Search Results - "Munsell, Brent"

Scalable High-Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning by Wu, Guorong, Kim, Minjeong, Wang, Qian, Munsell, Brent C., Shen, Dinggang

“…Feature selection is a critical step in deformable image registration. In particular, selecting the most discriminative features that accurately and concisely…”
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Detecting Anatomical Landmarks for Fast Alzheimer's Disease Diagnosis by Zhang, Jun, Gao, Yue, Gao, Yaozong, Munsell, Brent C., Shen, Dinggang

“…Structural magnetic resonance imaging (MRI) is a very popular and effective technique used to diagnose Alzheimer's disease (AD). The success of computer-aided…”
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Domain Transfer Learning for MCI Conversion Prediction by Cheng, Bo, Liu, Mingxia, Zhang, Daoqiang, Munsell, Brent C., Shen, Dinggang

“…Machine learning methods have successfully been used to predict the conversion of mild cognitive impairment (MCI) to Alzheimer's disease (AD), by classifying…”
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Hierarchical multi-atlas label fusion with multi-scale feature representation and label-specific patch partition by Wu, Guorong, Kim, Minjeong, Sanroma, Gerard, Wang, Qian, Munsell, Brent C., Shen, Dinggang

“…Multi-atlas patch-based label fusion methods have been successfully used to improve segmentation accuracy in many important medical image analysis…”
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Evaluation of machine learning algorithms for treatment outcome prediction in patients with epilepsy based on structural connectome data by Munsell, Brent C., Wee, Chong-Yaw, Keller, Simon S., Weber, Bernd, Elger, Christian, da Silva, Laura Angelica Tomaz, Nesland, Travis, Styner, Martin, Shen, Dinggang, Bonilha, Leonardo

“…The objective of this study is to evaluate machine learning algorithms aimed at predicting surgical treatment outcomes in groups of patients with temporal lobe…”
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Early brain development in infants at high risk for autism spectrum disorder by Hazlett, Heather Cody, Gu, Hongbin, Munsell, Brent C., Kim, Sun Hyung, Styner, Martin, Wolff, Jason J., Elison, Jed T., Swanson, Meghan R., Zhu, Hongtu, Botteron, Kelly N., Collins, D. Louis, Constantino, John N., Dager, Stephen R., Estes, Annette M., Evans, Alan C., Fonov, Vladimir S., Gerig, Guido, Kostopoulos, Penelope, McKinstry, Robert C., Pandey, Juhi, Paterson, Sarah, Pruett, John R., Schultz, Robert T., Shaw, Dennis W., Zwaigenbaum, Lonnie, Piven, Joseph

“…Surface area expansion from 6–12 months precedes brain overgrowth in high risk infants diagnosed with autism at 24 months and cortical features in the first…”
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Temporal Lobe Epilepsy Surgical Outcomes Can Be Inferred Based on Structural Connectome Hubs: A Machine Learning Study by Gleichgerrcht, Ezequiel, Keller, Simon S., Drane, Daniel L., Munsell, Brent C., Davis, Kathryn A., Kaestner, Erik, Weber, Bernd, Krantz, Samantha, Vandergrift, William A., Edwards, Jonathan C., McDonald, Carrie R., Kuzniecky, Ruben, Bonilha, Leonardo

“…Objective Medial temporal lobe epilepsy (TLE) is the most common form of medication‐resistant focal epilepsy in adults. Despite removal of medial temporal…”
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Deep learning applied to whole‐brain connectome to determine seizure control after epilepsy surgery by Gleichgerrcht, Ezequiel, Munsell, Brent, Bhatia, Sonal, Vandergrift, William A., Rorden, Chris, McDonald, Carrie, Edwards, Jonathan, Kuzniecky, Ruben, Bonilha, Leonardo

“…Summary Objective We evaluated whether deep learning applied to whole‐brain presurgical structural connectomes could be used to predict postoperative seizure…”
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White matter connectomes at birth accurately predict cognitive abilities at age 2 by Girault, Jessica B., Munsell, Brent C., Puechmaille, Danaële, Goldman, Barbara D., Prieto, Juan C., Styner, Martin, Gilmore, John H.

“…Cognitive ability is an important predictor of mental health outcomes that is influenced by neurodevelopment. Evidence suggests that the foundational wiring of…”
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Seizure forecasting using machine learning models trained by seizure diaries by Gleichgerrcht, Ezequiel, Dumitru, Mircea, Hartmann, David A, Munsell, Brent C, Kuzniecky, Ruben, Bonilha, Leonardo, Sameni, Reza

“…People with refractory epilepsy are overwhelmed by the uncertainty of their next seizures. Accurate prediction of future seizures could greatly improve the…”
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Correction to “Scalable High-Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning” [Jul 16 1505-1516] by Wu, Guorong, Kim, Minjeong, Wang, Qian, Munsell, Brent C., Shen, Dinggang

“…Presents corrections to the paper, "Scalable high performance image registration framework by unsupervised deep feature representations", (Wu, G. et al.), IEEE…”
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Hierarchical and symmetric infant image registration by robust longitudinal‐example‐guided correspondence detection by Wu, Yao, Wu, Guorong, Wang, Li, Munsell, Brent C., Wang, Qian, Lin, Weili, Feng, Qianjin, Chen, Wufan, Shen, Dinggang

“…Purpose: To investigate anatomical differences across individual subjects, or longitudinal changes in early brain development, it is important to perform…”
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Editorial: Deep Mining Big Social Data by Zhu, Xiaofeng, Sanroma, Gerard, Zhang, Jilian, Munsell, Brent C.

