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1. Language Models and Retrieval Augmented Generation for Automated Structured Data Extraction from Diagnostic Reports

Author

Jabal, Mohamed Sobhi, Warman, Pranav, Zhang, Jikai, Gupta, Kartikeye, Jain, Ayush, Mazurowski, Maciej, Wiggins, Walter, Magudia, Kirti, and Calabrese, Evan

Subjects
Computer Science - Computation and Language, Computer Science - Information Retrieval, Computer Science - Machine Learning, J.3, I.2, I.2.7
Abstract

Purpose: To develop and evaluate an automated system for extracting structured clinical information from unstructured radiology and pathology reports using open-weights large language models (LMs) and retrieval augmented generation (RAG), and to assess the effects of model configuration variables on extraction performance. Methods and Materials: The study utilized two datasets: 7,294 radiology reports annotated for Brain Tumor Reporting and Data System (BT-RADS) scores and 2,154 pathology reports annotated for isocitrate dehydrogenase (IDH) mutation status. An automated pipeline was developed to benchmark the performance of various LMs and RAG configurations. The impact of model size, quantization, prompting strategies, output formatting, and inference parameters was systematically evaluated. Results: The best performing models achieved over 98% accuracy in extracting BT-RADS scores from radiology reports and over 90% for IDH mutation status extraction from pathology reports. The top model being medical fine-tuned llama3. Larger, newer, and domain fine-tuned models consistently outperformed older and smaller models. Model quantization had minimal impact on performance. Few-shot prompting significantly improved accuracy. RAG improved performance for complex pathology reports but not for shorter radiology reports. Conclusions: Open LMs demonstrate significant potential for automated extraction of structured clinical data from unstructured clinical reports with local privacy-preserving application. Careful model selection, prompt engineering, and semi-automated optimization using annotated data are critical for optimal performance. These approaches could be reliable enough for practical use in research workflows, highlighting the potential for human-machine collaboration in healthcare data extraction.

Published
2024

2. BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023

Author

Kazerooni, Anahita Fathi, Khalili, Nastaran, Liu, Xinyang, Haldar, Debanjan, Jiang, Zhifan, Zapaishchykova, Anna, Pavaine, Julija, Shah, Lubdha M., Jones, Blaise V., Sheth, Nakul, Prabhu, Sanjay P., McAllister, Aaron S., Tu, Wenxin, Nandolia, Khanak K., Rodriguez, Andres F., Shaikh, Ibraheem Salman, Montano, Mariana Sanchez, Lai, Hollie Anne, Adewole, Maruf, Albrecht, Jake, Anazodo, Udunna, Anderson, Hannah, Anwar, Syed Muhammed, Aristizabal, Alejandro, Bagheri, Sina, Baid, Ujjwal, Bergquist, Timothy, Borja, Austin J., Calabrese, Evan, Chung, Verena, Conte, Gian-Marco, Eddy, James, Ezhov, Ivan, Familiar, Ariana M., Farahani, Keyvan, Gandhi, Deep, Gottipati, Anurag, Haldar, Shuvanjan, Iglesias, Juan Eugenio, Janas, Anastasia, Elaine, Elaine, Karargyris, Alexandros, Kassem, Hasan, Khalili, Neda, Kofler, Florian, LaBella, Dominic, Van Leemput, Koen, Li, Hongwei B., Maleki, Nazanin, Meier, Zeke, Menze, Bjoern, Moawad, Ahmed W., Pati, Sarthak, Piraud, Marie, Poussaint, Tina, Reitman, Zachary J., Rudie, Jeffrey D., Saluja, Rachit, Sheller, MIcah, Shinohara, Russell Takeshi, Viswanathan, Karthik, Wang, Chunhao, Wiestler, Benedikt, Wiggins, Walter F., Davatzikos, Christos, Storm, Phillip B., Bornhorst, Miriam, Packer, Roger, Hummel, Trent, de Blank, Peter, Hoffman, Lindsey, Aboian, Mariam, Nabavizadeh, Ali, Ware, Jeffrey B., Kann, Benjamin H., Rood, Brian, Resnick, Adam, Bakas, Spyridon, Vossough, Arastoo, and Linguraru, Marius George

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Pediatric central nervous system tumors are the leading cause of cancer-related deaths in children. The five-year survival rate for high-grade glioma in children is less than 20%. The development of new treatments is dependent upon multi-institutional collaborative clinical trials requiring reproducible and accurate centralized response assessment. We present the results of the BraTS-PEDs 2023 challenge, the first Brain Tumor Segmentation (BraTS) challenge focused on pediatric brain tumors. This challenge utilized data acquired from multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. BraTS-PEDs 2023 aimed to evaluate volumetric segmentation algorithms for pediatric brain gliomas from magnetic resonance imaging using standardized quantitative performance evaluation metrics employed across the BraTS 2023 challenges. The top-performing AI approaches for pediatric tumor analysis included ensembles of nnU-Net and Swin UNETR, Auto3DSeg, or nnU-Net with a self-supervised framework. The BraTSPEDs 2023 challenge fostered collaboration between clinicians (neuro-oncologists, neuroradiologists) and AI/imaging scientists, promoting faster data sharing and the development of automated volumetric analysis techniques. These advancements could significantly benefit clinical trials and improve the care of children with brain tumors.

