEEE International Conference on Image Processing, |
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1500–1503 |
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San Diego; CA; USA |
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ADAS @ adas @ JSL2008d |
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1016 |
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Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez |
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Title |
Rank Estimation in Missing Data Matrix Problems |
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Journal Article |
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2011 |
Publication |
Journal of Mathematical Imaging and Vision |
Abbreviated Journal |
JMIV |
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39 |
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2 |
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140-160 |
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A novel technique for missing data matrix rank estimation is presented. It is focused on matrices of trajectories, where every element of the matrix corresponds to an image coordinate from a feature point of a rigid moving object at a given frame; missing data are represented as empty entries. The objective of the proposed approach is to estimate the rank of a missing data matrix in order to fill in empty entries with some matrix completion method, without using or assuming neither the number of objects contained in the scene nor the kind of their motion. The key point of the proposed technique consists in studying the frequency behaviour of the individual trajectories, which are seen as 1D signals. The main assumption is that due to the rigidity of the moving objects, the frequency content of the trajectories will be similar after filling in their missing entries. The proposed rank estimation approach can be used in different computer vision problems, where the rank of a missing data matrix needs to be estimated. Experimental results with synthetic and real data are provided in order to empirically show the good performance of the proposed approach. |
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0924-9907 |
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ADAS |
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Admin @ si @ JSL2011; |
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1710 |
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Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez |
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Title |
An Iterative Multiresolution Scheme for SFM with Missing Data: single and multiple object scenes |
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Journal Article |
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2010 |
Publication |
Image and Vision Computing |
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IMAVIS |
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28 |
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1 |
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164-176 |
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Most of the techniques proposed for tackling the Structure from Motion problem (SFM) cannot deal with high percentages of missing data in the matrix of trajectories. Furthermore, an additional problem should be faced up when working with multiple object scenes: the rank of the matrix of trajectories should be estimated. This paper presents an iterative multiresolution scheme for SFM with missing data to be used in both the single and multiple object cases. The proposed scheme aims at recovering missing entries in the original input matrix. The objective is to improve the results by applying a factorization technique to the partially or totally filled in matrix instead of to the original input one. Experimental results obtained with synthetic and real data sequences, containing single and multiple objects, are presented to show the viability of the proposed approach. |
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0262-8856 |
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ADAS @ adas @ JSL2010 |
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1278 |
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Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez |
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Title |
Predicting Missing Ratings in Recommender Systems: Adapted Factorization Approach |
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Journal Article |
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2009 |
Publication |
International Journal of Electronic Commerce |
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14 |
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1 |
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89-108 |
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The paper presents a factorization-based approach to make predictions in recommender systems. These systems are widely used in electronic commerce to help customers find products according to their preferences. Taking into account the customer's ratings of some products available in the system, the recommender system tries to predict the ratings the customer would give to other products in the system. The proposed factorization-based approach uses all the information provided to compute the predicted ratings, in the same way as approaches based on Singular Value Decomposition (SVD). The main advantage of this technique versus SVD-based approaches is that it can deal with missing data. It also has a smaller computational cost. Experimental results with public data sets are provided to show that the proposed adapted factorization approach gives better predicted ratings than a widely used SVD-based approach. |
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1086-4415 |
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ADAS @ adas @ JSL2009b |
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1237 |
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Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez |
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An iterative multiresolution scheme for SFM with missing data |
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Journal Article |
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2009 |
Publication |
Journal of Mathematical Imaging and Vision |
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JMIV |
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34 |
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3 |
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240–258 |
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Several techniques have been proposed for tackling the Structure from Motion problem through factorization in the case of missing data. However, when the percentage of unknown data is high, most of them may not perform as well as expected. Focussing on this problem, an iterative multiresolution scheme, which aims at recovering missing entries in the originally given input matrix, is proposed. Information recovered following a coarse-to-fine strategy is used for filling in the missing entries. The objective is to recover, as much as possible, missing data in the given matrix.
Thus, when a factorization technique is applied to the partially or totally filled in matrix, instead of to the originally given input one, better results will be obtained. An evaluation study about the robustness to missing and noisy data is reported.
Experimental results obtained with synthetic and real video sequences are presented to show the viability of the proposed approach. |
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ADAS @ adas @ JSL2009a |
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1163 |
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Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez |
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Title |
Rank Estimation in 3D Multibody Motion Segmentation |
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Journal Article |
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2008 |
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Electronic Letters |
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44 |
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4 |
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279-280 |
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A novel technique for rank estimation in 3D multibody motion segmentation is proposed. It is based on the study of the frequency spectra of moving rigid objects and does not use or assume a prior knowledge of the objects contained in the scene (i.e. number of objects and motion). The significance of rank estimation on multibody motion segmentation results is shown by using two motion segmentation algorithms over both synthetic and real data. |
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ADAS @ adas @ JSL2008a |
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939 |
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Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez |
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An Adapted Alternation Approach for Recommender Systems |
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2008 |
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IEEE International Conference on e–Business Engineering, |
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128–135 |
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This paper presents an adaptation of the alternation technique to tackle the prediction task in recommender systems. These systems are widely considered in electronic commerce to help customers to find products they will probably like or dislike. As the SVD-based approaches, the proposed adapted alternation technique uses all the information stored in the system to find the predictions. The main advantage of this technique with respect to the SVD-based ones is that it can deal with missing data. Furthermore, it has a smaller computational cost. Experimental results with public data sets are provided in order to show the viability of the proposed adapted alternation approach. |
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Xi’an (Xina) |
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ADAS @ adas @ JSL2008e |
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1044 |
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Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez |
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Motion Segmentation from Feature Trajectories with Missing Data |
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2007 |
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3rd. Iberian Conference on Pattern Recognition and Image Analysis |
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IbPRIA 2007 |
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LNCS 4477 |
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483–490 |
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Girona (Spain) |
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J. Marti et al. (Eds.) |
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ADAS @ adas @ JSL2007a |
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814 |
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Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez |
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Factorization with Missing and Noisy Data |
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Conference Article |
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Year |
2006 |
Publication |
6th International Conference on Computational Science |
Abbreviated Journal |
ICCS´06 |
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Volume |
LNCS 3991 |
Issue |
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Pages |
555–562 |
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Abstract |
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Address |
Reading (United Kingdom) |
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Notes |
ADAS |
Approved |
no |
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Call Number |
ADAS @ adas @ JSL2006b |
Serial |
653 |
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