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Articles

A deep-learning framework reveals whole-body perturbations at cell level

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By Doris Kaltenecker, Izabela Horvath, Rami Al-Maskari, Ying Chen, Zeynep Ilgin Kolabas, Luciano Hoeher, Mihail Ivilinov Todorov, Saketh Kapoor, Louis B Kuemmerle, Hanno Steinke, Mayar Ali, Florian Kofler, Pauline Morigny, Julia Geppert, Denise Jeridi, Bastian Wittmann, Jie Luo, Suprosanna Shit, Melissa Ertürk, Laurent Simons, Chenchen Pan, Marie Piraud, Daniel Rueckert, Maria Rohm, Farida Hellal, Markus Elsner, Harsharan Singh Bhatia, Ingo Bechmann, Bjoern Menze, Stephan Herzig, Johannes Christian Paetzold, Mauricio Berriel Diaz, Ali Erturk, David-Paul Minde, Sena Gül Turhan, Tim Wohlgemuth, Carolina Cigankova, Victor Miro Kolenic, Nilsu Gür, Eren Aydeniz, Alara Yücecan
| Nature Verified
Abstract Many diseases, including obesity, have systemic effects that perturb multiple organ systems throughout the body1,2. However, tools for comprehensive, high-resolution analysis of disease-associated changes at the whole-body scale have been lacking. Here we developed MouseMapper, a suite of foundation-model-based deep-learning algorithms enabling multi-system analysis of disease across the entire mouse body.
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Generalist foundation models from a multimodal dataset for 3D computed tomography - Nature Biomedical Engineering

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By Sezgin Er, Chenyu Wang, Furkan Almas, Sevval Nil Esirgun, Irem Dogan, Benjamin Hou, Suprosanna Shit, Weicheng Dai, Murong Xu, Muhammed Furkan Dasdelen, Bastian Wittmann, Enis Simsar, Mehmet Simsar, Berkan Lafci, Ahmet Kaplan, Zhiyong Lu, Malgorzata Polacin, Bernhard Kainz, Christian Bluethgen, Kayhan Batmanghelich, Mehmet Özdemir, Bjoern Menze, Ibrahim Ethem Hamamci, Ayse Gulnihan Simsek, Omer Faruk Durugol, Hadrien Reynaud, Tamaz Amiranashvili, Emine Bensu Erdemir, Abdullah Alanbay, Anjany Sekuboyina
| Nature Verified
Abstract Advancements in medical imaging AI, particularly in 3D imaging, have been limited due to the scarcity of comprehensive datasets. We introduce CT-RATE, a public dataset that pairs 3D medical images with corresponding textual reports. CT-RATE comprises 25,692 non-contrast 3D chest CT scans from 21,304 unique patients. Each scan is accompanied by its corresponding radiology report.
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DeepISLES: a clinically validated ischemic stroke segmentation model from the ISLES'22 challenge - Nature Communications

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By Mauricio Reyes, Alexandre Hutton, Roland Wiest, Waldo Valenzuela, David Robben, Vincenzo Anania, Christian Heitkamp, Sabine Van Huffel, Hyunsu Jeong, Chiho Yoon, Chulhong Kim, Jiayu Huo, Sébastien Ourselin, Arnau Oliver, Liam Chalcroft, Jeroen Bertels, Ewout Heylen, Maged Goubran, Susanne Wegener, Florian Kofler, Suprosanna Shit, Bjoern Menze, Jan S. Kirschke
| Nature Verified
Abstract Diffusion-weighted MRI is critical for diagnosing and managing ischemic stroke, but variability in images and disease presentation limits the generalizability of AI algorithms. We present DeepISLES, a robust ensemble algorithm developed from top submissions to the 2022 Ischemic Stroke Lesion Segmentation challenge we organized.
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