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Pre-organizing Shape Instances for Landmark-Based Shape Correspondence by Munsell, Brent C., Temlyakov, Andrew, Styner, Martin, Wang, Song

“…The major challenge in constructing a statistical shape model for a structure is shape correspondence, which identifies a set of corresponded landmarks across…”
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Evaluating Shape Correspondence for Statistical Shape Analysis: A Benchmark Study by Munsell, B.C., Dalal, P., Song Wang

“…This paper introduces a new benchmark study to evaluate the performance of landmark-based shape correspondence used for statistical shape analysis. Different…”
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Fast multiple shape correspondence by pre-organizing shape instances by Munsell, Brent C, Temlyakov, Andrew, Song Wang

“…Accurately identifying corresponded landmarks from a population of shape instances is the major challenge in constructing statistical shape models. In general,…”
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Multi-modal classification of neurodegenerative disease by progressive graph-based transductive learning by Wang, Zhengxia, Zhu, Xiaofeng, Adeli, Ehsan, Zhu, Yingying, Nie, Feiping, Munsell, Brent, Wu, Guorong

“…•Learn an intrinsic data representation for optimal classification.•Flexible to integrate with multi-model imaging data.•Progressive graph-based transductive…”
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The White Matter Connectome as an Early Imaging Biomarker by Gilmore, John, Bagonis, Maria, Williams, Jared, Stephens, Rebecca, Cornea, Emil, Styner, Martin, Munsell, Brent

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Neural structures supporting spontaneous and assisted (entrained) speech fluency by Bonilha, Leonardo, Hillis, Argye E, Wilmskoetter, Janina, Hickok, Gregory, Basilakos, Alexandra, Munsell, Brent, Rorden, Chris, Fridriksson, Julius

“…Non-fluent speech is one of the most common impairments in post-stroke aphasia. The rehabilitation of non-fluent speech in aphasia is particularly challenging…”
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The ENIGMA‐Epilepsy working group: Mapping disease from large data sets by 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., Bargalló, Núria, Bartolini, Emanuele, Bernasconi, Andrea, Bernasconi, Neda, Bernhardt, Boris, Caldairou, Benoit, Carr, Sarah J.A., Cavalleri, Gianpiero L., Cendes, Fernando, Concha, Luis, Desmond, Patricia M., Domin, Martin, Duncan, John S., Focke, Niels K., Guerrini, Renzo, Hamandi, Khalid, Jackson, Graeme D., Jahanshad, Neda, Kälviäinen, Reetta, Keller, Simon S., Kochunov, Peter, Kowalczyk, Magdalena A., Kreilkamp, Barbara A.K., Kwan, Patrick, Lariviere, Sara, Lenge, Matteo, Lopez, Seymour M., Martin, Pascal, Mascalchi, Mario, Moreira, José C.V., Morita‐Sherman, Marcia E., Pariente, Jose C., Raviteja, Kotikalapudi, Rocha, Cristiane S., Semmelroch, Mira K.H.G., Sinclair, Benjamin, Soltanian‐Zadeh, Hamid, Stein, Dan J., Striano, Pasquale, Taylor, Peter N., Thomopoulos, Sophia I., Velakoulis, Dennis, Vivash, Lucy, Weber, Bernd, Yasuda, Clarissa Lin, Thompson, Paul M., McDonald, Carrie R., Abela, Eugenio, Absil, Julie, Adams, Sophia, Alvim, Marina, Balestrini, Simona, Bender, Benjamin, Bergo, Felipe, Bernardes, Tauana, Calvo, Anna, Carreno, Mar, Cherubini, Andrea, David, Philippe, Davoodi‐Bojd, Esmaeil, Delanty, Norman, Depondt, Chantal, Devinsky, Orrin, Doherty, Colin, França, Wendy Caroline, Franceschet, Leticia, Ishikawa, Akari, Kaestner, Erik, Langner, Soenke, Liu, Min, Mirandola, Laura, Naylor, Jillian, Nazem‐Zadeh, Mohammad‐reza, O'Brien, Terence J., Richardson, Mark, Rosenow, Felix, Severino, Mariasavina, Shuai, Chen, Tortora, Domenico, von Podewils, Felix, Vos, Sjoerd B., Zhang, Guohao

“…Epilepsy is a common and serious neurological disorder, with many different constituent conditions characterized by their electro clinical, imaging, and…”
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