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86 results on '"Martin-Isla, Carlos"'

1. Democratizing AI in Africa: FL for Low-Resource Edge Devices

Author

Fabila, Jorge, Campello, Víctor M., Martín-Isla, Carlos, Obungoloch, Johnes, Leo, Kinyera, Ronald, Amodoi, and Lekadir, Karim

Subjects
Computer Science - Machine Learning
Abstract

Africa faces significant challenges in healthcare delivery due to limited infrastructure and access to advanced medical technologies. This study explores the use of federated learning to overcome these barriers, focusing on perinatal health. We trained a fetal plane classifier using perinatal data from five African countries: Algeria, Ghana, Egypt, Malawi, and Uganda, along with data from Spanish hospitals. To incorporate the lack of computational resources in the analysis, we considered a heterogeneous set of devices, including a Raspberry Pi and several laptops, for model training. We demonstrate comparative performance between a centralized and a federated model, despite the compute limitations, and a significant improvement in model generalizability when compared to models trained only locally. These results show the potential for a future implementation at a large scale of a federated learning platform to bridge the accessibility gap and improve model generalizability with very little requirements.

Published
2024

2. MAMA-MIA: A Large-Scale Multi-Center Breast Cancer DCE-MRI Benchmark Dataset with Expert Segmentations

Author

Garrucho, Lidia, Reidel, Claire-Anne, Kushibar, Kaisar, Joshi, Smriti, Osuala, Richard, Tsirikoglou, Apostolia, Bobowicz, Maciej, del Riego, Javier, Catanese, Alessandro, Gwoździewicz, Katarzyna, Cosaka, Maria-Laura, Abo-Elhoda, Pasant M., Tantawy, Sara W., Sakrana, Shorouq S., Shawky-Abdelfatah, Norhan O., Abdo-Salem, Amr Muhammad, Kozana, Androniki, Divjak, Eugen, Ivanac, Gordana, Nikiforaki, Katerina, Klontzas, Michail E., García-Dosdá, Rosa, Gulsun-Akpinar, Meltem, Lafcı, Oğuz, Mann, Ritse, Martín-Isla, Carlos, Prior, Fred, Marias, Kostas, Starmans, Martijn P. A., Strand, Fredrik, Díaz, Oliver, Igual, Laura, and Lekadir, Karim

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Databases
Abstract

Current research in breast cancer Magnetic Resonance Imaging (MRI), especially with Artificial Intelligence (AI), faces challenges due to the lack of expert segmentations. To address this, we introduce the MAMA-MIA dataset, comprising 1506 multi-center dynamic contrast-enhanced MRI cases with expert segmentations of primary tumors and non-mass enhancement areas. These cases were sourced from four publicly available collections in The Cancer Imaging Archive (TCIA). Initially, we trained a deep learning model to automatically segment the cases, generating preliminary segmentations that significantly reduced expert segmentation time. Sixteen experts, averaging 9 years of experience in breast cancer, then corrected these segmentations, resulting in the final expert segmentations. Additionally, two radiologists conducted a visual inspection of the automatic segmentations to support future quality control studies. Alongside the expert segmentations, we provide 49 harmonized demographic and clinical variables and the pretrained weights of the well-known nnUNet architecture trained using the DCE-MRI full-images and expert segmentations. This dataset aims to accelerate the development and benchmarking of deep learning models and foster innovation in breast cancer diagnostics and treatment planning., Comment: 15 paes, 7 figures, 3 tables

Published
2024

3. Why is the winner the best?

Author

Eisenmann, Matthias, Reinke, Annika, Weru, Vivienn, Tizabi, Minu Dietlinde, Isensee, Fabian, Adler, Tim J., Ali, Sharib, Andrearczyk, Vincent, Aubreville, Marc, Baid, Ujjwal, Bakas, Spyridon, Balu, Niranjan, Bano, Sophia, Bernal, Jorge, Bodenstedt, Sebastian, Casella, Alessandro, Cheplygina, Veronika, Daum, Marie, de Bruijne, Marleen, Depeursinge, Adrien, Dorent, Reuben, Egger, Jan, Ellis, David G., Engelhardt, Sandy, Ganz, Melanie, Ghatwary, Noha, Girard, Gabriel, Godau, Patrick, Gupta, Anubha, Hansen, Lasse, Harada, Kanako, Heinrich, Mattias, Heller, Nicholas, Hering, Alessa, Huaulmé, Arnaud, Jannin, Pierre, Kavur, Ali Emre, Kodym, Oldřich, Kozubek, Michal, Li, Jianning, Li, Hongwei, Ma, Jun, Martín-Isla, Carlos, Menze, Bjoern, Noble, Alison, Oreiller, Valentin, Padoy, Nicolas, Pati, Sarthak, Payette, Kelly, Rädsch, Tim, Rafael-Patiño, Jonathan, Bawa, Vivek Singh, Speidel, Stefanie, Sudre, Carole H., van Wijnen, Kimberlin, Wagner, Martin, Wei, Donglai, Yamlahi, Amine, Yap, Moi Hoon, Yuan, Chun, Zenk, Maximilian, Zia, Aneeq, Zimmerer, David, Aydogan, Dogu Baran, Bhattarai, Binod, Bloch, Louise, Brüngel, Raphael, Cho, Jihoon, Choi, Chanyeol, Dou, Qi, Ezhov, Ivan, Friedrich, Christoph M., Fuller, Clifton, Gaire, Rebati Raman, Galdran, Adrian, Faura, Álvaro García, Grammatikopoulou, Maria, Hong, SeulGi, Jahanifar, Mostafa, Jang, Ikbeom, Kadkhodamohammadi, Abdolrahim, Kang, Inha, Kofler, Florian, Kondo, Satoshi, Kuijf, Hugo, Li, Mingxing, Luu, Minh Huan, Martinčič, Tomaž, Morais, Pedro, Naser, Mohamed A., Oliveira, Bruno, Owen, David, Pang, Subeen, Park, Jinah, Park, Sung-Hong, Płotka, Szymon, Puybareau, Elodie, Rajpoot, Nasir, Ryu, Kanghyun, Saeed, Numan, Shephard, Adam, Shi, Pengcheng, Štepec, Dejan, Subedi, Ronast, Tochon, Guillaume, Torres, Helena R., Urien, Helene, Vilaça, João L., Wahid, Kareem Abdul, Wang, Haojie, Wang, Jiacheng, Wang, Liansheng, Wang, Xiyue, Wiestler, Benedikt, Wodzinski, Marek, Xia, Fangfang, Xie, Juanying, Xiong, Zhiwei, Yang, Sen, Yang, Yanwu, Zhao, Zixuan, Maier-Hein, Klaus, Jäger, Paul F., Kopp-Schneider, Annette, and Maier-Hein, Lena

