Rebsamen, Michael Andreas

Up a level
Export as [feed] RSS
Group by: Date | Item Type | Refereed | No Grouping
Jump to: 2023 | 2022 | 2021 | 2020 | 2019


Schwarzwald, A; Salmen, A; Léon Betancourt, A; Diem, L; Hammer, H; Radojewski, P; Rebsamen, M; Kamber, N; Chan, A; Hoepner, R; Friedli, C (2023). Anti-neurochondrin antibody as a biomarker in primary autoimmune cerebellar ataxia - a case report and review of the literature. European journal of neurology, 30(4), pp. 1135-1147. Wiley 10.1111/ene.15648

Rebsamen, Michael; McKinley, Richard; Radojewski, Piotr; Pistor, Maximilian; Friedli, Christoph; Hoepner, Robert; Salmen, Anke; Chan, Andrew; Reyes, Mauricio; Wagner, Franca; Wiest, Roland; Rummel, Christian (2023). Reliable brain morphometry from contrast-enhanced T1w-MRI in patients with multiple sclerosis. Human brain mapping, 44(3), pp. 970-979. Wiley-Blackwell 10.1002/hbm.26117

Rebsamen, Michael; Friedli, Christoph; Radojewski, Piotr; Diem, Lara; Chan, Andrew; Wiest, Roland; Salmen, Anke; Rummel, Christian; Hoepner, Robert (2023). Multiple sclerosis as a model to investigate SARS‐CoV ‐2 effect on brain atrophy. CNS neuroscience & therapeutics, 29(2), pp. 538-543. Wiley 10.1111/cns.14050

Köstner, Manuel; Rebsamen, Michael; Radojewski, Piotr; Rummel, Christian; Jin, Baudouin; Meier, Raphael; Ahmadli, Uzeyir; Schindler, Kaspar; Wiest, Roland (2023). Large-scale transient peri-ictal perfusion magnetic resonance imaging abnormalities detected by quantitative image analysis. Brain Communications, 5(2), fcad047. Oxford University Press 10.1093/braincomms/fcad047


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. Journal of inherited metabolic disease, 45(6), pp. 1082-1093. Wiley 10.1002/jimd.12561

Schöne, Corina G; Rebsamen, Michael; Wyssen, Gerda; Rummel, Christian; Wagner, Franca; Vibert, Dominique; Mast, Fred W (2022). Hippocampal volume in patients with bilateral and unilateral peripheral vestibular dysfunction. NeuroImage: Clinical, 36, p. 103212. Elsevier 10.1016/j.nicl.2022.103212

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


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


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, 43(1), pp. 113-128. 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


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

Provide Feedback