Zobrazeno 1 - 10
of 67
pro vyhledávání: '"Matthias W. Wagner"'
Autor:
Matthias W. Wagner, Delvin So, Ting Guo, Lauren Erdman, Min Sheng, S. Ufkes, Ruth E. Grunau, Anne Synnes, Helen M. Branson, Vann Chau, Manohar M. Shroff, Birgit B. Ertl-Wagner, Steven P. Miller
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-7 (2022)
Abstract To predict adverse neurodevelopmental outcome of very preterm neonates. A total of 166 preterm neonates born between 24–32 weeks’ gestation underwent brain MRI early in life. Radiomics features were extracted from T1- and T2- weighted im
Externí odkaz:
https://doaj.org/article/09b13d4988a4402c9acc1be6431d0654
Publikováno v:
Frontiers in Artificial Intelligence, Vol 5 (2022)
Fetal MR imaging is subject to artifacts including motion, chemical shift, and radiofrequency artifacts. Currently, such artifacts are detected by the MRI operator, a process which is subjective, time consuming, and prone to errors. We propose a nove
Externí odkaz:
https://doaj.org/article/612dbada873842e0bd4c65995737b8b8
Publikováno v:
Frontiers in Artificial Intelligence, Vol 5 (2022)
Rapid development in Magnetic Resonance Imaging (MRI) has played a key role in prenatal diagnosis over the last few years. Deep learning (DL) architectures can facilitate the process of anomaly detection and affected-organ classification, making diag
Externí odkaz:
https://doaj.org/article/7a66df209f414861973c66091bcba169
Publikováno v:
Children, Vol 10, Iss 3, p 477 (2023)
Purpose: Literature is scarce regarding volumetric measures of limbic system components across the pediatric age range. The purpose of this study is to remedy this scarcity by reporting continuous volumetric measurements of limbic system components,
Externí odkaz:
https://doaj.org/article/b10e680b85024ff6ab11cbecbbe5bdc0
Publikováno v:
Bioengineering, Vol 10, Iss 2, p 140 (2023)
Identifying fetal orientation is essential for determining the mode of delivery and for sequence planning in fetal magnetic resonance imaging (MRI). This manuscript describes a deep learning algorithm named Fet-Net, composed of convolutional neural n
Externí odkaz:
https://doaj.org/article/4aa871935c034ffb84445a08bf868baa
Autor:
Magda Marcon, Alexander Ciritsis, Cristina Rossi, Anton S. Becker, Nicole Berger, Moritz C. Wurnig, Matthias W. Wagner, Thomas Frauenfelder, Andreas Boss
Publikováno v:
European Radiology Experimental, Vol 3, Iss 1, Pp 1-11 (2019)
Abstract Background Our aims were to determine if features derived from texture analysis (TA) can distinguish normal, benign, and malignant tissue on automated breast ultrasound (ABUS); to evaluate whether machine learning (ML) applied to TA can cate
Externí odkaz:
https://doaj.org/article/c61dd500d13945dfbb03ca7d63eae73b
Autor:
Matthias W. Wagner, Sharon L. Cushing, Makabongwe Tshuma, Karen A. Gordon, Birgit B. Ertl-Wagner, Logi Vidarsson
Publikováno v:
Children, Vol 9, Iss 6, p 800 (2022)
Background: Children with non-syndromic hereditary sensorineural hearing loss (SNHL) provide an opportunity to explore the impact of hearing on brain development. Objective: This study investigates volumetric differences of key hearing-related struct
Externí odkaz:
https://doaj.org/article/875ea414c9ef4f5586865af6023d6a95
Autor:
Justin Lo, Saiee Nithiyanantham, Jillian Cardinell, Dylan Young, Sherwin Cho, Abirami Kirubarajan, Matthias W. Wagner, Roxana Azma, Steven Miller, Mike Seed, Birgit Ertl-Wagner, Dafna Sussman
Publikováno v:
Sensors, Vol 21, Iss 13, p 4490 (2021)
Segmentation of the fetus from 2-dimensional (2D) magnetic resonance imaging (MRI) can aid radiologists with clinical decision making for disease diagnosis. Machine learning can facilitate this process of automatic segmentation, making diagnosis more
Externí odkaz:
https://doaj.org/article/d0a759ded55d47feadaf005f3910da80
Autor:
Daniel Stocker, Herman P. Marquez, Matthias W. Wagner, Dimitri A. Raptis, Pierre-Alain Clavien, Andreas Boss, Michael A. Fischer, Moritz C. Wurnig
Publikováno v:
Heliyon, Vol 4, Iss 11, Pp e00987- (2018)
Purpose: To find potentially diagnostic texture analysis (TA) features and to evaluate the diagnostic accuracy of two-dimensional (2D) magnetic resonance (MR) TA for differentiation between hepatocellular carcinoma (HCC) and benign hepatocellular tum
Externí odkaz:
https://doaj.org/article/83cdc7589b534ba4979d2be529172d88
Autor:
Michael Zhang, Samuel W. Wong, Jason N. Wright, Matthias W. Wagner, Sebastian Toescu, Michelle Han, Lydia T. Tam, Quan Zhou, Saman S. Ahmadian, Katie Shpanskaya, Seth Lummus, Hollie Lai, Azam Eghbal, Alireza Radmanesh, Jordan Nemelka, Stephen Harward, Michael Malinzak, Suzanne Laughlin, Sébastien Perreault, Kristina R. M. Braun, Robert M. Lober, Yoon Jae Cho, Birgit Ertl-Wagner, Chang Y. Ho, Kshitij Mankad, Hannes Vogel, Samuel H. Cheshier, Thomas S. Jacques, Kristian Aquilina, Paul G. Fisher, Michael Taylor, Tina Poussaint, Nicholas A. Vitanza, Gerald A. Grant, Stefan Pfister, Eric Thompson, Alok Jaju, Vijay Ramaswamy, Kristen W. Yeom
Publikováno v:
Radiology. 304:406-416
Background Radiogenomics of pediatric medulloblastoma (MB) offers an opportunity for MB risk stratification, which may aid therapeutic decision making, family counseling, and selection of patient groups suitable for targeted genetic analysis. Purpose