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

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multi-center study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and postprocessing (66%). The "typical" lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work., Comment: accepted to CVPR 2023

Published
2023

4. Biomedical image analysis competitions: The state of current participation practice

Author

Eisenmann, Matthias, Reinke, Annika, Weru, Vivienn, Tizabi, Minu Dietlinde, Isensee, Fabian, Adler, Tim J., Godau, Patrick, Cheplygina, Veronika, Kozubek, Michal, Ali, Sharib, Gupta, Anubha, Kybic, Jan, Noble, Alison, de Solórzano, Carlos Ortiz, Pachade, Samiksha, Petitjean, Caroline, Sage, Daniel, Wei, Donglai, Wilden, Elizabeth, Alapatt, Deepak, Andrearczyk, Vincent, Baid, Ujjwal, Bakas, Spyridon, Balu, Niranjan, Bano, Sophia, Bawa, Vivek Singh, Bernal, Jorge, Bodenstedt, Sebastian, Casella, Alessandro, Choi, Jinwook, Commowick, Olivier, Daum, Marie, Depeursinge, Adrien, Dorent, Reuben, Egger, Jan, Eichhorn, Hannah, Engelhardt, Sandy, Ganz, Melanie, Girard, Gabriel, Hansen, Lasse, Heinrich, Mattias, Heller, Nicholas, Hering, Alessa, Huaulmé, Arnaud, Kim, Hyunjeong, Landman, Bennett, Li, Hongwei Bran, Li, Jianning, Ma, Jun, Martel, Anne, Martín-Isla, Carlos, Menze, Bjoern, Nwoye, Chinedu Innocent, Oreiller, Valentin, Padoy, Nicolas, Pati, Sarthak, Payette, Kelly, Sudre, Carole, van Wijnen, Kimberlin, Vardazaryan, Armine, Vercauteren, Tom, Wagner, Martin, Wang, Chuanbo, Yap, Moi Hoon, Yu, Zeyun, Yuan, Chun, Zenk, Maximilian, Zia, Aneeq, Zimmerer, David, Bao, Rina, Choi, Chanyeol, Cohen, Andrew, Dzyubachyk, Oleh, Galdran, Adrian, Gan, Tianyuan, Guo, Tianqi, Gupta, Pradyumna, Haithami, Mahmood, Ho, Edward, Jang, Ikbeom, Li, Zhili, Luo, Zhengbo, Lux, Filip, Makrogiannis, Sokratis, Müller, Dominik, Oh, Young-tack, Pang, Subeen, Pape, Constantin, Polat, Gorkem, Reed, Charlotte Rosalie, Ryu, Kanghyun, Scherr, Tim, Thambawita, Vajira, Wang, Haoyu, Wang, Xinliang, Xu, Kele, Yeh, Hung, Yeo, Doyeob, Yuan, Yixuan, Zeng, Yan, Zhao, Xin, Abbing, Julian, Adam, Jannes, Adluru, Nagesh, Agethen, Niklas, Ahmed, Salman, Khalil, Yasmina Al, Alenyà, Mireia, Alhoniemi, Esa, An, Chengyang, Anwar, Talha, Arega, Tewodros Weldebirhan, Avisdris, Netanell, Aydogan, Dogu Baran, Bai, Yingbin, Calisto, Maria Baldeon, Basaran, Berke Doga, Beetz, Marcel, Bian, Cheng, Bian, Hao, Blansit, Kevin, Bloch, Louise, Bohnsack, Robert, Bosticardo, Sara, Breen, Jack, Brudfors, Mikael, Brüngel, Raphael, Cabezas, Mariano, Cacciola, Alberto, Chen, Zhiwei, Chen, Yucong, Chen, Daniel Tianming, Cho, Minjeong, Choi, Min-Kook, Xie, Chuantao Xie Chuantao, Cobzas, Dana, Cohen-Adad, Julien, Acero, Jorge Corral, Das, Sujit Kumar, de Oliveira, Marcela, Deng, Hanqiu, Dong, Guiming, Doorenbos, Lars, Efird, Cory, Escalera, Sergio, Fan, Di, Serj, Mehdi Fatan, Fenneteau, Alexandre, Fidon, Lucas, Filipiak, Patryk, Finzel, René, Freitas, Nuno R., Friedrich, Christoph M., Fulton, Mitchell, Gaida, Finn, Galati, Francesco, Galazis, Christoforos, Gan, Chang Hee, Gao, Zheyao, Gao, Shengbo, Gazda, Matej, Gerats, Beerend, Getty, Neil, Gibicar, Adam, Gifford, Ryan, Gohil, Sajan, Grammatikopoulou, Maria, Grzech, Daniel, Güley, Orhun, Günnemann, Timo, Guo, Chunxu, Guy, Sylvain, Ha, Heonjin, Han, Luyi, Han, Il Song, Hatamizadeh, Ali, He, Tian, Heo, Jimin, Hitziger, Sebastian, Hong, SeulGi, Hong, SeungBum, Huang, Rian, Huang, Ziyan, Huellebrand, Markus, Huschauer, Stephan, Hussain, Mustaffa, Inubushi, Tomoo, Polat, Ece Isik, Jafaritadi, Mojtaba, Jeong, SeongHun, Jian, Bailiang, Jiang, Yuanhong, Jiang, Zhifan, Jin, Yueming, Joshi, Smriti, Kadkhodamohammadi, Abdolrahim, Kamraoui, Reda Abdellah, Kang, Inha, Kang, Junghwa, Karimi, Davood, Khademi, April, Khan, Muhammad Irfan, Khan, Suleiman A., Khantwal, Rishab, Kim, Kwang-Ju, Kline, Timothy, Kondo, Satoshi, Kontio, Elina, Krenzer, Adrian, Kroviakov, Artem, Kuijf, Hugo, Kumar, Satyadwyoom, La Rosa, Francesco, Lad, Abhi, Lee, Doohee, Lee, Minho, Lena, Chiara, Li, Hao, Li, Ling, Li, Xingyu, Liao, Fuyuan, Liao, KuanLun, Oliveira, Arlindo Limede, Lin, Chaonan, Lin, Shan, Linardos, Akis, Linguraru, Marius George, Liu, Han, Liu, Tao, Liu, Di, Liu, Yanling, Lourenço-Silva, João, Lu, Jingpei, Lu, Jiangshan, Luengo, Imanol, Lund, Christina B., Luu, Huan Minh, Lv, Yi, Macar, Uzay, Maechler, Leon, L., Sina Mansour, Marshall, Kenji, Mazher, Moona, McKinley, Richard, Medela, Alfonso, Meissen, Felix, Meng, Mingyuan, Miller, Dylan, Mirjahanmardi, Seyed Hossein, Mishra, Arnab, Mitha, Samir, Mohy-ud-Din, Hassan, Mok, Tony Chi Wing, Murugesan, Gowtham Krishnan, Karthik, Enamundram Naga, Nalawade, Sahil, Nalepa, Jakub, Naser, Mohamed, Nateghi, Ramin, Naveed, Hammad, Nguyen, Quang-Minh, Quoc, Cuong Nguyen, Nichyporuk, Brennan, Oliveira, Bruno, Owen, David, Pal, Jimut Bahan, Pan, Junwen, Pan, Wentao, Pang, Winnie, Park, Bogyu, Pawar, Vivek, Pawar, Kamlesh, Peven, Michael, Philipp, Lena, Pieciak, Tomasz, Plotka, Szymon, Plutat, Marcel, Pourakpour, Fattaneh, Preložnik, Domen, Punithakumar, Kumaradevan, Qayyum, Abdul, Queirós, Sandro, Rahmim, Arman, Razavi, Salar, Ren, Jintao, Rezaei, Mina, Rico, Jonathan Adam, Rieu, ZunHyan, Rink, Markus, Roth, Johannes, Ruiz-Gonzalez, Yusely, Saeed, Numan, Saha, Anindo, Salem, Mostafa, Sanchez-Matilla, Ricardo, Schilling, Kurt, Shao, Wei, Shen, Zhiqiang, Shi, Ruize, Shi, Pengcheng, Sobotka, Daniel, Soulier, Théodore, Fadida, Bella Specktor, Stoyanov, Danail, Mun, Timothy Sum Hon, Sun, Xiaowu, Tao, Rong, Thaler, Franz, Théberge, Antoine, Thielke, Felix, Torres, Helena, Wahid, Kareem A., Wang, Jiacheng, Wang, YiFei, Wang, Wei, Wang, Xiong, Wen, Jianhui, Wen, Ning, Wodzinski, Marek, Wu, Ye, Xia, Fangfang, Xiang, Tianqi, Xiaofei, Chen, Xu, Lizhan, Xue, Tingting, Yang, Yuxuan, Yang, Lin, Yao, Kai, Yao, Huifeng, Yazdani, Amirsaeed, Yip, Michael, Yoo, Hwanseung, Yousefirizi, Fereshteh, Yu, Shunkai, Yu, Lei, Zamora, Jonathan, Zeineldin, Ramy Ashraf, Zeng, Dewen, Zhang, Jianpeng, Zhang, Bokai, Zhang, Jiapeng, Zhang, Fan, Zhang, Huahong, Zhao, Zhongchen, Zhao, Zixuan, Zhao, Jiachen, Zhao, Can, Zheng, Qingshuo, Zhi, Yuheng, Zhou, Ziqi, Zou, Baosheng, Maier-Hein, Klaus, Jäger, Paul F., Kopp-Schneider, Annette, and Maier-Hein, Lena