Published
2024

3. Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge

Author

LaBella, Dominic, Baid, Ujjwal, Khanna, Omaditya, McBurney-Lin, Shan, McLean, Ryan, Nedelec, Pierre, Rashid, Arif, Tahon, Nourel Hoda, Altes, Talissa, Bhalerao, Radhika, Dhemesh, Yaseen, Godfrey, Devon, Hilal, Fathi, Floyd, Scott, Janas, Anastasia, Kazerooni, Anahita Fathi, Kirkpatrick, John, Kent, Collin, Kofler, Florian, Leu, Kevin, Maleki, Nazanin, Menze, Bjoern, Pajot, Maxence, Reitman, Zachary J., Rudie, Jeffrey D., Saluja, Rachit, Velichko, Yury, Wang, Chunhao, Warman, Pranav, Adewole, Maruf, Albrecht, Jake, Anazodo, Udunna, Anwar, Syed Muhammad, Bergquist, Timothy, Chen, Sully Francis, Chung, Verena, Conte, Gian-Marco, Dako, Farouk, Eddy, James, Ezhov, Ivan, Khalili, Nastaran, Iglesias, Juan Eugenio, Jiang, Zhifan, Johanson, Elaine, Van Leemput, Koen, Li, Hongwei Bran, Linguraru, Marius George, Liu, Xinyang, Mahtabfar, Aria, Meier, Zeke, Moawad, Ahmed W., Mongan, John, Piraud, Marie, Shinohara, Russell Takeshi, Wiggins, Walter F., Abayazeed, Aly H., Akinola, Rachel, Jakab, András, Bilello, Michel, de Verdier, Maria Correia, Crivellaro, Priscila, Davatzikos, Christos, Farahani, Keyvan, Freymann, John, Hess, Christopher, Huang, Raymond, Lohmann, Philipp, Moassefi, Mana, Pease, Matthew W., Vollmuth, Phillipp, Sollmann, Nico, Diffley, David, Nandolia, Khanak K., Warren, Daniel I., Hussain, Ali, Fehringer, Pascal, Bronstein, Yulia, Deptula, Lisa, Stein, Evan G., Taherzadeh, Mahsa, de Oliveira, Eduardo Portela, Haughey, Aoife, Kontzialis, Marinos, Saba, Luca, Turner, Benjamin, Brüßeler, Melanie M. T., Ansari, Shehbaz, Gkampenis, Athanasios, Weiss, David Maximilian, Mansour, Aya, Shawali, Islam H., Yordanov, Nikolay, Stein, Joel M., Hourani, Roula, Moshebah, Mohammed Yahya, Abouelatta, Ahmed Magdy, Rizvi, Tanvir, Willms, Klara, Martin, Dann C., Okar, Abdullah, D'Anna, Gennaro, Taha, Ahmed, Sharifi, Yasaman, Faghani, Shahriar, Kite, Dominic, Pinho, Marco, Haider, Muhammad Ammar, Aristizabal, Alejandro, Karargyris, Alexandros, Kassem, Hasan, Pati, Sarthak, Sheller, Micah, Alonso-Basanta, Michelle, Villanueva-Meyer, Javier, Rauschecker, Andreas M., Nada, Ayman, Aboian, Mariam, Flanders, Adam E., Wiestler, Benedikt, Bakas, Spyridon, and Calabrese, Evan

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas, which are typically benign extra-axial tumors with diverse radiologic and anatomical presentation and a propensity for multiplicity. Nine participating teams each developed deep-learning automated segmentation models using image data from the largest multi-institutional systematically expert annotated multilabel multi-sequence meningioma MRI dataset to date, which included 1000 training set cases, 141 validation set cases, and 283 hidden test set cases. Each case included T2, T2/FLAIR, T1, and T1Gd brain MRI sequences with associated tumor compartment labels delineating enhancing tumor, non-enhancing tumor, and surrounding non-enhancing T2/FLAIR hyperintensity. Participant automated segmentation models were evaluated and ranked based on a scoring system evaluating lesion-wise metrics including dice similarity coefficient (DSC) and 95% Hausdorff Distance. The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor, respectively and a corresponding average DSC of 0.899, 0.904, and 0.871, respectively. These results serve as state-of-the-art benchmarks for future pre-operative meningioma automated segmentation algorithms. Additionally, we found that 1286 of 1424 cases (90.3%) had at least 1 compartment voxel abutting the edge of the skull-stripped image edge, which requires further investigation into optimal pre-processing face anonymization steps., Comment: 16 pages, 11 tables, 10 figures, MICCAI

