Zobrazeno 1 - 10
of 124
pro vyhledávání: '"Paetzold, Johannes C"'
Autor:
Lux, Laurin, Berger, Alexander H., Weers, Alexander, Stucki, Nico, Rueckert, Daniel, Bauer, Ulrich, Paetzold, Johannes C.
Topological correctness plays a critical role in many image segmentation tasks, yet most networks are trained using pixel-wise loss functions, such as Dice, neglecting topological accuracy. Existing topology-aware methods often lack robust topologica
Externí odkaz:
http://arxiv.org/abs/2411.03228
Autor:
Mächler, Leon, Grimberg, Gustav, Ezhov, Ivan, Nickel, Manuel, Shit, Suprosanna, Naccache, David, Paetzold, Johannes C.
This paper presents FedPID, our submission to the Federated Tumor Segmentation Challenge 2024 (FETS24). Inspired by FedCostWAvg and FedPIDAvg, our winning contributions to FETS21 and FETS2022, we propose an improved aggregation strategy for federated
Externí odkaz:
http://arxiv.org/abs/2411.02152
Autor:
Graf, Robert, Hunecke, Florian, Pohl, Soeren, Atad, Matan, Moeller, Hendrik, Starck, Sophie, Kroencke, Thomas, Bette, Stefanie, Bamberg, Fabian, Pischon, Tobias, Niendorf, Thoralf, Schmidt, Carsten, Paetzold, Johannes C., Rueckert, Daniel, Kirschke, Jan S
Deep learning has made significant strides in medical imaging, leveraging the use of large datasets to improve diagnostics and prognostics. However, large datasets often come with inherent errors through subject selection and acquisition. In this pap
Externí odkaz:
http://arxiv.org/abs/2410.10220
Autor:
Balcerak, Michal, Amiranashvili, Tamaz, Wagner, Andreas, Weidner, Jonas, Karnakov, Petr, Paetzold, Johannes C., Ezhov, Ivan, Koumoutsakos, Petros, Wiestler, Benedikt, Menze, Bjoern
Physical models in the form of partial differential equations serve as important priors for many under-constrained problems. One such application is tumor treatment planning, which relies on accurately estimating the spatial distribution of tumor cel
Externí odkaz:
http://arxiv.org/abs/2409.20409
Autor:
Prabhakar, Chinmay, Shit, Suprosanna, Musio, Fabio, Yang, Kaiyuan, Amiranashvili, Tamaz, Paetzold, Johannes C., Li, Hongwei Bran, Menze, Bjoern
Blood vessel networks, represented as 3D graphs, help predict disease biomarkers, simulate blood flow, and aid in synthetic image generation, relevant in both clinical and pre-clinical settings. However, generating realistic vessel graphs that corres
Externí odkaz:
http://arxiv.org/abs/2407.05842
In this work, we propose an efficient algorithm for the calculation of the Betti matching, which can be used as a loss function to train topology aware segmentation networks. Betti matching loss builds on techniques from topological data analysis, sp
Externí odkaz:
http://arxiv.org/abs/2407.04683
Autor:
Graf, Robert, Platzek, Paul-Sören, Riedel, Evamaria Olga, Ramschütz, Constanze, Starck, Sophie, Möller, Hendrik Kristian, Atad, Matan, Völzke, Henry, Bülow, Robin, Schmidt, Carsten Oliver, Rüdebusch, Julia, Jung, Matthias, Reisert, Marco, Weiss, Jakob, Löffler, Maximilian, Bamberg, Fabian, Wiestler, Bene, Paetzold, Johannes C., Rueckert, Daniel, Kirschke, Jan Stefan
Objectives: To present a publicly available torso segmentation network for large epidemiology datasets on volumetric interpolated breath-hold examination (VIBE) images. Materials & Methods: We extracted preliminary segmentations from TotalSegmentator
Externí odkaz:
http://arxiv.org/abs/2406.00125
Autor:
Li, Hongwei Bran, Navarro, Fernando, Ezhov, Ivan, Bayat, Amirhossein, Das, Dhritiman, Kofler, Florian, Shit, Suprosanna, Waldmannstetter, Diana, Paetzold, Johannes C., Hu, Xiaobin, Wiestler, Benedikt, Zimmer, Lucas, Amiranashvili, Tamaz, Prabhakar, Chinmay, Berger, Christoph, Weidner, Jonas, Alonso-Basant, Michelle, Rashid, Arif, Baid, Ujjwal, Adel, Wesam, Ali, Deniz, Baheti, Bhakti, Bai, Yingbin, Bhatt, Ishaan, Cetindag, Sabri Can, Chen, Wenting, Cheng, Li, Dutand, Prasad, Dular, Lara, Elattar, Mustafa A., Feng, Ming, Gao, Shengbo, Huisman, Henkjan, Hu, Weifeng, Innani, Shubham, Jiat, Wei, Karimi, Davood, Kuijf, Hugo J., Kwak, Jin Tae, Le, Hoang Long, Lia, Xiang, Lin, Huiyan, Liu, Tongliang, Ma, Jun, Ma, Kai, Ma, Ting, Oksuz, Ilkay, Holland, Robbie, Oliveira, Arlindo L., Pal, Jimut Bahan, Pei, Xuan, Qiao, Maoying, Saha, Anindo, Selvan, Raghavendra, Shen, Linlin, Silva, Joao Lourenco, Spiclin, Ziga, Talbar, Sanjay, Wang, Dadong, Wang, Wei, Wang, Xiong, Wang, Yin, Xia, Ruiling, Xu, Kele, Yan, Yanwu, Yergin, Mert, Yu, Shuang, Zeng, Lingxi, Zhang, YingLin, Zhao, Jiachen, Zheng, Yefeng, Zukovec, Martin, Do, Richard, Becker, Anton, Simpson, Amber, Konukoglu, Ender, Jakab, Andras, Bakas, Spyridon, Joskowicz, Leo, Menze, Bjoern
Uncertainty in medical image segmentation tasks, especially inter-rater variability, arising from differences in interpretations and annotations by various experts, presents a significant challenge in achieving consistent and reliable image segmentat
Externí odkaz:
http://arxiv.org/abs/2405.18435
Autor:
Berger, Alexander H., Stucki, Nico, Lux, Laurin, Buergin, Vincent, Shit, Suprosanna, Banaszak, Anna, Rueckert, Daniel, Bauer, Ulrich, Paetzold, Johannes C.
Publikováno v:
MICCAI 2024, Lecture Notes in Computer Science, vol. 15008, pp. 721-731, 2024
Topological accuracy in medical image segmentation is a highly important property for downstream applications such as network analysis and flow modeling in vessels or cell counting. Recently, significant methodological advancements have brought well-
Externí odkaz:
http://arxiv.org/abs/2403.11001
Autor:
Wittmann, Bastian, Glandorf, Lukas, Paetzold, Johannes C., Amiranashvili, Tamaz, Wälchli, Thomas, Razansky, Daniel, Menze, Bjoern
Segmentation of blood vessels in murine cerebral 3D OCTA images is foundational for in vivo quantitative analysis of the effects of neurovascular disorders, such as stroke or Alzheimer's, on the vascular network. However, to accurately segment blood
Externí odkaz:
http://arxiv.org/abs/2403.07116