Zobrazeno 1 - 10
of 92
pro vyhledávání: '"Dorent, Reuben"'
Autor:
Dorent, Reuben, Haouchine, Nazim, Golby, Alexandra, Frisken, Sarah, Kapur, Tina, Wells, William
We propose a deep mixture of multimodal hierarchical variational auto-encoders called MMHVAE that synthesizes missing images from observed images in different modalities. MMHVAE's design focuses on tackling four challenges: (i) creating a complex lat
Externí odkaz:
http://arxiv.org/abs/2410.19378
Autor:
Fehrentz, Maximilian, Azampour, Mohammad Farid, Dorent, Reuben, Rasheed, Hassan, Galvin, Colin, Golby, Alexandra, Wells, William M., Frisken, Sarah, Navab, Nassir, Haouchine, Nazim
We present in this paper a novel approach for 3D/2D intraoperative registration during neurosurgery via cross-modal inverse neural rendering. Our approach separates implicit neural representation into two components, handling anatomical structure pre
Externí odkaz:
http://arxiv.org/abs/2409.11983
Autor:
Rasheed, Hassan, Dorent, Reuben, Fehrentz, Maximilian, Kapur, Tina, Wells III, William M., Golby, Alexandra, Frisken, Sarah, Schnabel, Julia A., Haouchine, Nazim
We propose in this paper a texture-invariant 2D keypoints descriptor specifically designed for matching preoperative Magnetic Resonance (MR) images with intraoperative Ultrasound (US) images. We introduce a matching-by-synthesis strategy, where intra
Externí odkaz:
http://arxiv.org/abs/2409.08169
Autor:
Dorent, Reuben, Khajavi, Roya, Idris, Tagwa, Ziegler, Erik, Somarouthu, Bhanusupriya, Jacene, Heather, LaCasce, Ann, Deissler, Jonathan, Ehrhardt, Jan, Engelson, Sofija, Fischer, Stefan M., Gu, Yun, Handels, Heinz, Kasai, Satoshi, Kondo, Satoshi, Maier-Hein, Klaus, Schnabel, Julia A., Wang, Guotai, Wang, Litingyu, Wald, Tassilo, Yang, Guang-Zhong, Zhang, Hanxiao, Zhang, Minghui, Pieper, Steve, Harris, Gordon, Kikinis, Ron, Kapur, Tina
Accurate assessment of lymph node size in 3D CT scans is crucial for cancer staging, therapeutic management, and monitoring treatment response. Existing state-of-the-art segmentation frameworks in medical imaging often rely on fully annotated dataset
Externí odkaz:
http://arxiv.org/abs/2408.10069
Autor:
Dorent, Reuben, Torio, Erickson, Haouchine, Nazim, Galvin, Colin, Frisken, Sarah, Golby, Alexandra, Kapur, Tina, Wells, William
Intraoperative ultrasound (iUS) imaging has the potential to improve surgical outcomes in brain surgery. However, its interpretation is challenging, even for expert neurosurgeons. In this work, we designed the first patient-specific framework that pe
Externí odkaz:
http://arxiv.org/abs/2405.09959
Whole brain parcellation requires inferring hundreds of segmentation labels in large image volumes and thus presents significant practical challenges for deep learning approaches. We introduce label merge-and-split, a method that first greatly reduce
Externí odkaz:
http://arxiv.org/abs/2404.10572
Autor:
Baur, Kathleen, Xiong, Xin, Torio, Erickson, Du, Rose, Juvekar, Parikshit, Dorent, Reuben, Golby, Alexandra, Frisken, Sarah, Haouchine, Nazim
Although Digital Subtraction Angiography (DSA) is the most important imaging for visualizing cerebrovascular anatomy, its interpretation by clinicians remains difficult. This is particularly true when treating arteriovenous malformations (AVMs), wher
Externí odkaz:
http://arxiv.org/abs/2402.09636
Autor:
Haouchine, Nazim, Dorent, Reuben, Juvekar, Parikshit, Torio, Erickson, Wells III, William M., Kapur, Tina, Golby, Alexandra J., Frisken, Sarah
We present a novel method for intraoperative patient-to-image registration by learning Expected Appearances. Our method uses preoperative imaging to synthesize patient-specific expected views through a surgical microscope for a predicted range of tra
Externí odkaz:
http://arxiv.org/abs/2310.01735
Autor:
Dorent, Reuben, Haouchine, Nazim, Kögl, Fryderyk, Joutard, Samuel, Juvekar, Parikshit, Torio, Erickson, Golby, Alexandra, Ourselin, Sebastien, Frisken, Sarah, Vercauteren, Tom, Kapur, Tina, Wells, William M.