Published
2024

5. The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI

Author

Moawad, Ahmed W., Janas, Anastasia, Baid, Ujjwal, Ramakrishnan, Divya, Saluja, Rachit, Ashraf, Nader, Jekel, Leon, Amiruddin, Raisa, Adewole, Maruf, Albrecht, Jake, Anazodo, Udunna, Aneja, Sanjay, Anwar, Syed Muhammad, Bergquist, Timothy, Calabrese, Evan, Chiang, Veronica, Chung, Verena, Conte, Gian Marco Marco, Dako, Farouk, Eddy, James, Ezhov, Ivan, Familiar, Ariana, Farahani, Keyvan, Iglesias, Juan Eugenio, Jiang, Zhifan, Johanson, Elaine, Kazerooni, Anahita Fathi, Kofler, Florian, Krantchev, Kiril, LaBella, Dominic, Van Leemput, Koen, Li, Hongwei Bran, Linguraru, Marius George, Link, Katherine E., Liu, Xinyang, Maleki, Nazanin, Meier, Zeke, Menze, Bjoern H, Moy, Harrison, Osenberg, Klara, Piraud, Marie, Reitman, Zachary, Shinohara, Russel Takeshi, Tahon, Nourel hoda, Nada, Ayman, Velichko, Yuri S., Wang, Chunhao, Wiestler, Benedikt, Wiggins, Walter, Shafique, Umber, Willms, Klara, Avesta, Arman, Bousabarah, Khaled, Chakrabarty, Satrajit, Gennaro, Nicolo, Holler, Wolfgang, Kaur, Manpreet, LaMontagne, Pamela, Lin, MingDe, Lost, Jan, Marcus, Daniel S., Maresca, Ryan, Merkaj, Sarah, Nada, Ayaman, Pedersen, Gabriel Cassinelli, von Reppert, Marc, Sotiras, Aristeidis, Teytelboym, Oleg, Tillmans, Niklas, Westerhoff, Malte, Youssef, Ayda, Godfrey, Devon, Floyd, Scott, Rauschecker, Andreas, Villanueva-Meyer, Javier, Pflüger, Irada, Cho, Jaeyoung, Bendszus, Martin, Brugnara, Gianluca, Cramer, Justin, Perez-Carillo, Gloria J. Guzman, Johnson, Derek R., Kam, Anthony, Kwan, Benjamin Yin Ming, Lai, Lillian, Lall, Neil U., Memon, Fatima, Patro, Satya Narayana, Petrovic, Bojan, So, Tiffany Y., Thompson, Gerard, Wu, Lei, Schrickel, E. Brooke, Bansal, Anu, Barkhof, Frederik, Besada, Cristina, Chu, Sammy, Druzgal, Jason, Dusoi, Alexandru, Farage, Luciano, Feltrin, Fabricio, Fong, Amy, Fung, Steve H., Gray, R. Ian, Ikuta, Ichiro, Iv, Michael, Postma, Alida A., Mahajan, Amit, Joyner, David, Krumpelman, Chase, Letourneau-Guillon, Laurent, Lincoln, Christie M., Maros, Mate E., Miller, Elka, Morón, Fanny, Nimchinsky, Esther A., Ozsarlak, Ozkan, Patel, Uresh, Rohatgi, Saurabh, Saha, Atin, Sayah, Anousheh, Schwartz, Eric D., Shih, Robert, Shiroishi, Mark S., Small, Juan E., Tanwar, Manoj, Valerie, Jewels, Weinberg, Brent D., White, Matthew L., Young, Robert, Zohrabian, Vahe M., Azizova, Aynur, Brüßeler, Melanie Maria Theresa, Fehringer, Pascal, Ghonim, Mohanad, Ghonim, Mohamed, Gkampenis, Athanasios, Okar, Abdullah, Pasquini, Luca, Sharifi, Yasaman, Singh, Gagandeep, Sollmann, Nico, Soumala, Theodora, Taherzadeh, Mahsa, Yordanov, Nikolay, Vollmuth, Philipp, Foltyn-Dumitru, Martha, Malhotra, Ajay, Abayazeed, Aly H., Dellepiane, Francesco, Lohmann, Philipp, Pérez-García, Víctor M., Elhalawani, Hesham, Al-Rubaiey, Sanaria, Armindo, Rui Duarte, Ashraf, Kholod, Asla, Moamen M., Badawy, Mohamed, Bisschop, Jeroen, Lomer, Nima Broomand, Bukatz, Jan, Chen, Jim, Cimflova, Petra, Corr, Felix, Crawley, Alexis, Deptula, Lisa, Elakhdar, Tasneem, Shawali, Islam H., Faghani, Shahriar, Frick, Alexandra, Gulati, Vaibhav, Haider, Muhammad Ammar, Hierro, Fátima, Dahl, Rasmus Holmboe, Jacobs, Sarah Maria, Hsieh, Kuang-chun Jim, Kandemirli, Sedat G., Kersting, Katharina, Kida, Laura, Kollia, Sofia, Koukoulithras, Ioannis, Li, Xiao, Abouelatta, Ahmed, Mansour, Aya, Maria-Zamfirescu, Ruxandra-Catrinel, Marsiglia, Marcela, Mateo-Camacho, Yohana Sarahi, McArthur, Mark, McDonnell, Olivia, McHugh, Maire, Moassefi, Mana, Morsi, Samah Mostafa, Muntenu, Alexander, Nandolia, Khanak K., Naqvi, Syed Raza, Nikanpour, Yalda, Alnoury, Mostafa, Nouh, Abdullah Mohamed Aly, Pappafava, Francesca, Patel, Markand D., Petrucci, Samantha, Rawie, Eric, Raymond, Scott, Roohani, Borna, Sabouhi, Sadeq, Sanchez-Garcia, Laura M., Shaked, Zoe, Suthar, Pokhraj P., Altes, Talissa, Isufi, Edvin, Dhermesh, Yaseen, Gass, Jaime, Thacker, Jonathan, Tarabishy, Abdul Rahman, Turner, Benjamin, Vacca, Sebastiano, Vilanilam, George K., Warren, Daniel, Weiss, David, Worede, Fikadu, Yousry, Sara, Lerebo, Wondwossen, Aristizabal, Alejandro, Karargyris, Alexandros, Kassem, Hasan, Pati, Sarthak, Sheller, Micah, Bakas, Spyridon, Rudie, Jeffrey D., and Aboian, Mariam

Subjects
Quantitative Biology - Other Quantitative Biology, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space.The BraTS-METS 2023 challenge successfully curated well-annotated, diverse datasets and identified common errors, facilitating the translation of BM segmentation across varied clinical environments and providing personalized volumetric reports to patients undergoing BM treatment.

Published
2023

6. The Brain Tumor Segmentation (BraTS) Challenge 2023: Glioma Segmentation in Sub-Saharan Africa Patient Population (BraTS-Africa)

Author

Adewole, Maruf, Rudie, Jeffrey D., Gbadamosi, Anu, Toyobo, Oluyemisi, Raymond, Confidence, Zhang, Dong, Omidiji, Olubukola, Akinola, Rachel, Suwaid, Mohammad Abba, Emegoakor, Adaobi, Ojo, Nancy, Aguh, Kenneth, Kalaiwo, Chinasa, Babatunde, Gabriel, Ogunleye, Afolabi, Gbadamosi, Yewande, Iorpagher, Kator, Calabrese, Evan, Aboian, Mariam, Linguraru, Marius, Albrecht, Jake, Wiestler, Benedikt, Kofler, Florian, Janas, Anastasia, LaBella, Dominic, Kzerooni, Anahita Fathi, Li, Hongwei Bran, Iglesias, Juan Eugenio, Farahani, Keyvan, Eddy, James, Bergquist, Timothy, Chung, Verena, Shinohara, Russell Takeshi, Wiggins, Walter, Reitman, Zachary, Wang, Chunhao, Liu, Xinyang, Jiang, Zhifan, Familiar, Ariana, Van Leemput, Koen, Bukas, Christina, Piraud, Maire, Conte, Gian-Marco, Johansson, Elaine, Meier, Zeke, Menze, Bjoern H, Baid, Ujjwal, Bakas, Spyridon, Dako, Farouk, Fatade, Abiodun, and Anazodo, Udunna C

