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Characterizing Anatomical Variability and Alzheimer's Disease Related Cortical Thinning in the Medial Temporal Lobe Using Graph-Based Groupwise Registration and Point Set Geodesic Shooting.

L. Xie, L. Wisse, S. Das, R. Ittyerah, J. Wang, D. Wolk, and P. Yushkevich. ShapeMI@@MICCAI, volume 11167 of Lecture Notes in Computer Science, page 28-37. Springer, (2018)

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lin A David

David A Traill

David A Wuttke

David A Lazica

Steffen Wolk

Other publications of authors with the same name

A comparison of Freesurfer and multi-atlas MUSE for brain anatomy segmentation: Findings about size and age bias, and inter-scanner stability in multi-site aging studies.D. Srinivasan, G. Erus, J. Doshi, D. Wolk, H. Shou, M. Habes, and C. Davatzikos. NeuroImage, (2020)Gene-SGAN: a method for discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering.Z. Yang, J. Wen, A. Abdulkadir, Y. Cui, G. Erus, E. Mamourian, R. Melhem, D. Srinivasan, S. Govindarajan, J. Chen and 18 other author(s). CoRR, (2023)Deep Label Fusion: A 3D End-To-End Hybrid Multi-atlas Segmentation and Deep Learning Pipeline.L. Xie, L. Wisse, J. Wang, S. Ravikumar, T. Glenn, A. Luther, S. Lim, D. Wolk, and P. Yushkevich. IPMI, volume 12729 of Lecture Notes in Computer Science, page 428-439. Springer, (2021)Predicting Cognitive Data from Medical Images Using Sparse Linear Regression.B. Kandel, D. Wolk, J. Gee, and B. Avants. IPMI, volume 7917 of Lecture Notes in Computer Science, page 86-97. Springer, (2013)Deep label fusion: A generalizable hybrid multi-atlas and deep convolutional neural network for medical image segmentation.L. Xie, L. Wisse, J. Wang, S. Ravikumar, P. Khandelwal, T. Glenn, A. Luther, S. Lim, D. Wolk, and P. Yushkevich. Medical Image Anal., (2023)Characterizing Anatomical Variability and Alzheimer's Disease Related Cortical Thinning in the Medial Temporal Lobe Using Graph-Based Groupwise Registration and Point Set Geodesic Shooting.L. Xie, L. Wisse, S. Das, R. Ittyerah, J. Wang, D. Wolk, and P. Yushkevich. ShapeMI@@MICCAI, volume 11167 of Lecture Notes in Computer Science, page 28-37. Springer, (2018)Disentangling Alzheimer's disease neurodegeneration from typical brain aging using machine learning.G. Hwang, A. Abdulkadir, G. Erus, M. Habes, R. Pomponio, H. Shou, J. Doshi, E. Mamourian, T. Rashid, M. Bilgel and 11 other author(s). CoRR, (2021)A framework for informing segmentation of in vivo MRI with information derived from ex vivo imaging: Application in the medial temporal lobe.P. Yushkevich, L. Wisse, D. Adler, R. Ittyerah, J. Pluta, J. Robinson, T. Schuck, J. Trojanowski, M. Grossman, J. Detre and 6 other author(s). EMBC, page 6014-6017. IEEE, (2016)Unfolding the Medial Temporal Lobe Cortex to Characterize Neurodegeneration Due to Alzheimer's Disease Pathology Using Ex vivo Imaging.S. Ravikumar, L. Wisse, S. Lim, D. Irwin, R. Ittyerah, L. Xie, S. Das, E. Lee, M. Tisdall, K. Prabhakaran and 19 other author(s). MLCN@MICCAI, volume 13001 of Lecture Notes in Computer Science, page 3-12. Springer, (2021)Image-to-Image Translation Between Tau Pathology and Neuronal Metabolism PET in Alzheimer Disease with Multi-domain Contrastive Learning.M. Duong, S. Das, P. Khandelwal, X. Lyu, L. Xie, P. Yushkevich, D. Wolk, and I. Nasrallah. MLCN@MICCAI, volume 14312 of Lecture Notes in Computer Science, page 3-13. Springer, (2023)

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Disambiguation of "Wolk, David A."