TotalVibeSegmentator: Full Torso Segmentation for the NAKO and UK Biobank in Volumetric Interpolated Breath-hold Examination Body Images

1 code implementation • 31 May 2024 • Robert Graf, Paul-Sören Platzek, Evamaria Olga Riedel, Constanze Ramschütz, Sophie Starck, Hendrik Kristian Möller, Matan Atad, Henry Völzke, Robin Bülow, Carsten Oliver Schmidt, Julia Rüdebusch, Matthias Jung, Marco Reisert, Jakob Weiss, Maximilian Löffler, Fabian Bamberg, Bene Wiestler, Johannes C. Paetzold, Daniel Rueckert, Jan Stefan Kirschke

Objectives: To present a publicly available torso segmentation network for large epidemiology datasets on volumetric interpolated breath-hold examination (VIBE) images.

Epidemiology Holdout Set +3

DenseNet and Support Vector Machine classifications of major depressive disorder using vertex-wise cortical features

no code implementations • 18 Nov 2023 • Vladimir Belov, Tracy Erwin-Grabner, Ling-Li Zeng, Christopher R. K. Ching, Andre Aleman, Alyssa R. Amod, Zeynep Basgoze, Francesco Benedetti, Bianca Besteher, Katharina Brosch, Robin Bülow, Romain Colle, Colm G. Connolly, Emmanuelle Corruble, Baptiste Couvy-Duchesne, Kathryn Cullen, Udo Dannlowski, Christopher G. Davey, Annemiek Dols, Jan Ernsting, Jennifer W. Evans, Lukas Fisch, Paola Fuentes-Claramonte, Ali Saffet Gonul, Ian H. Gotlib, Hans J. Grabe, Nynke A. Groenewold, Dominik Grotegerd, Tim Hahn, J. Paul Hamilton, Laura K. M. Han, Ben J Harrison, Tiffany C. Ho, Neda Jahanshad, Alec J. Jamieson, Andriana Karuk, Tilo Kircher, Bonnie Klimes-Dougan, Sheri-Michelle Koopowitz, Thomas Lancaster, Ramona Leenings, Meng Li, David E. J. Linden, Frank P. MacMaster, David M. A. Mehler, Susanne Meinert, Elisa Melloni, Bryon A. Mueller, Benson Mwangi, Igor Nenadić, Amar Ojha, Yasumasa Okamoto, Mardien L. Oudega, Brenda W. J. H. Penninx, Sara Poletti, Edith Pomarol-Clotet, Maria J. Portella, Elena Pozzi, Joaquim Radua, Elena Rodríguez-Cano, Matthew D. Sacchet, Raymond Salvador, Anouk Schrantee, Kang Sim, Jair C. Soares, Aleix Solanes, Dan J. Stein, Frederike Stein, Aleks Stolicyn, Sophia I. Thomopoulos, Yara J. Toenders, Aslihan Uyar-Demir, Eduard Vieta, Yolanda Vives-Gilabert, Henry Völzke, Martin Walter, Heather C. Whalley, Sarah Whittle, Nils Winter, Katharina Wittfeld, Margaret J. Wright, Mon-Ju Wu, Tony T. Yang, Carlos Zarate, Dick J. Veltman, Lianne Schmaal, Paul M. Thompson, Roberto Goya-Maldonado

Based on the hypothesis that integration of vertex-wise cortical features can improve classification performance, we evaluated the classification of a DenseNet and a Support Vector Machine (SVM), with the expectation that the former would outperform the latter.

Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures

no code implementations • 16 Jun 2022 • Vladimir Belov, Tracy Erwin-Grabner, Ali Saffet Gonul, Alyssa R. Amod, Amar Ojha, Andre Aleman, Annemiek Dols, Anouk Scharntee, Aslihan Uyar-Demir, Ben J Harrison, Benson M. Irungu, Bianca Besteher, Bonnie Klimes-Dougan, Brenda W. J. H. Penninx, Bryon A. Mueller, Carlos Zarate, Christopher G. Davey, Christopher R. K. Ching, Colm G. Connolly, Cynthia H. Y. Fu, Dan J. Stein, Danai Dima, David E. J. Linden, David M. A. Mehler, Edith Pomarol-Clotet, Elena Pozzi, Elisa Melloni, Francesco Benedetti, Frank P. MacMaster, Hans J. Grabe, Henry Völzke, Ian H. Gotlib, Jair C. Soares, Jennifer W. Evans, Kang Sim, Katharina Wittfeld, Kathryn Cullen, Liesbeth Reneman, Mardien L. Oudega, Margaret J. Wright, Maria J. Portella, Matthew D. Sacchet, Meng Li, Moji Aghajani, Mon-Ju Wu, Natalia Jaworska, Neda Jahanshad, Nic J. A. van der Wee, Nynke Groenewold, Paul J. Hamilton, Philipp Saemann, Robin Bülow, Sara Poletti, Sarah Whittle, Sophia I. Thomopoulos, Steven J. A. van, der Werff, Sheri-Michelle Koopowitz, Thomas Lancaster, Tiffany C. Ho, Tony T. Yang, Zeynep Basgoze, Dick J. Veltman, Lianne Schmaal, Paul M. Thompson, Roberto Goya-Maldonado

Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders.

Classification

An Uncertainty-Aware, Shareable and Transparent Neural Network Architecture for Brain-Age Modeling

no code implementations • 16 Jul 2021 • Tim Hahn, Jan Ernsting, Nils R. Winter, Vincent Holstein, Ramona Leenings, Marie Beisemann, Lukas Fisch, Kelvin Sarink, Daniel Emden, Nils Opel, Ronny Redlich, Jonathan Repple, Dominik Grotegerd, Susanne Meinert, Jochen G. Hirsch, Thoralf Niendorf, Beate Endemann, Fabian Bamberg, Thomas Kröncke, Robin Bülow, Henry Völzke, Oyunbileg von Stackelberg, Ramona Felizitas Sowade, Lale Umutlu, Börge Schmidt, Svenja Caspers, German National Cohort Study Center Consortium, Harald Kugel, Tilo Kircher, Benjamin Risse, Christian Gaser, James H. Cole, Udo Dannlowski, Klaus Berger

The deviation between chronological age and age predicted from neuroimaging data has been identified as a sensitive risk-marker of cross-disorder brain changes, growing into a cornerstone of biological age-research.

quantile regression Uncertainty Quantification

Predicting brain-age from raw T 1 -weighted Magnetic Resonance Imaging data using 3D Convolutional Neural Networks

1 code implementation • 22 Mar 2021 • Lukas Fisch, Jan Ernsting, Nils R. Winter, Vincent Holstein, Ramona Leenings, Marie Beisemann, Kelvin Sarink, Daniel Emden, Nils Opel, Ronny Redlich, Jonathan Repple, Dominik Grotegerd, Susanne Meinert, Niklas Wulms, Heike Minnerup, Jochen G. Hirsch, Thoralf Niendorf, Beate Endemann, Fabian Bamberg, Thomas Kröncke, Annette Peters, Robin Bülow, Henry Völzke, Oyunbileg von Stackelberg, Ramona Felizitas Sowade, Lale Umutlu, Börge Schmidt, Svenja Caspers, German National Cohort Study Center Consortium, Harald Kugel, Bernhard T. Baune, Tilo Kircher, Benjamin Risse, Udo Dannlowski, Klaus Berger, Tim Hahn

For comparison, state-of-the-art models using preprocessed neuroimaging data are trained and validated on the same samples.

Transfer Learning