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Physics - Medical Physics
Abstract

Gliomas are the most common type of primary brain tumors. Although gliomas are relatively rare, they are among the deadliest types of cancer, with a survival rate of less than 2 years after diagnosis. Gliomas are challenging to diagnose, hard to treat and inherently resistant to conventional therapy. Years of extensive research to improve diagnosis and treatment of gliomas have decreased mortality rates across the Global North, while chances of survival among individuals in low- and middle-income countries (LMICs) remain unchanged and are significantly worse in Sub-Saharan Africa (SSA) populations. Long-term survival with glioma is associated with the identification of appropriate pathological features on brain MRI and confirmation by histopathology. Since 2012, the Brain Tumor Segmentation (BraTS) Challenge have evaluated state-of-the-art machine learning methods to detect, characterize, and classify gliomas. However, it is unclear if the state-of-the-art methods can be widely implemented in SSA given the extensive use of lower-quality MRI technology, which produces poor image contrast and resolution and more importantly, the propensity for late presentation of disease at advanced stages as well as the unique characteristics of gliomas in SSA (i.e., suspected higher rates of gliomatosis cerebri). Thus, the BraTS-Africa Challenge provides a unique opportunity to include brain MRI glioma cases from SSA in global efforts through the BraTS Challenge to develop and evaluate computer-aided-diagnostic (CAD) methods for the detection and characterization of glioma in resource-limited settings, where the potential for CAD tools to transform healthcare are more likely., Comment: arXiv admin note: text overlap with arXiv:2107.02314

Published
2023

7. The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)

Author

Kazerooni, Anahita Fathi, Khalili, Nastaran, Liu, Xinyang, Haldar, Debanjan, Jiang, Zhifan, Anwar, Syed Muhammed, Albrecht, Jake, Adewole, Maruf, Anazodo, Udunna, Anderson, Hannah, Bagheri, Sina, Baid, Ujjwal, Bergquist, Timothy, Borja, Austin J., Calabrese, Evan, Chung, Verena, Conte, Gian-Marco, Dako, Farouk, Eddy, James, Ezhov, Ivan, Familiar, Ariana, Farahani, Keyvan, Haldar, Shuvanjan, Iglesias, Juan Eugenio, Janas, Anastasia, Johansen, Elaine, Jones, Blaise V, Kofler, Florian, LaBella, Dominic, Lai, Hollie Anne, Van Leemput, Koen, Li, Hongwei Bran, Maleki, Nazanin, McAllister, Aaron S, Meier, Zeke, Menze, Bjoern, Moawad, Ahmed W, Nandolia, Khanak K, Pavaine, Julija, Piraud, Marie, Poussaint, Tina, Prabhu, Sanjay P, Reitman, Zachary, Rodriguez, Andres, Rudie, Jeffrey D, Sanchez-Montano, Mariana, Shaikh, Ibraheem Salman, Shah, Lubdha M., Sheth, Nakul, Shinohara, Russel Taki, Tu, Wenxin, Viswanathan, Karthik, Wang, Chunhao, Ware, Jeffrey B, Wiestler, Benedikt, Wiggins, Walter, Zapaishchykova, Anna, Aboian, Mariam, Bornhorst, Miriam, de Blank, Peter, Deutsch, Michelle, Fouladi, Maryam, Hoffman, Lindsey, Kann, Benjamin, Lazow, Margot, Mikael, Leonie, Nabavizadeh, Ali, Packer, Roger, Resnick, Adam, Rood, Brian, Vossough, Arastoo, Bakas, Spyridon, and Linguraru, Marius George

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Quantitative Biology - Quantitative Methods
Abstract

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20\%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. The MICCAI Brain Tumor Segmentation (BraTS) Challenge is a landmark community benchmark event with a successful history of 12 years of resource creation for the segmentation and analysis of adult glioma. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge, which represents the first BraTS challenge focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The BraTS-PEDs 2023 challenge focuses on benchmarking the development of volumentric segmentation algorithms for pediatric brain glioma through standardized quantitative performance evaluation metrics utilized across the BraTS 2023 cluster of challenges. Models gaining knowledge from the BraTS-PEDs multi-parametric structural MRI (mpMRI) training data will be evaluated on separate validation and unseen test mpMRI dataof high-grade pediatric glioma. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors.

Published
2023

8. The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)

Author

Li, Hongwei Bran, Conte, Gian Marco, Anwar, Syed Muhammad, Kofler, Florian, Ezhov, Ivan, van Leemput, Koen, Piraud, Marie, Diaz, Maria, Cole, Byrone, Calabrese, Evan, Rudie, Jeff, Meissen, Felix, Adewole, Maruf, Janas, Anastasia, Kazerooni, Anahita Fathi, LaBella, Dominic, Moawad, Ahmed W., Farahani, Keyvan, Eddy, James, Bergquist, Timothy, Chung, Verena, Shinohara, Russell Takeshi, Dako, Farouk, Wiggins, Walter, Reitman, Zachary, Wang, Chunhao, Liu, Xinyang, Jiang, Zhifan, Familiar, Ariana, Johanson, Elaine, Meier, Zeke, Davatzikos, Christos, Freymann, John, Kirby, Justin, Bilello, Michel, Fathallah-Shaykh, Hassan M., Wiest, Roland, Kirschke, Jan, Colen, Rivka R., Kotrotsou, Aikaterini, Lamontagne, Pamela, Marcus, Daniel, Milchenko, Mikhail, Nazeri, Arash, Weber, Marc André, Mahajan, Abhishek, Mohan, Suyash, Mongan, John, Hess, Christopher, Cha, Soonmee, Villanueva, Javier, Colak, Meyer Errol, Crivellaro, Priscila, Jakab, Andras, Albrecht, Jake, Anazodo, Udunna, Aboian, Mariam, Yu, Thomas, Baid, Ujjwal, Bakas, Spyridon, Linguraru, Marius George, Menze, Bjoern, Iglesias, Juan Eugenio, and Wiestler, Benedikt

