MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision

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

Clinical trainee performance on task‐based AR/VR‐guided surgical simulation is correlated with their 3D image spatial reasoning scores

Autor: Roy Eagleson, Denis Kikinov, Liam Bilbie, Sandrine de Ribaupierre

Publikováno v: Healthcare Technology Letters, Vol 11, Iss 2-3, Pp 117-125 (2024)

Abstract This paper describes a methodology for the assessment of training simulator‐based computer‐assisted intervention skills on an AR/VR‐guided procedure making use of CT axial slice views for a neurosurgical procedure: external ventricular

Externí odkaz: https://doaj.org/article/4bcb2e5206214c4aa71bbdd6f72c9558

MICAL: Mutual Information-Based CNN-Aided Learned Factor Graphs for Seizure Detection from EEG Signals

Autor: Salafian, Bahareh, Ben-Knaan, Eyal Fishel, Shlezinger, Nir, de Ribaupierre, Sandrine, Farsad, Nariman

We develop a hybrid model-based data-driven seizure detection algorithm called Mutual Information-based CNNAided Learned factor graphs (MICAL) for detection of eclectic seizures from EEG signals. Our proposed method contains three main components: a

Externí odkaz: http://arxiv.org/abs/2206.02298

Altered resting-state functional connectivity in newborns with hypoxic ischemic encephalopathy assessed using high-density functional near-infrared spectroscopy

Autor: Lingkai Tang, Lilian M. N. Kebaya, Talal Altamimi, Alexandra Kowalczyk, Melab Musabi, Sriya Roychaudhuri, Homa Vahidi, Paige Meyerink, Sandrine de Ribaupierre, Soume Bhattacharya, Leandro Tristao Abi Ramia de Moraes, Keith St. Lawrence, Emma G. Duerden

Publikováno v: Scientific Reports, Vol 14, Iss 1, Pp 1-13 (2024)

Abstract Hypoxic-ischemic encephalopathy (HIE) results from a lack of oxygen to the brain during the perinatal period. HIE can lead to mortality and various acute and long-term morbidities. Improved bedside monitoring methods are needed to identify b

Externí odkaz: https://doaj.org/article/662ad034733e4e16a2c8cd80adec65bd

Automatic extraction of requirements expressed in industrial standards : a way towards machine readable standards ?

Autor: de Ribaupierre, Helene, Cutting-Decelle, Anne-Francoise, Baumier, Nathalie, Blumental, Serge

The project, under industrial funding, presented in this publication aims at the semantic analysis of a normative document describing requirements applicable to electrical appliances. The objective of the project is to build a semantic approach to ex

Externí odkaz: http://arxiv.org/abs/2112.13091