Zobrazeno 1 - 10
of 121
pro vyhledávání: '"Lessmann, Nikolas"'
Deep convolutional neural networks are widely used in medical image segmentation but require many labeled images for training. Annotating three-dimensional medical images is a time-consuming and costly process. To overcome this limitation, we propose
Externí odkaz:
http://arxiv.org/abs/2407.04638
Autor:
Humpire-Mamani, Gabriel Efrain, Jacobs, Colin, Prokop, Mathias, van Ginneken, Bram, Lessmann, Nikolas
Transfer learning leverages pre-trained model features from a large dataset to save time and resources when training new models for various tasks, potentially enhancing performance. Due to the lack of large datasets in the medical imaging domain, tra
Externí odkaz:
http://arxiv.org/abs/2311.05032
Autor:
Mamani, Gabriel Efrain Humpire, Lessmann, Nikolas, Scholten, Ernst Th., Prokop, Mathias, Jacobs, Colin, van Ginneken, Bram
In this study, we introduce a deep learning approach for segmenting kidney parenchyma and kidney abnormalities to support clinicians in identifying and quantifying renal abnormalities such as cysts, lesions, masses, metastases, and primary tumors. Ou
Externí odkaz:
http://arxiv.org/abs/2309.03383
Autor:
van der Graaf, Jasper W., van Hooff, Miranda L., Buckens, Constantinus F. M., Rutten, Matthieu, van Susante, Job L. C., Kroeze, Robert Jan, de Kleuver, Marinus, van Ginneken, Bram, Lessmann, Nikolas
Publikováno v:
Scientific Data 11.1 (2024): 264
This paper presents a large publicly available multi-center lumbar spine magnetic resonance imaging (MRI) dataset with reference segmentations of vertebrae, intervertebral discs (IVDs), and spinal canal. The dataset includes 447 sagittal T1 and T2 MR
Externí odkaz:
http://arxiv.org/abs/2306.12217
Autor:
Hering, Alessa, Hansen, Lasse, Mok, Tony C. W., Chung, Albert C. S., Siebert, Hanna, Häger, Stephanie, Lange, Annkristin, Kuckertz, Sven, Heldmann, Stefan, Shao, Wei, Vesal, Sulaiman, Rusu, Mirabela, Sonn, Geoffrey, Estienne, Théo, Vakalopoulou, Maria, Han, Luyi, Huang, Yunzhi, Yap, Pew-Thian, Brudfors, Mikael, Balbastre, Yaël, Joutard, Samuel, Modat, Marc, Lifshitz, Gal, Raviv, Dan, Lv, Jinxin, Li, Qiang, Jaouen, Vincent, Visvikis, Dimitris, Fourcade, Constance, Rubeaux, Mathieu, Pan, Wentao, Xu, Zhe, Jian, Bailiang, De Benetti, Francesca, Wodzinski, Marek, Gunnarsson, Niklas, Sjölund, Jens, Grzech, Daniel, Qiu, Huaqi, Li, Zeju, Thorley, Alexander, Duan, Jinming, Großbröhmer, Christoph, Hoopes, Andrew, Reinertsen, Ingerid, Xiao, Yiming, Landman, Bennett, Huo, Yuankai, Murphy, Keelin, Lessmann, Nikolas, van Ginneken, Bram, Dalca, Adrian V., Heinrich, Mattias P.
Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically releva
Externí odkaz:
http://arxiv.org/abs/2112.04489
Autor:
Hering, Alessa, Häger, Stephanie, Moltz, Jan, Lessmann, Nikolas, Heldmann, Stefan, van Ginneken, Bram
Deep-learning-based registration methods emerged as a fast alternative to conventional registration methods. However, these methods often still cannot achieve the same performance as conventional registration methods because they are either limited t
Externí odkaz:
http://arxiv.org/abs/2011.14372
Autor:
de Vente, Coen, Boulogne, Luuk H., Venkadesh, Kiran Vaidhya, Sital, Cheryl, Lessmann, Nikolas, Jacobs, Colin, Sánchez, Clara I., van Ginneken, Bram
Amidst the ongoing pandemic, several studies have shown that COVID-19 classification and grading using computed tomography (CT) images can be automated with convolutional neural networks (CNNs). Many of these studies focused on reporting initial resu
Externí odkaz:
http://arxiv.org/abs/2009.09725
Autor:
Lessmann, Nikolas, van Ginneken, Bram
Random transformations are commonly used for augmentation of the training data with the goal of reducing the uniformity of the training samples. These transformations normally aim at variations that can be expected in images from the same modality. H
Externí odkaz:
http://arxiv.org/abs/2003.06158
Autor:
Sekuboyina, Anjany, Husseini, Malek E., Bayat, Amirhossein, Löffler, Maximilian, Liebl, Hans, Li, Hongwei, Tetteh, Giles, Kukačka, Jan, Payer, Christian, Štern, Darko, Urschler, Martin, Chen, Maodong, Cheng, Dalong, Lessmann, Nikolas, Hu, Yujin, Wang, Tianfu, Yang, Dong, Xu, Daguang, Ambellan, Felix, Amiranashvili, Tamaz, Ehlke, Moritz, Lamecker, Hans, Lehnert, Sebastian, Lirio, Marilia, de Olaguer, Nicolás Pérez, Ramm, Heiko, Sahu, Manish, Tack, Alexander, Zachow, Stefan, Jiang, Tao, Ma, Xinjun, Angerman, Christoph, Wang, Xin, Brown, Kevin, Kirszenberg, Alexandre, Puybareau, Élodie, Chen, Di, Bai, Yiwei, Rapazzo, Brandon H., Yeah, Timyoas, Zhang, Amber, Xu, Shangliang, Hou, Feng, He, Zhiqiang, Zeng, Chan, Xiangshang, Zheng, Liming, Xu, Netherton, Tucker J., Mumme, Raymond P., Court, Laurence E., Huang, Zixun, He, Chenhang, Wang, Li-Wen, Ling, Sai Ho, Huynh, Lê Duy, Boutry, Nicolas, Jakubicek, Roman, Chmelik, Jiri, Mulay, Supriti, Sivaprakasam, Mohanasankar, Paetzold, Johannes C., Shit, Suprosanna, Ezhov, Ivan, Wiestler, Benedikt, Glocker, Ben, Valentinitsch, Alexander, Rempfler, Markus, Menze, Björn H., Kirschke, Jan S.
Publikováno v:
Medical Image Analysis, Volume 73, October 2021, 102166
Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit clinical decision-support systems for diagnosis, surgery planning, and p
Externí odkaz:
http://arxiv.org/abs/2001.09193