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input magnetic resonance imaging (MRI) modalities: T1-weighted images with and without contrast enhancement, T2-weighted images, and FLAIR images. However, some sequences are often missing in clinical practice due to time constraints or image artifacts, such as patient motion. Consequently, the ability to substitute missing modalities and gain segmentation performance is highly desirable and necessary for the broader adoption of these algorithms in the clinical routine. In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023. The primary objective of this challenge is to evaluate image synthesis methods that can realistically generate missing MRI modalities when multiple available images are provided. The ultimate aim is to facilitate automated brain tumor segmentation pipelines. The image dataset used in the benchmark is diverse and multi-modal, created through collaboration with various hospitals and research institutions., Comment: Technical report of BraSyn

Published
2023

9. The Brain Tumor Segmentation (BraTS) Challenge: Local Synthesis of Healthy Brain Tissue via Inpainting

Author

Kofler, Florian, Meissen, Felix, Steinbauer, Felix, Graf, Robert, Ehrlich, Stefan K, Reinke, Annika, Oswald, Eva, Waldmannstetter, Diana, Hoelzl, Florian, Horvath, Izabela, Turgut, Oezguen, Shit, Suprosanna, Bukas, Christina, Yang, Kaiyuan, Paetzold, Johannes C., de da Rosa, Ezequiel, Mekki, Isra, Vinayahalingam, Shankeeth, Kassem, Hasan, Zhang, Juexin, Chen, Ke, Weng, Ying, Durrer, Alicia, Cattin, Philippe C., Wolleb, Julia, Sadique, M. S., Rahman, M. M., Farzana, W., Temtam, A., Iftekharuddin, K. M., Adewole, Maruf, Anwar, Syed Muhammad, Baid, Ujjwal, Janas, Anastasia, Kazerooni, Anahita Fathi, LaBella, Dominic, Li, Hongwei Bran, Moawad, Ahmed W, Conte, Gian-Marco, Farahani, Keyvan, Eddy, James, Sheller, Micah, Pati, Sarthak, Karagyris, Alexandros, Aristizabal, Alejandro, Bergquist, Timothy, Chung, Verena, Shinohara, Russell Takeshi, Dako, Farouk, Wiggins, Walter, Reitman, Zachary, Wang, Chunhao, Liu, Xinyang, Jiang, Zhifan, Johanson, Elaine, Meier, Zeke, Familiar, Ariana, Davatzikos, Christos, Freymann, John, Kirby, Justin, Bilello, Michel, Fathallah-Shaykh, Hassan M, Wiest, Roland, Kirschke, Jan, Colen, Rivka R, Kotrotsou, Aikaterini, Lamontagne, Pamela, Marcus, Daniel, Milchenko, Mikhail, Nazeri, Arash, Weber, Marc-André, Mahajan, Abhishek, Mohan, Suyash, Mongan, John, Hess, Christopher, Cha, Soonmee, Villanueva-Meyer, Javier, Colak, Errol, Crivellaro, Priscila, Jakab, Andras, Fatade, Abiodun, Omidiji, Olubukola, Lagos, Rachel Akinola, Olatunji, O O, Khanna, Goldey, Kirkpatrick, John, Alonso-Basanta, Michelle, Rashid, Arif, Bornhorst, Miriam, Nabavizadeh, Ali, Lepore, Natasha, Palmer, Joshua, Porras, Antonio, Albrecht, Jake, Anazodo, Udunna, Aboian, Mariam, Calabrese, Evan, Rudie, Jeffrey David, Linguraru, Marius George, Iglesias, Juan Eugenio, Van Leemput, Koen, Bakas, Spyridon, Wiestler, Benedikt, Ezhov, Ivan, Piraud, Marie, and Menze, Bjoern H

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

A myriad of algorithms for the automatic analysis of brain MR images is available to support clinicians in their decision-making. For brain tumor patients, the image acquisition time series typically starts with an already pathological scan. This poses problems, as many algorithms are designed to analyze healthy brains and provide no guarantee for images featuring lesions. Examples include, but are not limited to, algorithms for brain anatomy parcellation, tissue segmentation, and brain extraction. To solve this dilemma, we introduce the BraTS inpainting challenge. Here, the participants explore inpainting techniques to synthesize healthy brain scans from lesioned ones. The following manuscript contains the task formulation, dataset, and submission procedure. Later, it will be updated to summarize the findings of the challenge. The challenge is organized as part of the ASNR-BraTS MICCAI challenge., Comment: 14 pages, 6 figures

Published
2023

10. The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma

Author

LaBella, Dominic, Adewole, Maruf, Alonso-Basanta, Michelle, Altes, Talissa, Anwar, Syed Muhammad, Baid, Ujjwal, Bergquist, Timothy, Bhalerao, Radhika, Chen, Sully, Chung, Verena, Conte, Gian-Marco, Dako, Farouk, Eddy, James, Ezhov, Ivan, Godfrey, Devon, Hilal, Fathi, Familiar, Ariana, Farahani, Keyvan, Iglesias, Juan Eugenio, Jiang, Zhifan, Johanson, Elaine, Kazerooni, Anahita Fathi, Kent, Collin, Kirkpatrick, John, Kofler, Florian, Van Leemput, Koen, Li, Hongwei Bran, Liu, Xinyang, Mahtabfar, Aria, McBurney-Lin, Shan, McLean, Ryan, Meier, Zeke, Moawad, Ahmed W, Mongan, John, Nedelec, Pierre, Pajot, Maxence, Piraud, Marie, Rashid, Arif, Reitman, Zachary, Shinohara, Russell Takeshi, Velichko, Yury, Wang, Chunhao, Warman, Pranav, Wiggins, Walter, Aboian, Mariam, Albrecht, Jake, Anazodo, Udunna, Bakas, Spyridon, Flanders, Adam, Janas, Anastasia, Khanna, Goldey, Linguraru, Marius George, Menze, Bjoern, Nada, Ayman, Rauschecker, Andreas M, Rudie, Jeff, Tahon, Nourel Hoda, Villanueva-Meyer, Javier, Wiestler, Benedikt, and Calabrese, Evan

