Rebsamen, Michael Andreas

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2022

Muri, Raphaela; Maissen-Abgottsponn, Stephanie; Rummel, Christian; Rebsamen, Michael; Wiest, Roland; Hochuli, Michel; Jansma, Bernadette M; Trepp, Roman; Everts, Regula (2022). Cortical thickness and its relationship to cognitive performance and metabolic control in adults with phenylketonuria. (In Press). Journal of inherited metabolic disease Wiley 10.1002/jimd.12561

Park, Bo-Yong; Larivière, Sara; Rodríguez-Cruces, Raul; Royer, Jessica; Tavakol, Shahin; Wang, Yezhou; Caciagli, Lorenzo; Caligiuri, Maria Eugenia; Gambardella, Antonio; Concha, Luis; Keller, Simon S; Cendes, Fernando; Alvim, Marina K M; Yasuda, Clarissa; Bonilha, Leonardo; Gleichgerrcht, Ezequiel; Focke, Niels K; Kreilkamp, Barbara A K; Domin, Martin; von Podewils, Felix; ... (2022). Topographic divergence of atypical cortical asymmetry and atrophy patterns in temporal lobe epilepsy. Brain : a journal of neurology, 145(4), pp. 1285-1298. Oxford University Press 10.1093/brain/awab417

Rebsamen, Michael; Radojewski, Piotr; McKinley, Richard; Reyes, Mauricio; Wiest, Roland; Rummel, Christian (2022). A Quantitative Imaging Biomarker Supporting Radiological Assessment of Hippocampal Sclerosis Derived From Deep Learning-Based Segmentation of T1w-MRI. Frontiers in neurology, 13, p. 812432. Frontiers Media S.A. 10.3389/fneur.2022.812432

Zito, Giuseppe A; Tarrano, Clément; Jegatheesan, Prasanthi; Ekmen, Asya; Béranger, Benoît; Rebsamen, Michael; Hubsch, Cécile; Sangla, Sophie; Bonnet, Cécilia; Delorme, Cécile; Méneret, Aurélie; Degos, Bertrand; Bouquet, Floriane; Brissard, Marion Apoil; Vidailhet, Marie; Gallea, Cécile; Roze, Emmanuel; Worbe, Yulia (2022). Somatotopy of cervical dystonia in motor-cerebellar networks: Evidence from resting state fMRI. Parkinsonism & related disorders, 94, pp. 30-36. Elsevier 10.1016/j.parkreldis.2021.11.034

2021

McKinley, Richard I; Rebsamen, Michael; Dätwyler, Katrin; Meier, Raphael; Radojewski, Piotr; Wiest, Roland (2021). Uncertainty-Driven Refinement of Tumor-Core Segmentation Using 3D-to-2D Networks with Label Uncertainty. Lecture notes in computer science, 12658, pp. 401-411. Springer 10.1007/978-3-030-72084-1_36

2020

Rebsamen, Michael; Rummel, Christian; Reyes, Mauricio; Wiest, Roland; McKinley, Richard (2020). Direct cortical thickness estimation using deep learning-based anatomy segmentation and cortex parcellation. Human brain mapping, 41(17), pp. 4804-4814. Wiley-Blackwell 10.1002/hbm.25159

Dobrocky, T.; Rebsamen, M.; Rummel, C.; Häni, L.; Mordasini, P.; Raabe, A.; Ulrich, C. T.; Gralla, J.; Piechowiak, E. I.; Beck, J. (2020). Monro-Kellie Hypothesis: Increase of Ventricular CSF Volume after Surgical Closure of a Spinal Dural Leak in Patients with Spontaneous Intracranial Hypotension. AJNR. American journal of neuroradiology, 41(11), pp. 2055-2061. American Society of Neuroradiology 10.3174/ajnr.A6782

Sisodiya, Sanjay M; Whelan, Christopher D; Hatton, Sean N; Huynh, Khoa; Altmann, Andre; Ryten, Mina; Vezzani, Annamaria; Caligiuri, Maria Eugenia; Labate, Angelo; Gambardella, Antonio; Ives-Deliperi, Victoria; Meletti, Stefano; Munsell, Brent C; Bonilha, Leonardo; Tondelli, Manuela; Rebsamen, Michael; Rummel, Christian; Vaudano, Anna Elisabetta; Wiest, Roland; Balachandra, Akshara R; ... (2020). The ENIGMA-Epilepsy working group: Mapping disease from large data sets. (In Press). Human brain mapping Wiley-Blackwell 10.1002/hbm.25037

Rebsamen, Michael; Suter, Yannick; Wiest, Roland; Reyes, Mauricio; Rummel, Christian (2020). Brain Morphometry Estimation: From Hours to Seconds Using Deep Learning. Frontiers in neurology, 11(244), p. 244. Frontiers Media S.A. 10.3389/fneur.2020.00244

2019

Rebsamen, Michael; Rummel, Christian; Mürner-Lavanchy, Ines; Reyes, M; Wiest, Roland; McKinley, Richard (2019). Surface-Based Brain Morphometry for the Prediction of Fluid Intelligence in the Neurocognitive Prediction Challenge 2019. In: Pohl, Kilian M.; Thompson, Wesley K.; Adeli, Ehsan; Linguraru, Marius George (eds.) Adolescent Brain Cognitive Development Neurocognitive Prediction. ABCD-NP 2019. Lecture notes in computer science: Vol. 11791 (pp. 26-34). Cham, Switzerland: Springer

Rebsamen, Michael; Knecht, Urspeter; Reyes, Mauricio; Wiest, Roland; Meier, Raphael; McKinley, Richard (2019). Divide and Conquer: Stratifying Training Data by Tumor Grade Improves Deep Learning-Based Brain Tumor Segmentation. Frontiers in neuroscience, 13, p. 1182. Frontiers Research Foundation 10.3389/fnins.2019.01182

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