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

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Published
2022

5. MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images

Author

Li, Lei, Wu, Fuping, Wang, Sihan, Luo, Xinzhe, Martin-Isla, Carlos, Zhai, Shuwei, Zhang, Jianpeng, Liu7, Yanfei, Zhang, Zhen, Ankenbrand, Markus J., Jiang, Haochuan, Zhang, Xiaoran, Wang, Linhong, Arega, Tewodros Weldebirhan, Altunok, Elif, Zhao, Zhou, Li, Feiyan, Ma, Jun, Yang, Xiaoping, Puybareau, Elodie, Oksuz, Ilkay, Bricq, Stephanie, Li, Weisheng, Punithakumar, Kumaradevan, Tsaftaris, Sotirios A., Schreiber, Laura M., Yang, Mingjing, Liu, Guocai, Xia, Yong, Wang, Guotai, Escalera, Sergio, and Zhuang, Xiahai

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on myocardium is the key to this assessment. This work defines a new task of medical image analysis, i.e., to perform myocardial pathology segmentation (MyoPS) combining three-sequence cardiac magnetic resonance (CMR) images, which was first proposed in the MyoPS challenge, in conjunction with MICCAI 2020. The challenge provided 45 paired and pre-aligned CMR images, allowing algorithms to combine the complementary information from the three CMR sequences for pathology segmentation. In this article, we provide details of the challenge, survey the works from fifteen participants and interpret their methods according to five aspects, i.e., preprocessing, data augmentation, learning strategy, model architecture and post-processing. In addition, we analyze the results with respect to different factors, in order to examine the key obstacles and explore potential of solutions, as well as to provide a benchmark for future research. We conclude that while promising results have been reported, the research is still in the early stage, and more in-depth exploration is needed before a successful application to the clinics. Note that MyoPS data and evaluation tool continue to be publicly available upon registration via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/myops20/).

Published
2022

6. Domain generalization in deep learning for contrast-enhanced imaging

Author

Sendra-Balcells, Carla, Campello, Víctor M., Martín-Isla, Carlos, Viladés, David, Descalzo, Martín L., Guala, Andrea, Rodríguez-Palomares, José F., and Lekadir, Karim

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

The domain generalization problem has been widely investigated in deep learning for non-contrast imaging over the last years, but it received limited attention for contrast-enhanced imaging. However, there are marked differences in contrast imaging protocols across clinical centers, in particular in the time between contrast injection and image acquisition, while access to multi-center contrast-enhanced image data is limited compared to available datasets for non-contrast imaging. This calls for new tools for generalizing single-domain, single-center deep learning models across new unseen domains and clinical centers in contrast-enhanced imaging. In this paper, we present an exhaustive evaluation of deep learning techniques to achieve generalizability to unseen clinical centers for contrast-enhanced image segmentation. To this end, several techniques are investigated, optimized and systematically evaluated, including data augmentation, domain mixing, transfer learning and domain adaptation. To demonstrate the potential of domain generalization for contrast-enhanced imaging, the methods are evaluated for ventricular segmentation in contrast-enhanced cardiac magnetic resonance imaging (MRI). The results are obtained based on a multi-center cardiac contrast-enhanced MRI dataset acquired in four hospitals located in three countries (France, Spain and China). They show that the combination of data augmentation and transfer learning can lead to single-center models that generalize well to new clinical centers not included during training. Single-domain neural networks enriched with suitable generalization procedures can reach and even surpass the performance of multi-center, multi-vendor models in contrast-enhanced imaging, hence eliminating the need for comprehensive multi-center datasets to train generalizable models.