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Meningiomas are the most common primary intracranial tumor in adults and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on multiparametric MRI (mpMRI) for diagnosis, treatment planning, and longitudinal treatment monitoring; yet automated, objective, and quantitative tools for non-invasive assessment of meningiomas on mpMRI are lacking. The BraTS meningioma 2023 challenge will provide a community standard and benchmark for state-of-the-art automated intracranial meningioma segmentation models based on the largest expert annotated multilabel meningioma mpMRI dataset to date. Challenge competitors will develop automated segmentation models to predict three distinct meningioma sub-regions on MRI including enhancing tumor, non-enhancing tumor core, and surrounding nonenhancing T2/FLAIR hyperintensity. Models will be evaluated on separate validation and held-out test datasets using standardized metrics utilized across the BraTS 2023 series of challenges including the Dice similarity coefficient and Hausdorff distance. The models developed during the course of this challenge will aid in incorporation of automated meningioma MRI segmentation into clinical practice, which will ultimately improve care of patients with meningioma.

Published
2023

12. Head CT deep learning model is highly accurate for early infarct estimation

Author

Gauriau, Romane, Bizzo, Bernardo C., Comeau, Donnella S., Hillis, James M., Bridge, Christopher P., Chin, John K., Pawar, Jayashri, Pourvaziri, Ali, Sesic, Ivana, Sharaf, Elshaimaa, Cao, Jinjin, Noro, Flavia T. C., Wiggins, Walter F., Caton, M. Travis, Kitamura, Felipe, Dreyer, Keith J., Kalafut, John F., Andriole, Katherine P., Pomerantz, Stuart R., Gonzalez, Ramon G., and Lev, Michael H.

Published
2023
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14. Imaging AI in Practice: A Demonstration of Future Workflow Using Integration Standards.

Author

Wiggins, Walter F, Magudia, Kirti, Schmidt, Teri M Sippel, O'Connor, Stacy D, Carr, Christopher D, Kohli, Marc D, and Andriole, Katherine P

Subjects
Information and Computing Sciences, Biomedical and Clinical Sciences, Clinical Sciences, Networking and Information Technology R&D (NITRD), Biomedical Imaging, 4.2 Evaluation of markers and technologies, Detection, screening and diagnosis, Computer Applications-General, Technology Assessment
Abstract

Artificial intelligence (AI) tools are rapidly being developed for radiology and other clinical areas. These tools have the potential to dramatically change clinical practice; however, for these tools to be usable and function as intended, they must be integrated into existing radiology systems. In a collaborative effort between the Radiological Society of North America, radiologists, and imaging-focused vendors, the Imaging AI in Practice (IAIP) demonstrations were developed to show how AI tools can generate, consume, and present results throughout the radiology workflow in a simulated clinical environment. The IAIP demonstrations highlight the critical importance of semantic and interoperability standards, as well as orchestration profiles for successful clinical integration of radiology AI tools. Keywords: Computer Applications-General (Informatics), Technology Assessment © RSNA, 2021.

Published
2021

17. Imaging of Traumatic Brain Injury

Author

Wiggins, Walter F., Dixon, Austin X., Kranz, Peter G., Torres, Carlos, Section editor, Patlas, Michael N., editor, Katz, Douglas S., editor, and Scaglione, Mariano, editor

Published
2022
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23. The Brain Tumor Segmentation in Pediatrics (BraTS-PEDs) Challenge: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)

