Multi-Modality Conditioned Variational U-Net for Field-of-View Extension in Brain Diffusion MRI

Autor: Li, Zhiyuan, Yao, Tianyuan, Kanakaraj, Praitayini, Gao, Chenyu, Bao, Shunxing, Zuo, Lianrui, Kim, Michael E., Newlin, Nancy R., Rudravaram, Gaurav, Khairi, Nazirah M., Huo, Yuankai, Schilling, Kurt G., Kukull, Walter A., Toga, Arthur W., Archer, Derek B., Hohman, Timothy J., Landman, Bennett A.

An incomplete field-of-view (FOV) in diffusion magnetic resonance imaging (dMRI) can severely hinder the volumetric and bundle analyses of whole-brain white matter connectivity. Although existing works have investigated imputing the missing regions u

Externí odkaz: http://arxiv.org/abs/2409.13846

A Comparison of Neuroelectrophysiology Databases

Autor: Subash, Priyanka, Gray, Alex, Boswell, Misque, Cohen, Samantha L., Garner, Rachael, Salehi, Sana, Fisher, Calvary, Hobel, Samuel, Ghosh, Satrajit, Halchenko, Yaroslav, Dichter, Benjamin, Poldrack, Russell A., Markiewicz, Chris, Hermes, Dora, Delorme, Arnaud, Makeig, Scott, Behan, Brendan, Sparks, Alana, Arnott, Stephen R, Wang, Zhengjia, Magnotti, John, Beauchamp, Michael S., Pouratian, Nader, Toga, Arthur W., Duncan, Dominique

As data sharing has become more prevalent, three pillars - archives, standards, and analysis tools - have emerged as critical components in facilitating effective data sharing and collaboration. This paper compares four freely available intracranial

Externí odkaz: http://arxiv.org/abs/2306.15041

Evaluating U-net Brain Extraction for Multi-site and Longitudinal Preclinical Stroke Imaging

Autor: Tarakci, Erendiz, Mandeville, Joseph, Hyder, Fahmeed, Sanganahalli, Basavaraju G., Thedens, Daniel R., Arbab, Ali, Huang, Shuning, Bibic, Adnan, Mihailovic, Jelena, Morais, Andreia, Lamb, Jessica, Nagarkatti, Karisma, Dinitz, Marcio A., Rogatko, Andre, Toga, Arthur W., Lyden, Patrick, Ayata, Cenk, Cabeen, Ryan P.

Rodent stroke models are important for evaluating treatments and understanding the pathophysiology and behavioral changes of brain ischemia, and magnetic resonance imaging (MRI) is a valuable tool for measuring outcome in preclinical studies. Brain e

Externí odkaz: http://arxiv.org/abs/2203.05716

The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up

Autor: Marinescu, Razvan V., Oxtoby, Neil P., Young, Alexandra L., Bron, Esther E., Toga, Arthur W., Weiner, Michael W., Barkhof, Frederik, Fox, Nick C., Eshaghi, Arman, Toni, Tina, Salaterski, Marcin, Lunina, Veronika, Ansart, Manon, Durrleman, Stanley, Lu, Pascal, Iddi, Samuel, Li, Dan, Thompson, Wesley K., Donohue, Michael C., Nahon, Aviv, Levy, Yarden, Halbersberg, Dan, Cohen, Mariya, Liao, Huiling, Li, Tengfei, Yu, Kaixian, Zhu, Hongtu, Tamez-Pena, Jose G., Ismail, Aya, Wood, Timothy, Bravo, Hector Corrada, Nguyen, Minh, Sun, Nanbo, Feng, Jiashi, Yeo, B. T. Thomas, Chen, Gang, Qi, Ke, Chen, Shiyang, Qiu, Deqiang, Buciuman, Ionut, Kelner, Alex, Pop, Raluca, Rimocea, Denisa, Ghazi, Mostafa M., Nielsen, Mads, Ourselin, Sebastien, Sorensen, Lauge, Venkatraghavan, Vikram, Liu, Keli, Rabe, Christina, Manser, Paul, Hill, Steven M., Howlett, James, Huang, Zhiyue, Kiddle, Steven, Mukherjee, Sach, Rouanet, Anais, Taschler, Bernd, Tom, Brian D. M., White, Simon R., Faux, Noel, Sedai, Suman, Oriol, Javier de Velasco, Clemente, Edgar E. V., Estrada, Karol, Aksman, Leon, Altmann, Andre, Stonnington, Cynthia M., Wang, Yalin, Wu, Jianfeng, Devadas, Vivek, Fourrier, Clementine, Raket, Lars Lau, Sotiras, Aristeidis, Erus, Guray, Doshi, Jimit, Davatzikos, Christos, Vogel, Jacob, Doyle, Andrew, Tam, Angela, Diaz-Papkovich, Alex, Jammeh, Emmanuel, Koval, Igor, Moore, Paul, Lyons, Terry J., Gallacher, John, Tohka, Jussi, Ciszek, Robert, Jedynak, Bruno, Pandya, Kruti, Bilgel, Murat, Engels, William, Cole, Joseph, Golland, Polina, Klein, Stefan, Alexander, Daniel C.

Publikováno v: Machine Learning for Biomedical Imaging (MELBA), Dec 2021

We present the findings of "The Alzheimer's Disease Prediction Of Longitudinal Evolution" (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international teams at predicting the future trajectory of 219 individuals at risk

Externí odkaz: http://arxiv.org/abs/2002.03419

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