Published
2021
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7. Auditing Unfair Biases in CNN-Based Diagnosis of Alzheimer’s Disease

Author

Dang, Vien Ngoc, Casamitjana, Adrià, Starmans, Martijn P. A., Martín-Isla, Carlos, Hernández-González, Jerónimo, Lekadir, Karim, Disease Neuroimaging Initiative, for the Alzheimer’s, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wesarg, Stefan, editor, Puyol Antón, Esther, editor, Baxter, John S. H., editor, Erdt, Marius, editor, Drechsler, Klaus, editor, Oyarzun Laura, Cristina, editor, Freiman, Moti, editor, Chen, Yufei, editor, Rekik, Islem, editor, Eagleson, Roy, editor, Feragen, Aasa, editor, King, Andrew P., editor, Cheplygina, Veronika, editor, Ganz-Benjaminsen, Melani, editor, Ferrante, Enzo, editor, Glocker, Ben, editor, Moyer, Daniel, editor, and Petersen, Eikel, editor

Published
2023
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8. Combining Multi-Sequence and Synthetic Images for Improved Segmentation of Late Gadolinium Enhancement Cardiac MRI

Author

Campello, Víctor M., Martín-Isla, Carlos, Izquierdo, Cristian, Petersen, Steffen E., Ballester, Miguel A. González, and Lekadir, Karim

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Accurate segmentation of the cardiac boundaries in late gadolinium enhancement magnetic resonance images (LGE-MRI) is a fundamental step for accurate quantification of scar tissue. However, while there are many solutions for automatic cardiac segmentation of cine images, the presence of scar tissue can make the correct delineation of the myocardium in LGE-MRI challenging even for human experts. As part of the Multi-Sequence Cardiac MR Segmentation Challenge, we propose a solution for LGE-MRI segmentation based on two components. First, a generative adversarial network is trained for the task of modality-to-modality translation between cine and LGE-MRI sequences to obtain extra synthetic images for both modalities. Second, a deep learning model is trained for segmentation with different combinations of original, augmented and synthetic sequences. Our results based on three magnetic resonance sequences (LGE, bSSFP and T2) from 45 different patients show that the multi-sequence model training integrating synthetic images and data augmentation improves in the segmentation over conventional training with real datasets. In conclusion, the accuracy of the segmentation of LGE-MRI images can be improved by using complementary information provided by non-contrast MRI sequences., Comment: 10 pages, Accepted to MS-CMRSeg Challenge (STACOM 2019), reference added and affiliations updated

Published
2019

9. MyoPS: A benchmark of myocardial pathology segmentation combining three-sequence cardiac magnetic resonance images

Author

Li, Lei, Wu, Fuping, Wang, Sihan, Luo, Xinzhe, Martín-Isla, Carlos, Zhai, Shuwei, Zhang, Jianpeng, Liu, Yanfei, Zhang, Zhen, Ankenbrand, Markus J., Jiang, Haochuan, Zhang, Xiaoran, Wang, Linhong, Arega, Tewodros Weldebirhan, Altunok, Elif, Zhao, Zhou, Li, Feiyan, Ma, Jun, Yang, Xiaoping, Puybareau, Elodie, Oksuz, Ilkay, Bricq, Stephanie, Li, Weisheng, Punithakumar, Kumaradevan, Tsaftaris, Sotirios A., Schreiber, Laura M., Yang, Mingjing, Liu, Guocai, Xia, Yong, Wang, Guotai, Escalera, Sergio, and Zhuang, Xiahai

Published
2023
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10. nn-UNet Training on CycleGAN-Translated Images for Cross-modal Domain Adaptation in Biomedical Imaging

Author

Joshi, Smriti, Osuala, Richard, Martín-Isla, Carlos, Campello, Victor M., Sendra-Balcells, Carla, Lekadir, Karim, Escalera, Sergio, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Crimi, Alessandro, editor, and Bakas, Spyridon, editor

Published
2022
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13. Stacked BCDU-Net with Semantic CMR Synthesis: Application to Myocardial Pathology Segmentation Challenge

Author

Martín-Isla, Carlos, Asadi-Aghbolaghi, Maryam, Gkontra, Polyxeni, Campello, Victor M., Escalera, Sergio, Lekadir, Karim, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Zhuang, Xiahai, editor, and Li, Lei, editor

Published
2020
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14. Combining Multi-Sequence and Synthetic Images for Improved Segmentation of Late Gadolinium Enhancement Cardiac MRI

Author

Campello, Víctor M., Martín-Isla, Carlos, Izquierdo, Cristian, Petersen, Steffen E., Ballester, Miguel A. González, Lekadir, Karim, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Pop, Mihaela, editor, Sermesant, Maxime, editor, Camara, Oscar, editor, Zhuang, Xiahai, editor, Li, Shuo, editor, Young, Alistair, editor, Mansi, Tommaso, editor, and Suinesiaputra, Avan, editor

Published
2020
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16. Deep Learning Segmentation of the Right Ventricle in Cardiac MRI: The M&Ms Challenge