Author

Kazerooni, Anahita Fathi, Khalili, Nastaran, Gandhi, Deep, Liu, Xinyang, Jiang, Zhifan, Anwar, Syed Muhammed, Albrecht, Jake, Adewole, Maruf, Anazodo, Udunna, Anderson, Hannah, Bagheri, Sina, Baid, Ujjwal, Bergquist, Timothy, Borja, Austin J., Calabrese, Evan, Chung, Verena, Conte, Gian-Marco, Dako, Farouk, Eddy, James, Ezhov, Ivan, Familiar, Ariana, Farahani, Keyvan, Gottipati, Anurag, Haldar, Debanjan, Haldar, Shuvanjan, Iglesias, Juan Eugenio, Janas, Anastasia, Johansen, Elaine, Jones, Blaise V, Khalili, Neda, Kofler, Florian, LaBella, Dominic, Lai, Hollie Anne, Van Leemput, Koen, Li, Hongwei Bran, Maleki, Nazanin, McAllister, Aaron S, Meier, Zeke, Menze, Bjoern, Moawad, Ahmed W, Nandolia, Khanak K, Pavaine, Julija, Piraud, Marie, Poussaint, Tina, Prabhu, Sanjay P, Reitman, Zachary, Rodriguez, Andres, Rudie, Jeffrey D, Sanchez-Montano, Mariana, Shaikh, Ibraheem Salman, Shah, Lubdha M., Sheth, Nakul, Shinohara, Russel Taki, Tu, Wenxin, Viswanathan, Karthik, Wang, Chunhao, Ware, Jeffrey B, Wiestler, Benedikt, Wiggins, Walter, Zapaishchykova, Anna, Aboian, Mariam, Bornhorst, Miriam, de Blank, Peter, Deutsch, Michelle, Fouladi, Maryam, Hoffman, Lindsey, Kann, Benjamin, Lazow, Margot, Mikael, Leonie, Nabavizadeh, Ali, Packer, Roger, Bakas, Spyridon, Resnick, Adam, Rood, Brian, Vossough, Arastoo, Linguraru, Marius George, Kazerooni, Anahita Fathi, Khalili, Nastaran, Gandhi, Deep, Liu, Xinyang, Jiang, Zhifan, Anwar, Syed Muhammed, Albrecht, Jake, Adewole, Maruf, Anazodo, Udunna, Anderson, Hannah, Bagheri, Sina, Baid, Ujjwal, Bergquist, Timothy, Borja, Austin J., Calabrese, Evan, Chung, Verena, Conte, Gian-Marco, Dako, Farouk, Eddy, James, Ezhov, Ivan, Familiar, Ariana, Farahani, Keyvan, Gottipati, Anurag, Haldar, Debanjan, Haldar, Shuvanjan, Iglesias, Juan Eugenio, Janas, Anastasia, Johansen, Elaine, Jones, Blaise V, Khalili, Neda, Kofler, Florian, LaBella, Dominic, Lai, Hollie Anne, Van Leemput, Koen, Li, Hongwei Bran, Maleki, Nazanin, McAllister, Aaron S, Meier, Zeke, Menze, Bjoern, Moawad, Ahmed W, Nandolia, Khanak K, Pavaine, Julija, Piraud, Marie, Poussaint, Tina, Prabhu, Sanjay P, Reitman, Zachary, Rodriguez, Andres, Rudie, Jeffrey D, Sanchez-Montano, Mariana, Shaikh, Ibraheem Salman, Shah, Lubdha M., Sheth, Nakul, Shinohara, Russel Taki, Tu, Wenxin, Viswanathan, Karthik, Wang, Chunhao, Ware, Jeffrey B, Wiestler, Benedikt, Wiggins, Walter, Zapaishchykova, Anna, Aboian, Mariam, Bornhorst, Miriam, de Blank, Peter, Deutsch, Michelle, Fouladi, Maryam, Hoffman, Lindsey, Kann, Benjamin, Lazow, Margot, Mikael, Leonie, Nabavizadeh, Ali, Packer, Roger, Bakas, Spyridon, Resnick, Adam, Rood, Brian, Vossough, Arastoo, and Linguraru, Marius George

Abstract

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs challenge, focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors., Comment: arXiv admin note: substantial text overlap with arXiv:2305.17033

Published
2024

25. Using Novel Segmentation Technology to Define Safe Corridors for Minimally Invasive Posterior Lumbar Interbody Fusion

Author

Tabarestani, Troy Q., primary, Salven, David S., additional, Sykes, David A. W., additional, Bardeesi, Anas M., additional, Bartlett, Alyssa M., additional, Wang, Timothy Y., additional, Paturu, Mounica R., additional, Dibble, Christopher F., additional, Shaffrey, Christopher I., additional, Ray, Wilson Z., additional, Chi, John H., additional, Wiggins, Walter F., additional, and Abd-El-Barr, Muhammad M., additional

Published
2023
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26. Severe CNS involvement in a subset of long-term treated children with infantile-onset Pompe disease

Author

Kenney-Jung, Daniel, primary, Korlimarla, Aditi, additional, Spiridigliozzi, Gail A., additional, Wiggins, Walter, additional, Malinzak, Michael, additional, Nichting, Gretchen, additional, Jung, Seung-Hye, additional, Sun, Angela, additional, Wang, Raymond Y., additional, Al Shamsi, Aisha, additional, Phornphutkul, Chanika, additional, Owens, James, additional, Provenzale, James M., additional, and Kishnani, Priya S., additional

Published
2023
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30. Two-Year Clinical and Radiographic Outcomes for Percutaneous Lumbar Interbody Fusion With an Expandable Titanium Cage Through Kambin’s Triangle Without Facetectomy

Author

Tabarestani, Troy Q., primary, Wang, Timothy Y., additional, Sykes, David A.W., additional, Mehta, Vikram A., additional, Yang, Lexie Z., additional, Chow, Shein-Chung, additional, Shaffrey, Christopher I., additional, Wiggins, Walter F., additional, Chi, John H., additional, and Abd-El-Barr, Muhammad M., additional

Published
2023
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34. The Brain Tumor Segmentation (BraTS) Challenge 2023: Local Synthesis of Healthy Brain Tissue via Inpainting

