Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions von Akkus, Zeynettin, Galimzianova, Alfiia, Hoogi, Assaf, Rubin, Daniel L., Erickson, Bradley J.

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Automated Abdominal Segmentation of CT Scans for Body Composition Analysis Using Deep Learning von Weston, Alexander D, Korfiatis, Panagiotis, Kline, Timothy L, Philbrick, Kenneth A, Kostandy, Petro, Sakinis, Tomas, Sugimoto, Motokazu, Takahashi, Naoki, Erickson, Bradley J

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A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop von Langlotz, Curtis P, Allen, Bibb, Erickson, Bradley J, Kalpathy-Cramer, Jayashree, Bigelow, Keith, Cook, Tessa S, Flanders, Adam E, Lungren, Matthew P, Mendelson, David S, Rudie, Jeffrey D, Wang, Ge, Kandarpa, Krishna

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The RSNA Pediatric Bone Age Machine Learning Challenge von Halabi, Safwan S, Prevedello, Luciano M, Kalpathy-Cramer, Jayashree, Mamonov, Artem B, Bilbily, Alexander, Cicero, Mark, Pan, Ian, Pereira, Lucas Araújo, Sousa, Rafael Teixeira, Abdala, Nitamar, Kitamura, Felipe Campos, Thodberg, Hans H, Chen, Leon, Shih, George, Andriole, Katherine, Kohli, Marc D, Erickson, Bradley J, Flanders, Adam E

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The Effects of Changes in Utilization and Technological Advancements of Cross-Sectional Imaging on Radiologist Workload von McDonald, Robert J., MD, PhD, Schwartz, Kara M., MD, Eckel, Laurence J., MD, Diehn, Felix E., MD, Hunt, Christopher H., MD, Bartholmai, Brian J., MD, Erickson, Bradley J., MD, PhD, Kallmes, David F., MD

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A Survey of Deep-Learning Applications in Ultrasound: Artificial Intelligence–Powered Ultrasound for Improving Clinical Workflow von Akkus, Zeynettin, Cai, Jason, Boonrod, Arunnit, Zeinoddini, Atefeh, Weston, Alexander D., Philbrick, Kenneth A., Erickson, Bradley J.

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The RSNA International COVID-19 Open Radiology Database (RICORD) von Tsai, Emily B, Simpson, Scott, Lungren, Matthew P, Hershman, Michelle, Roshkovan, Leonid, Colak, Errol, Erickson, Bradley J, Shih, George, Stein, Anouk, Kalpathy-Cramer, Jayashree, Shen, Jody, Hafez, Mona, John, Susan, Rajiah, Prabhakar, Pogatchnik, Brian P, Mongan, John, Altinmakas, Emre, Ranschaert, Erik R, Kitamura, Felipe C, Topff, Laurens, Moy, Linda, Kanne, Jeffrey P, Wu, Carol C

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Association between pathological and MRI findings in multiple sclerosis von Filippi, Massimo, Prof, Rocca, Maria A, MD, Barkhof, Frederik, Prof, Brück, Wolfgang, Prof, Chen, Jacqueline T, PhD, Comi, Giancarlo, MD, DeLuca, Gabriele, MD, De Stefano, Nicola, MD, Erickson, Bradley J, PhD, Evangelou, Nikos, Prof, Fazekas, Franz, Prof, Geurts, Jeroen JG, Prof, Lucchinetti, Claudia, Prof, Miller, David H, Prof, Pelletier, Daniel, Prof, Popescu, Bogdan F Gh, PhD, Lassmann, Hans, Prof

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A Comparison of Three Different Deep Learning-Based Models to Predict the MGMT Promoter Methylation Status in Glioblastoma Using Brain MRI von Faghani, Shahriar, Khosravi, Bardia, Moassefi, Mana, Conte, Gian Marco, Erickson, Bradley J.