Author

Martin-Isla, Carlos, Campello, Victor M., Izquierdo, Cristian, Kushibar, Kaisar, Sendra-Balcells, Carla, Gkontra, Polyxeni, Sojoudi, Alireza, Fulton, Mitchell J., Arega, Tewodros Weldebirhan, Punithakumar, Kumaradevan, Li, Lei, Sun, Xiaowu, Al Khalil, Yasmina, Liu, Di, Jabbar, Sana, Queiros, Sandro, Galati, Francesco, Mazher, Moona, Gao, Zheyao, Beetz, Marcel, Tautz, Lennart, Galazis, Christoforos, Varela, Marta, Hullebrand, Markus, Grau, Vicente, Zhuang, Xiahai, Puig, Domenec, Zuluaga, Maria A., Mohy-ud-Din, Hassan, Metaxas, Dimitris, Breeuwer, Marcel, van der Geest, Rob J., Noga, Michelle, Bricq, Stephanie, Rentschler, Mark E., Guala, Andrea, Petersen, Steffen E., Escalera, Sergio, Palomares, Jose F. Rodriguez, Lekadir, Karim, Martin-Isla, Carlos, Campello, Victor M., Izquierdo, Cristian, Kushibar, Kaisar, Sendra-Balcells, Carla, Gkontra, Polyxeni, Sojoudi, Alireza, Fulton, Mitchell J., Arega, Tewodros Weldebirhan, Punithakumar, Kumaradevan, Li, Lei, Sun, Xiaowu, Al Khalil, Yasmina, Liu, Di, Jabbar, Sana, Queiros, Sandro, Galati, Francesco, Mazher, Moona, Gao, Zheyao, Beetz, Marcel, Tautz, Lennart, Galazis, Christoforos, Varela, Marta, Hullebrand, Markus, Grau, Vicente, Zhuang, Xiahai, Puig, Domenec, Zuluaga, Maria A., Mohy-ud-Din, Hassan, Metaxas, Dimitris, Breeuwer, Marcel, van der Geest, Rob J., Noga, Michelle, Bricq, Stephanie, Rentschler, Mark E., Guala, Andrea, Petersen, Steffen E., Escalera, Sergio, Palomares, Jose F. Rodriguez, and Lekadir, Karim

Abstract

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 12 th 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.

Published
2023

17. Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation

Author

Campello, Victor M., Gkontra, Polyxeni, Izquierdo, Cristian, Martin-Isla, Carlos, Sojoudi, Alireza, Full, Peter M., Maier-Hein, Klaus, Zhang, Yao, He, Zhiqiang, Ma, Jun, Parreno, Mario, Albiol, Alberto, Kong, Fanwei, Shadden, Shawn C., Corral Acero, Jorge, Sundaresan, Vaanathi, Saber, Mina, Elattar, Mustafa, Li, Hongwei, Menze, Bjoern, Khader, Firas, Haarburger, Christoph, Scannell, Cian M., Veta, Mitko, Carscadden, Adam, Punithakumar, Kumaradevan, Liu, Xiao, Tsaftaris, Sotirios A., Huang, Xiaoqiong, Yang, Xin, Li, Lei, Zhuang, Xiahai, Viladés Medel, David, Descalzo, Martin, Guala, Andrea, Mura, Lucía La, Friedrich, Matthias G., Garg, Ria, Lebel, Julie, Henriques, Filipe., Karakas, Mahir, Cavus, Ersin, Petersen, Steffen E., Escalera, Sergio, Segui, Santi, Rodriguez-Palomares, Jose F.., Lekadir, Karim, Universitat Autònoma de Barcelona, Medical Image Analysis, and EAISI Health

Subjects
Computer science, Processament digital d'imatges, computer.software_genre, SDG 3 – Goede gezondheid en welzijn, Field (computer science), 030218 nuclear medicine & medical imaging, 0302 clinical medicine, Resource (project management), Segmentation, Cardiac imaging, Image segmentation, domain adaption, Radiological and Ultrasound Technology, Heart, Benchmarking, Magnetic Resonance Imaging, Hospitals, Computer Science Applications, Generalizability, Domain adaption, Biomedical engineering, data augmentation, Data augmentation, Machine learning, 03 medical and health sciences, Magnetic resonance imaging, SDG 3 - Good Health and Well-being, Imatges per ressonància magnètica, Aprenentatge automàtic, Humans, Training, Generalizability theory, Electrical and Electronic Engineering, generalizability, business.industry, Deep learning, deep learning, Public dataset, public dataset, Cardiac Imaging Techniques, Cardiovascular magnetic resonance, Artificial intelligence, business, computer, Digital image processing, Software, Protocols
Abstract

The emergence of deep learning has considerably advanced the state-of-the-art in cardiac magnetic resonance (CMR) segmentation. Many techniques have been proposed over the last few years, bringing the accuracy of automated segmentation close to human performance. However, these models have been all too often trained and validated using cardiac imaging samples from single clinical centres or homogeneous imaging protocols. This has prevented the development and validation of models that are generalizable across different clinical centres, imaging conditions or scanner vendors. To promote further research and scientific benchmarking in the field of generalizable deep learning for cardiac segmentation, this paper presents the results of the Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation (M&Ms) Challenge, which was recently organized as part of the MICCAI 2020 Conference. A total of 14 teams submitted different solutions to the problem, combining various baseline models, data augmentation strategies, and domain adaptation techniques. The obtained results indicate the importance of intensity-driven data augmentation, as well as the need for further research to improve generalizability towards unseen scanner vendors or new imaging protocols. Furthermore, we present a new resource of 375 heterogeneous CMR datasets acquired by using four different scanner vendors in six hospitals and three different countries (Spain, Canada and Germany), which we provide as open-access for the community to enable future research in the field.

Published
2021

18. Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge

Author

Campello, Victor M, Gkontra, Polyxeni, Izquierdo, Cristian, Martin-Isla, Carlos, et al, Menze, Bjoern, and University of Zurich

Subjects
Radiological and Ultrasound Technology, 610 Medicine & health, Electrical and Electronic Engineering, 11493 Department of Quantitative Biomedicine, Software, Computer Science Applications
Published
2021

20. Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge

Author

Liu, Xiao, Campello, Victor M., Gkontra, Polyxeni, Izquierdo, Cristian, Martin-Isla, Carlos, Sojoudi, Alireza, Full, Peter M., Maier-Hein, Klaus, Zhang, Yao, He, Zhiqiang, Ma, Jun, Parreno, Mario, Albiol, Alberto, Kong, Fanwei, Shadden, Shawn C., Acero, Jorge Corral, Sundaresan, Vaanathi, Saber, Mina, Elattar, Mustafa, Li, Hongwei, Menze, Bjoern, Khader, Firas, Haarburger, Christoph, Scannell, Cian M., Veta, Mitko, Carscadden, Adam, Punithakumar, Kumaradevan, Tsaftaris, Sotirios A., Huang, Xiaoqiong, Yang, Xin, Li, Lei, Zhuang, Xiahai, Vilades, David, Descalzo, Martin L., Guala, Andrea, La Mura, Lucia, Friedrich, Matthias G., Garg, Ria, Lebel, Julie, Henriques, Filipe, Karakas, Mahir, Cavus, Ersin, Petersen, Steffen E., Escalera, Sergio, Segui, Santi, Palomares, Jose F.Rodriguez, Lekadir, Karim, Liu, Xiao, Campello, Victor M., Gkontra, Polyxeni, Izquierdo, Cristian, Martin-Isla, Carlos, Sojoudi, Alireza, Full, Peter M., Maier-Hein, Klaus, Zhang, Yao, He, Zhiqiang, Ma, Jun, Parreno, Mario, Albiol, Alberto, Kong, Fanwei, Shadden, Shawn C., Acero, Jorge Corral, Sundaresan, Vaanathi, Saber, Mina, Elattar, Mustafa, Li, Hongwei, Menze, Bjoern, Khader, Firas, Haarburger, Christoph, Scannell, Cian M., Veta, Mitko, Carscadden, Adam, Punithakumar, Kumaradevan, Tsaftaris, Sotirios A., Huang, Xiaoqiong, Yang, Xin, Li, Lei, Zhuang, Xiahai, Vilades, David, Descalzo, Martin L., Guala, Andrea, La Mura, Lucia, Friedrich, Matthias G., Garg, Ria, Lebel, Julie, Henriques, Filipe, Karakas, Mahir, Cavus, Ersin, Petersen, Steffen E., Escalera, Sergio, Segui, Santi, Palomares, Jose F.Rodriguez, and Lekadir, Karim

Abstract

The emergence of deep learning has considerably advanced the state-of-the-art in cardiac magnetic resonance (CMR) segmentation. Many techniques have been proposed over the last few years, bringing the accuracy of automated segmentation close to human performance. However, these models have been all too often trained and validated using cardiac imaging samples from single clinical centres or homogeneous imaging protocols. This has prevented the development and validation of models that are generalizable across different clinical centres, imaging conditions or scanner vendors. To promote further research and scientific benchmarking in the field of generalizable deep learning for cardiac segmentation, this paper presents the results of the Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation (M&Ms) Challenge, which was recently organized as part of the MICCAI 2020 Conference. A total of 14 teams submitted different solutions to the problem, combining various baseline models, data augmentation strategies, and domain adaptation techniques. The obtained results indicate the importance of intensity-driven data augmentation, as well as the need for further research to improve generalizability towards unseen scanner vendors or new imaging protocols. Furthermore, we present a new resource of 375 heterogeneous CMR datasets acquired by using four different scanner vendors in six hospitals and three different countries (Spain, Canada and Germany), which we provide as open-access for the community to enable future research in the field.

Published
2021

21. Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge

Author

Campello, Victor M; https://orcid.org/0000-0003-1727-983X, Gkontra, Polyxeni; https://orcid.org/0000-0001-8828-6143, Izquierdo, Cristian, Martin-Isla, Carlos, et al, Menze, Bjoern; https://orcid.org/0000-0003-4136-5690, Campello, Victor M; https://orcid.org/0000-0003-1727-983X, Gkontra, Polyxeni; https://orcid.org/0000-0001-8828-6143, Izquierdo, Cristian, Martin-Isla, Carlos, et al, and Menze, Bjoern; https://orcid.org/0000-0003-4136-5690

Abstract

The emergence of deep learning has considerably advanced the state-of-the-art in cardiac magnetic resonance (CMR) segmentation. Many techniques have been proposed over the last few years, bringing the accuracy of automated segmentation close to human performance. However, these models have been all too often trained and validated using cardiac imaging samples from single clinical centres or homogeneous imaging protocols. This has prevented the development and validation of models that are generalizable across different clinical centres, imaging conditions or scanner vendors. To promote further research and scientific benchmarking in the field of generalizable deep learning for cardiac segmentation, this paper presents the results of the Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation (M&Ms) Challenge, which was recently organized as part of the MICCAI 2020 Conference. A total of 14 teams submitted different solutions to the problem, combining various baseline models, data augmentation strategies, and domain adaptation techniques. The obtained results indicate the importance of intensity-driven data augmentation, as well as the need for further research to improve generalizability towards unseen scanner vendors or new imaging protocols. Furthermore, we present a new resource of 375 heterogeneous CMR datasets acquired by using four different scanner vendors in six hospitals and three different countries (Spain, Canada and Germany), which we provide as open-access for the community to enable future research in the field.

Published
2021

22. Image-Based Cardiac Diagnosis With Machine Learning: A Review

Author

Martin-Isla, Carlos, Campello, Victor M, Izquierdo, Cristian, Raisi-Estabragh, Zahra, Baeßler, Bettina, Petersen, Steffen E, Lekadir, Karim, Martin-Isla, Carlos, Campello, Victor M, Izquierdo, Cristian, Raisi-Estabragh, Zahra, Baeßler, Bettina, Petersen, Steffen E, and Lekadir, Karim

Abstract

Cardiac imaging plays an important role in the diagnosis of cardiovascular disease (CVD). Until now, its role has been limited to visual and quantitative assessment of cardiac structure and function. However, with the advent of big data and machine learning, new opportunities are emerging to build artificial intelligence tools that will directly assist the clinician in the diagnosis of CVDs. This paper presents a thorough review of recent works in this field and provide the reader with a detailed presentation of the machine learning methods that can be further exploited to enable more automated, precise and early diagnosis of most CVDs.

Published
2020

25. Pedal de guitarra via mòbil Android

Author

Universitat Politècnica de Catalunya. Departament de Ciències de la Computació, Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica, Marco Gómez, Jordi, Salaet Pereira, Juan Fernando, Martin Isla, Carlos, Universitat Politècnica de Catalunya. Departament de Ciències de la Computació, Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica, Marco Gómez, Jordi, Salaet Pereira, Juan Fernando, and Martin Isla, Carlos

Published
2014

26. Pedal de guitarra via mòbil Android

Author

Martin Isla, Carlos, Universitat Politècnica de Catalunya. Departament de Ciències de la Computació, Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica, Marco Gómez, Jordi, and Salaet Pereira, Juan Fernando

Subjects
Aplicacions mòbils, Informàtica::Aplicacions de la informàtica [Àrees temàtiques de la UPC], Electric guitar--Pedaling, Guitarra elèctrica--Pedals, Mobile apps

27. Using Out-of-Distribution Detection for Model Refinement in Cardiac Image Segmentation

Author

Galati, Francesco, Zuluaga, Maria A., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Puyol Antón, Esther, editor, Pop, Mihaela, editor, Martín-Isla, Carlos, editor, Sermesant, Maxime, editor, Suinesiaputra, Avan, editor, Camara, Oscar, editor, Lekadir, Karim, editor, and Young, Alistair, editor