We introduce MHVAE, a deep hierarchical variational auto-encoder (VAE) that synthesizes missing images from various modalities. Extending multi-modal VAEs with a hierarchical latent structure, we introduce a probabilistic formulation for fusing multi
Externí odkaz:
http://arxiv.org/abs/2309.08747
Autor:
Li, Jianning, Zhou, Zongwei, Yang, Jiancheng, Pepe, Antonio, Gsaxner, Christina, Luijten, Gijs, Qu, Chongyu, Zhang, Tiezheng, Chen, Xiaoxi, Li, Wenxuan, Wodzinski, Marek, Friedrich, Paul, Xie, Kangxian, Jin, Yuan, Ambigapathy, Narmada, Nasca, Enrico, Solak, Naida, Melito, Gian Marco, Vu, Viet Duc, Memon, Afaque R., Schlachta, Christopher, De Ribaupierre, Sandrine, Patel, Rajnikant, Eagleson, Roy, Chen, Xiaojun, Mächler, Heinrich, Kirschke, Jan Stefan, de la Rosa, Ezequiel, Christ, Patrick Ferdinand, Li, Hongwei Bran, Ellis, David G., Aizenberg, Michele R., Gatidis, Sergios, Küstner, Thomas, Shusharina, Nadya, Heller, Nicholas, Andrearczyk, Vincent, Depeursinge, Adrien, Hatt, Mathieu, Sekuboyina, Anjany, Löffler, Maximilian, Liebl, Hans, Dorent, Reuben, Vercauteren, Tom, Shapey, Jonathan, Kujawa, Aaron, Cornelissen, Stefan, Langenhuizen, Patrick, Ben-Hamadou, Achraf, Rekik, Ahmed, Pujades, Sergi, Boyer, Edmond, Bolelli, Federico, Grana, Costantino, Lumetti, Luca, Salehi, Hamidreza, Ma, Jun, Zhang, Yao, Gharleghi, Ramtin, Beier, Susann, Sowmya, Arcot, Garza-Villarreal, Eduardo A., Balducci, Thania, Angeles-Valdez, Diego, Souza, Roberto, Rittner, Leticia, Frayne, Richard, Ji, Yuanfeng, Ferrari, Vincenzo, Chatterjee, Soumick, Dubost, Florian, Schreiber, Stefanie, Mattern, Hendrik, Speck, Oliver, Haehn, Daniel, John, Christoph, Nürnberger, Andreas, Pedrosa, João, Ferreira, Carlos, Aresta, Guilherme, Cunha, António, Campilho, Aurélio, Suter, Yannick, Garcia, Jose, Lalande, Alain, Vandenbossche, Vicky, Van Oevelen, Aline, Duquesne, Kate, Mekhzoum, Hamza, Vandemeulebroucke, Jef, Audenaert, Emmanuel, Krebs, Claudia, van Leeuwen, Timo, Vereecke, Evie, Heidemeyer, Hauke, Röhrig, Rainer, Hölzle, Frank, Badeli, Vahid, Krieger, Kathrin, Gunzer, Matthias, Chen, Jianxu, van Meegdenburg, Timo, Dada, Amin, Balzer, Miriam, Fragemann, Jana, Jonske, Frederic, Rempe, Moritz, Malorodov, Stanislav, Bahnsen, Fin H., Seibold, Constantin, Jaus, Alexander, Marinov, Zdravko, Jaeger, Paul F., Stiefelhagen, Rainer, Santos, Ana Sofia, Lindo, Mariana, Ferreira, André, Alves, Victor, Kamp, Michael, Abourayya, Amr, Nensa, Felix, Hörst, Fabian, Brehmer, Alexander, Heine, Lukas, Hanusrichter, Yannik, Weßling, Martin, Dudda, Marcel, Podleska, Lars E., Fink, Matthias A., Keyl, Julius, Tserpes, Konstantinos, Kim, Moon-Sung, Elhabian, Shireen, Lamecker, Hans, Zukić, Dženan, Paniagua, Beatriz, Wachinger, Christian, Urschler, Martin, Duong, Luc, Wasserthal, Jakob, Hoyer, Peter F., Basu, Oliver, Maal, Thomas, Witjes, Max J. H., Schiele, Gregor, Chang, Ti-chiun, Ahmadi, Seyed-Ahmad, Luo, Ping, Menze, Bjoern, Reyes, Mauricio, Deserno, Thomas M., Davatzikos, Christos, Puladi, Behrus, Fua, Pascal, Yuille, Alan L., Kleesiek, Jens, Egger, Jan
Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit s
Externí odkaz:
http://arxiv.org/abs/2308.16139