Author

Kofler, Florian, Meissen, Felix, Steinbauer, Felix, Graf, Robert, Oswald, Eva, de da Rosa, Ezequiel, Li, Hongwei Bran, Baid, Ujjwal, Hoelzl, Florian, Turgut, Oezguen, Horvath, Izabela, Waldmannstetter, Diana, Bukas, Christina, Adewole, Maruf, Anwar, Syed Muhammad, Janas, Anastasia, Kazerooni, Anahita Fathi, LaBella, Dominic, Moawad, Ahmed W, Farahani, Keyvan, Eddy, James, Bergquist, Timothy, Chung, Verena, Shinohara, Russell Takeshi, Dako, Farouk, Wiggins, Walter, Reitman, Zachary, Wang, Chunhao, Liu, Xinyang, Jiang, Zhifan, Familiar, Ariana, Conte, Gian-Marco, Johanson, Elaine, Meier, Zeke, Davatzikos, Christos, Freymann, John, Kirby, Justin, Bilello, Michel, Fathallah-Shaykh, Hassan M, Wiest, Roland, Kirschke, Jan, Colen, Rivka R, Kotrotsou, Aikaterini, Lamontagne, Pamela, Marcus, Daniel, Milchenko, Mikhail, Nazeri, Arash, Weber, Marc-André, Mahajan, Abhishek, Mohan, Suyash, Mongan, John, Hess, Christopher, Cha, Soonmee, Villanueva-Meyer, Javier, Colak, Errol, Crivellaro, Priscila, Jakab, Andras, Albrecht, Jake, Anazodo, Udunna, Aboian, Mariam, Iglesias, Juan Eugenio, Van Leemput, Koen, Bakas, Spyridon, Rueckert, Daniel, Wiestler, Benedikt, Ezhov, Ivan, Piraud, Marie, Menze, Bjoern, Kofler, Florian, Meissen, Felix, Steinbauer, Felix, Graf, Robert, Oswald, Eva, de da Rosa, Ezequiel, Li, Hongwei Bran, Baid, Ujjwal, Hoelzl, Florian, Turgut, Oezguen, Horvath, Izabela, Waldmannstetter, Diana, Bukas, Christina, Adewole, Maruf, Anwar, Syed Muhammad, Janas, Anastasia, Kazerooni, Anahita Fathi, LaBella, Dominic, Moawad, Ahmed W, Farahani, Keyvan, Eddy, James, Bergquist, Timothy, Chung, Verena, Shinohara, Russell Takeshi, Dako, Farouk, Wiggins, Walter, Reitman, Zachary, Wang, Chunhao, Liu, Xinyang, Jiang, Zhifan, Familiar, Ariana, Conte, Gian-Marco, Johanson, Elaine, Meier, Zeke, Davatzikos, Christos, Freymann, John, Kirby, Justin, Bilello, Michel, Fathallah-Shaykh, Hassan M, Wiest, Roland, Kirschke, Jan, Colen, Rivka R, Kotrotsou, Aikaterini, Lamontagne, Pamela, Marcus, Daniel, Milchenko, Mikhail, Nazeri, Arash, Weber, Marc-André, Mahajan, Abhishek, Mohan, Suyash, Mongan, John, Hess, Christopher, Cha, Soonmee, Villanueva-Meyer, Javier, Colak, Errol, Crivellaro, Priscila, Jakab, Andras, Albrecht, Jake, Anazodo, Udunna, Aboian, Mariam, Iglesias, Juan Eugenio, Van Leemput, Koen, Bakas, Spyridon, Rueckert, Daniel, Wiestler, Benedikt, Ezhov, Ivan, Piraud, Marie, and Menze, Bjoern

Abstract

A myriad of algorithms for the automatic analysis of brain MR images is available to support clinicians in their decision-making. For brain tumor patients, the image acquisition time series typically starts with a scan that is already pathological. This poses problems, as many algorithms are designed to analyze healthy brains and provide no guarantees for images featuring lesions. Examples include but are not limited to algorithms for brain anatomy parcellation, tissue segmentation, and brain extraction. To solve this dilemma, we introduce the BraTS 2023 inpainting challenge. Here, the participants' task is to explore inpainting techniques to synthesize healthy brain scans from lesioned ones. The following manuscript contains the task formulation, dataset, and submission procedure. Later it will be updated to summarize the findings of the challenge. The challenge is organized as part of the BraTS 2023 challenge hosted at the MICCAI 2023 conference in Vancouver, Canada., Comment: 5 pages, 1 figure

Published
2023

35. How dimensions can guide surgical planning and training: a systematic review of Kambin’s triangle

Author

Waguia Kouam, Romaric, primary, Tabarestani, Troy Q., additional, Sykes, David A. W., additional, Gupta, Nithin, additional, Futch, Brittany G., additional, Kakmou, Elisabeth, additional, Goodwin, C. Rory, additional, Foster, Norah A., additional, Than, Khoi D., additional, Wiggins, Walter F., additional, and Abd-El-Barr, Muhammad M., additional

Published
2023
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37. Implementation of Clinical Artificial Intelligence in Radiology: Who Decides and How?

Author

Daye, Dania, primary, Wiggins, Walter F., additional, Lungren, Matthew P., additional, Alkasab, Tarik, additional, Kottler, Nina, additional, Allen, Bibb, additional, Roth, Christopher J., additional, Bizzo, Bernardo C., additional, Durniak, Kimberly, additional, Brink, James A., additional, Larson, David B., additional, Dreyer, Keith J., additional, and Langlotz, Curtis P., additional

Published
2022
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43. BCNU wafer placement with temozolomide (TMZ) in the immediate postoperative period after tumor resection followed by radiation therapy with TMZ in patients with newly diagnosed high grade glioma: final results of a prospective, multi-institutional, phase II trial

Author

Burri, Stuart H., Prabhu, Roshan S., Sumrall, Ashley L., Brick, Wendy, Blaker, Brian D., Heideman, Jr., Brent E., Boltes, Peggy, Kelly, Renee, Symanowski, James T., Wiggins, Walter F., Ashby, Lynn, Norton, H. James, Judy, Kevin, and Asher, Anthony L.

Published
2015
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47. Robotic-Assisted Trajectory Into Kambin's Triangle During Percutaneous Transforaminal Lumbar Interbody Fusion—Initial Case Series Investigating Safety and Efficacy

Author

Dalton, Tara, primary, Sykes, David, additional, Wang, Timothy Y, additional, Donnelly, Dustin, additional, Than, Khoi D, additional, Karikari, Isaac O, additional, Goodwin, C Rory, additional, Gupta, Dhanesh K, additional, Wiggins, Walter F, additional, and Abd-El-Barr, Muhammad M, additional

Published
2021
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48. Prospective evaluation of novice‐acquired three‐dimensional point‐of‐care ultrasound for carotid stenosis

Author

Drews, Elena, primary, Wildman‐Tobriner, Benjamin, additional, Mathews, Amanda, additional, Vissoci, Joao Ricardo N., additional, Kalisz, Kevin, additional, Amrhein, Timothy J., additional, Wiggins, Walter, additional, El Husseini, Nada, additional, Nast, Jacob, additional, Ruderman, Brandon T., additional, Theophanous, Rebecca, additional, Peethumnongsin, Erica, additional, Fernandes, Nayara, additional, and Broder, Joshua S., additional

Published
2021
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