Published
2022
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28. Late Fusion U-Net with GAN-Based Augmentation for Generalizable Cardiac MRI Segmentation

Author

Al Khalil, Yasmina, Amirrajab, Sina, Pluim, Josien, Breeuwer, Marcel, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Puyol Antón, Esther, editor, Pop, Mihaela, editor, Martín-Isla, Carlos, editor, Sermesant, Maxime, editor, Suinesiaputra, Avan, editor, Camara, Oscar, editor, Lekadir, Karim, editor, and Young, Alistair, editor

Published
2022
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29. Automated Segmentation of the Right Ventricle from Magnetic Resonance Imaging Using Deep Convolutional Neural Networks

Author

Punithakumar, Kumaradevan, Carscadden, Adam, Noga, Michelle, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Puyol Antón, Esther, editor, Pop, Mihaela, editor, Martín-Isla, Carlos, editor, Sermesant, Maxime, editor, Suinesiaputra, Avan, editor, Camara, Oscar, editor, Lekadir, Karim, editor, and Young, Alistair, editor

Published
2022
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30. 3D Right Ventricle Reconstruction from 2D U-Net Segmentation of Sparse Short-Axis and 4-Chamber Cardiac Cine MRI Views

Author

Tautz, Lennart, Walczak, Lars, Manini, Chiara, Hennemuth, Anja, Hüllebrand, Markus, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Puyol Antón, Esther, editor, Pop, Mihaela, editor, Martín-Isla, Carlos, editor, Sermesant, Maxime, editor, Suinesiaputra, Avan, editor, Camara, Oscar, editor, Lekadir, Karim, editor, and Young, Alistair, editor

Published
2022
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31. Multi-disease, Multi-view and Multi-center Right Ventricular Segmentation in Cardiac MRI Using Efficient Late-Ensemble Deep Learning Approach

Author

Mazher, Moona, Qayyum, Abdul, Benzinou, Abdesslam, Abdel-Nasser, Mohamed, Puig, Domenec, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Puyol Antón, Esther, editor, Pop, Mihaela, editor, Martín-Isla, Carlos, editor, Sermesant, Maxime, editor, Suinesiaputra, Avan, editor, Camara, Oscar, editor, Lekadir, Karim, editor, and Young, Alistair, editor

Published
2022
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32. A Multi-view Crossover Attention U-Net Cascade with Fourier Domain Adaptation for Multi-domain Cardiac MRI Segmentation

Author

Beetz, Marcel, Corral Acero, Jorge, Grau, Vicente, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Puyol Antón, Esther, editor, Pop, Mihaela, editor, Martín-Isla, Carlos, editor, Sermesant, Maxime, editor, Suinesiaputra, Avan, editor, Camara, Oscar, editor, Lekadir, Karim, editor, and Young, Alistair, editor

Published
2022
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33. Refined Deep Layer Aggregation for Multi-Disease, Multi-View & Multi-Center Cardiac MR Segmentation

Author

Liu, Di, Yan, Zhennan, Chang, Qi, Axel, Leon, Metaxas, Dimitris N., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Puyol Antón, Esther, editor, Pop, Mihaela, editor, Martín-Isla, Carlos, editor, Sermesant, Maxime, editor, Suinesiaputra, Avan, editor, Camara, Oscar, editor, Lekadir, Karim, editor, and Young, Alistair, editor

Published
2022
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34. Consistency Based Co-segmentation for Multi-view Cardiac MRI Using Vision Transformer

Author

Gao, Zheyao, Zhuang, Xiahai, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Puyol Antón, Esther, editor, Pop, Mihaela, editor, Martín-Isla, Carlos, editor, Sermesant, Maxime, editor, Suinesiaputra, Avan, editor, Camara, Oscar, editor, Lekadir, Karim, editor, and Young, Alistair, editor

Published
2022
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35. Tempera: Spatial Transformer Feature Pyramid Network for Cardiac MRI Segmentation

Author

Galazis, Christoforos, Wu, Huiyi, Li, Zhuoyu, Petri, Camille, Bharath, Anil A., Varela, Marta, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Puyol Antón, Esther, editor, Pop, Mihaela, editor, Martín-Isla, Carlos, editor, Sermesant, Maxime, editor, Suinesiaputra, Avan, editor, Camara, Oscar, editor, Lekadir, Karim, editor, and Young, Alistair, editor

Published
2022
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36. Deformable Bayesian Convolutional Networks for Disease-Robust Cardiac MRI Segmentation

Author

Fulton, Mitchell J., Heckman, Christoffer R., Rentschler, Mark E., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Puyol Antón, Esther, editor, Pop, Mihaela, editor, Martín-Isla, Carlos, editor, Sermesant, Maxime, editor, Suinesiaputra, Avan, editor, Camara, Oscar, editor, Lekadir, Karim, editor, and Young, Alistair, editor

Published
2022
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37. Right Ventricular Segmentation from Short- and Long-Axis MRIs via Information Transition

Author

Li, Lei, Ding, Wangbin, Huang, Liqin, Zhuang, Xiahai, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Puyol Antón, Esther, editor, Pop, Mihaela, editor, Martín-Isla, Carlos, editor, Sermesant, Maxime, editor, Suinesiaputra, Avan, editor, Camara, Oscar, editor, Lekadir, Karim, editor, and Young, Alistair, editor

Published
2022
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38. Multi-view SA-LA Net: A Framework for Simultaneous Segmentation of RV on Multi-view Cardiac MR Images

Author

Jabbar, Sana, Bukhari, Syed Talha, Mohy-ud-Din, Hassan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Puyol Antón, Esther, editor, Pop, Mihaela, editor, Martín-Isla, Carlos, editor, Sermesant, Maxime, editor, Suinesiaputra, Avan, editor, Camara, Oscar, editor, Lekadir, Karim, editor, and Young, Alistair, editor

Published
2022
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39. Using MRI-specific Data Augmentation to Enhance the Segmentation of Right Ventricle in Multi-disease, Multi-center and Multi-view Cardiac MRI