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Uncover This Tech Term: Uncertainty Quantification for Deep Learning von Faghani, Shahriar, Gamble, Cooper, Erickson, Bradley J

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Predicting Deletion of Chromosomal Arms 1p/19q in Low-Grade Gliomas from MR Images Using Machine Intelligence von Akkus, Zeynettin, Ali, Issa, Sedlář, Jiří, Agrawal, Jay P., Parney, Ian F., Giannini, Caterina, Erickson, Bradley J.

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Consensus recommendations for a dynamic susceptibility contrast MRI protocol for use in high-grade gliomas von Boxerman, Jerrold L, Quarles, Chad C, Hu, Leland S, Erickson, Bradley J, Gerstner, Elizabeth R, Smits, Marion, Kaufmann, Timothy J, Barboriak, Daniel P, Huang, Raymond H, Wick, Wolfgang, Weller, Michael, Galanis, Evanthia, Kalpathy-Cramer, Jayashree, Shankar, Lalitha, Jacobs, Paula, Chung, Caroline, van den Bent, Martin J, Chang, Susan, Al Yung, W K, Cloughesy, Timothy F, Wen, Patrick Y, Gilbert, Mark R, Rosen, Bruce R, Ellingson, Benjamin M, Schmainda, Kathleen M

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MRI texture features as biomarkers to predict MGMT methylation status in glioblastomas von Korfiatis, Panagiotis, Kline, Timothy L., Coufalova, Lucie, Lachance, Daniel H., Parney, Ian F., Carter, Rickey E., Buckner, Jan C., Erickson, Bradley J.

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Redefining the 3D Topography of the Acetabular Safe Zone: A Multivariable Study Evaluating Prosthetic Hip Stability von Hevesi, Mario, Wyles, Cody C., Rouzrokh, Pouria, Erickson, Bradley J., Maradit-Kremers, Hilal, Lewallen, David G., Taunton, Michael J., Trousdale, Robert T., Berry, Daniel J.

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SOUP-GAN: Super-Resolution MRI Using Generative Adversarial Networks von Zhang, Kuan, Hu, Haoji, Philbrick, Kenneth, Conte, Gian Marco, Sobek, Joseph D, Rouzrokh, Pouria, Erickson, Bradley J

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Feasibility of Observing Cerebrovascular Disease Phenotypes with Smartphone Monitoring: Study Design Considerations for Real-World Studies von Zawada, Stephanie J, Ganjizadeh, Ali, Hagen, Clint E, Demaerschalk, Bart M, Erickson, Bradley J

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What Does Deep Learning See? Insights From a Classifier Trained to Predict Contrast Enhancement Phase From CT Images von Philbrick, Kenneth A, Yoshida, Kotaro, Inoue, Dai, Akkus, Zeynettin, Kline, Timothy L, Weston, Alexander D, Korfiatis, Panagiotis, Takahashi, Naoki, Erickson, Bradley J

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Brain Gliomas: Multicenter Standardized Assessment of Dynamic Contrast-enhanced and Dynamic Susceptibility Contrast MR Images von Anzalone, Nicoletta, Castellano, Antonella, Cadioli, Marcello, Conte, Gian Marco, Cuccarini, Valeria, Bizzi, Alberto, Grimaldi, Marco, Costa, Antonella, Grillea, Giovanni, Vitali, Paolo, Aquino, Domenico, Terreni, Maria Rosa, Torri, Valter, Erickson, Bradley J, Caulo, Massimo

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Topological data analysis in medical imaging: current state of the art von Singh, Yashbir, Farrelly, Colleen M., Hathaway, Quincy A., Leiner, Tim, Jagtap, Jaidip, Carlsson, Gunnar E., Erickson, Bradley J.

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Performance of an Artificial Multi-observer Deep Neural Network for Fully Automated Segmentation of Polycystic Kidneys von Kline, Timothy L., Korfiatis, Panagiotis, Edwards, Marie E., Blais, Jaime D., Czerwiec, Frank S., Harris, Peter C., King, Bernard F., Torres, Vicente E., Erickson, Bradley J.

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