Author

Arega, Tewodros Weldebirhan, Legrand, François, Bricq, Stéphanie, Meriaudeau, Fabrice, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Puyol Antón, Esther, editor, Pop, Mihaela, editor, Martín-Isla, Carlos, editor, Sermesant, Maxime, editor, Suinesiaputra, Avan, editor, Camara, Oscar, editor, Lekadir, Karim, editor, and Young, Alistair, editor

Published
2022
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40. Right Ventricular Segmentation in Multi-view Cardiac MRI Using a Unified U-net Model

Author

Queirós, Sandro, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Puyol Antón, Esther, editor, Pop, Mihaela, editor, Martín-Isla, Carlos, editor, Sermesant, Maxime, editor, Suinesiaputra, Avan, editor, Camara, Oscar, editor, Lekadir, Karim, editor, and Young, Alistair, editor

Published
2022
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41. Generating Subpopulation-Specific Biventricular Anatomy Models Using Conditional Point Cloud Variational Autoencoders

Author

Beetz, Marcel, Banerjee, Abhirup, Grau, Vicente, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Puyol Antón, Esther, editor, Pop, Mihaela, editor, Martín-Isla, Carlos, editor, Sermesant, Maxime, editor, Suinesiaputra, Avan, editor, Camara, Oscar, editor, Lekadir, Karim, editor, and Young, Alistair, editor

Published
2022
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42. The Impact of Domain Shift on Left and Right Ventricle Segmentation in Short Axis Cardiac MR Images

Author

Ugurlu, Devran, Puyol-Antón, Esther, Ruijsink, Bram, Young, Alistair, Machado, Inês, Hammernik, Kerstin, King, Andrew P., Schnabel, Julia A., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Puyol Antón, Esther, editor, Pop, Mihaela, editor, Martín-Isla, Carlos, editor, Sermesant, Maxime, editor, Suinesiaputra, Avan, editor, Camara, Oscar, editor, Lekadir, Karim, editor, and Young, Alistair, editor

Published
2022
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43. Right Ventricle Segmentation via Registration and Multi-input Modalities in Cardiac Magnetic Resonance Imaging from Multi-disease, Multi-view and Multi-center

Author

Sun, Xiaowu, Cheng, Li-Hsin, van der Geest, Rob J., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Puyol Antón, Esther, editor, Pop, Mihaela, editor, Martín-Isla, Carlos, editor, Sermesant, Maxime, editor, Suinesiaputra, Avan, editor, Camara, Oscar, editor, Lekadir, Karim, editor, and Young, Alistair, editor

Published
2022
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44. Vessel Extraction and Analysis of Aortic Dissection

Author

Fang, Hui, Guo, Zhanqiang, Shao, Guozhu, Tan, Zimeng, Yu, Jinyang, Liu, Jia, Cao, Yukun, Zhou, Jie, Shi, Heshui, Feng, Jianjiang, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Puyol Antón, Esther, editor, Pop, Mihaela, editor, Martín-Isla, Carlos, editor, Sermesant, Maxime, editor, Suinesiaputra, Avan, editor, Camara, Oscar, editor, Lekadir, Karim, editor, and Young, Alistair, editor

Published
2022
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45. Characterizing Myocardial Ischemia and Reperfusion Patterns with Hierarchical Manifold Learning

Author

Freiche, Benoît, Clarysse, Patrick, Viallon, Magalie, Croisille, Pierre, Duchateau, Nicolas, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Puyol Antón, Esther, editor, Pop, Mihaela, editor, Martín-Isla, Carlos, editor, Sermesant, Maxime, editor, Suinesiaputra, Avan, editor, Camara, Oscar, editor, Lekadir, Karim, editor, and Young, Alistair, editor

Published
2022
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46. A Bi-atrial Statistical Shape Model as a Basis to Classify Left Atrial Enlargement from Simulated and Clinical 12-Lead ECGs

Author

Nagel, Claudia, Schaufelberger, Matthias, Dössel, Olaf, Loewe, Axel, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Puyol Antón, Esther, editor, Pop, Mihaela, editor, Martín-Isla, Carlos, editor, Sermesant, Maxime, editor, Suinesiaputra, Avan, editor, Camara, Oscar, editor, Lekadir, Karim, editor, and Young, Alistair, editor

Published
2022
Full Text
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47. Predicting 3D Cardiac Deformations with Point Cloud Autoencoders

Author

Beetz, Marcel, Ossenberg-Engels, Julius, Banerjee, Abhirup, Grau, Vicente, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Puyol Antón, Esther, editor, Pop, Mihaela, editor, Martín-Isla, Carlos, editor, Sermesant, Maxime, editor, Suinesiaputra, Avan, editor, Camara, Oscar, editor, Lekadir, Karim, editor, and Young, Alistair, editor

Published
2022
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48. Novel Imaging Biomarkers to Evaluate Heart Dysfunction Post-chemotherapy: A Preclinical MRI Feasibility Study

Author

Lin, Peter, Escartin, Terenz, Ng, Matthew, Li, Mengyuan, Larsen, Melissa, Barry, Jennifer, Roifman, Idan, Pop, Mihaela, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Puyol Antón, Esther, editor, Pop, Mihaela, editor, Martín-Isla, Carlos, editor, Sermesant, Maxime, editor, Suinesiaputra, Avan, editor, Camara, Oscar, editor, Lekadir, Karim, editor, and Young, Alistair, editor

Published
2022
Full Text
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49. Quality-Aware Cine Cardiac MRI Reconstruction and Analysis from Undersampled K-Space Data

Author

Machado, Inês, Puyol-Antón, Esther, Hammernik, Kerstin, Cruz, Gastão, Ugurlu, Devran, Ruijsink, Bram, Castelo-Branco, Miguel, Young, Alistair, Prieto, Claudia, Schnabel, Julia A., King, Andrew P., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Puyol Antón, Esther, editor, Pop, Mihaela, editor, Martín-Isla, Carlos, editor, Sermesant, Maxime, editor, Suinesiaputra, Avan, editor, Camara, Oscar, editor, Lekadir, Karim, editor, and Young, Alistair, editor

Published
2022
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50. Influence of Morphometric and Mechanical Factors in Thoracic Aorta Finite Element Modeling

Author

Zhang, Ruifen, Sigovan, Monica, Clarysse, Patrick, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Puyol Antón, Esther, editor, Pop, Mihaela, editor, Martín-Isla, Carlos, editor, Sermesant, Maxime, editor, Suinesiaputra, Avan, editor, Camara, Oscar, editor, Lekadir, Karim, editor, and Young, Alistair, editor

Published
2022
Full Text
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