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1. 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

2. 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

3. 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, Liu, Xinyang, Gandhi, Deep, Jiang, Zhifan, Anwar, Syed Muhammed, Albrecht, Jake, Adewole, Maruf, Anazodo, Udunna, Anderson, Hannah, Baid, Ujjwal, Bergquist, Timothy, Borja, Austin J., Calabrese, Evan, Chung, Verena, Conte, Gian-Marco, Dako, Farouk, Eddy, James, Ezhov, Ivan, Familiar, Ariana, Farahani, Keyvan, Franson, Andrea, Gottipati, Anurag, 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, Rudie, Jeffrey D, Sanchez-Montano, Mariana, Shaikh, Ibraheem Salman, Sheth, Nakul, Tu, Wenxin, Wang, Chunhao, Ware, Jeffrey B, Wiestler, Benedikt, Zapaishchykova, Anna, Bornhorst, Miriam, Deutsch, Michelle, Fouladi, Maryam, Lazow, Margot, Mikael, Leonie, Hummel, Trent, Kann, Benjamin, de Blank, Peter, Hoffman, Lindsey, Aboian, Mariam, Nabavizadeh, Ali, Packer, Roger, Bakas, Spyridon, Resnick, Adam, Rood, Brian, Vossough, Arastoo, and Linguraru, Marius George

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

4. 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

5. 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

6. 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

7. 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

8. 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, and Menze, Bjoern

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 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

9. 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

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

Author

Moawad AW, Janas A, Baid U, Ramakrishnan D, Saluja R, Ashraf N, Jekel L, Amiruddin R, Adewole M, Albrecht J, Anazodo U, Aneja S, Anwar SM, Bergquist T, Calabrese E, Chiang V, Chung V, Conte GMM, Dako F, Eddy J, Ezhov I, Familiar A, Farahani K, Iglesias JE, Jiang Z, Johanson E, Kazerooni AF, Kofler F, Krantchev K, LaBella D, Van Leemput K, Li HB, Linguraru MG, Link KE, Liu X, Maleki N, Meier Z, Menze BH, Moy H, Osenberg K, Piraud M, Reitman Z, Shinohara RT, Tahon NH, Nada A, Velichko YS, Wang C, Wiestler B, Wiggins W, Shafique U, Willms K, Avesta A, Bousabarah K, Chakrabarty S, Gennaro N, Holler W, Kaur M, LaMontagne P, Lin M, Lost J, Marcus DS, Maresca R, Merkaj S, Nada A, Pedersen GC, von Reppert M, Sotiras A, Teytelboym O, Tillmans N, Westerhoff M, Youssef A, Godfrey D, Floyd S, Rauschecker A, Villanueva-Meyer J, Pflüger I, Cho J, Bendszus M, Brugnara G, Cramer J, Perez-Carillo GJG, Johnson DR, Kam A, Kwan BYM, Lai L, Lall NU, Memon F, Patro SN, Petrovic B, So TY, Thompson G, Wu L, Schrickel EB, Bansal A, Barkhof F, Besada C, Chu S, Druzgal J, Dusoi A, Farage L, Feltrin F, Fong A, Fung SH, Gray RI, Ikuta I, Iv M, Postma AA, Mahajan A, Joyner D, Krumpelman C, Letourneau-Guillon L, Lincoln CM, Maros ME, Miller E, Morón F, Nimchinsky EA, Ozsarlak O, Patel U, Rohatgi S, Saha A, Sayah A, Schwartz ED, Shih R, Shiroishi MS, Small JE, Tanwar M, Valerie J, Weinberg BD, White ML, Young R, Zohrabian VM, Azizova A, Brüßeler MMT, Fehringer P, Ghonim M, Ghonim M, Gkampenis A, Okar A, Pasquini L, Sharifi Y, Singh G, Sollmann N, Soumala T, Taherzadeh M, Yordanov N, Vollmuth P, Foltyn-Dumitru M, Malhotra A, Abayazeed AH, Dellepiane F, Lohmann P, Pérez-García VM, Elhalawani H, Al-Rubaiey S, Armindo RD, Ashraf K, Asla MM, Badawy M, Bisschop J, Lomer NB, Bukatz J, Chen J, Cimflova P, Corr F, Crawley A, Deptula L, Elakhdar T, Shawali IH, Faghani S, Frick A, Gulati V, Haider MA, Hierro F, Dahl RH, Jacobs SM, Hsieh KJ, Kandemirli SG, Kersting K, Kida L, Kollia S, Koukoulithras I, Li X, Abouelatta A, Mansour A, Maria-Zamfirescu RC, Marsiglia M, Mateo-Camacho YS, McArthur M, McDonnell O, McHugh M, Moassefi M, Morsi SM, Muntenu A, Nandolia KK, Naqvi SR, Nikanpour Y, Alnoury M, Nouh AMA, Pappafava F, Patel MD, Petrucci S, Rawie E, Raymond S, Roohani B, Sabouhi S, Sanchez-Garcia LM, Shaked Z, Suthar PP, Altes T, Isufi E, Dhermesh Y, Gass J, Thacker J, Tarabishy AR, Turner B, Vacca S, Vilanilam GK, Warren D, Weiss D, Willms K, Worede F, Yousry S, Lerebo W, Aristizabal A, Karargyris A, Kassem H, Pati S, Sheller M, Bakas S, Rudie JD, and Aboian M

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. The mean scores for the teams were calculated. 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. The Dice score for the winning team was 0.65 ± 0.25. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space. The Dice scores and lesion detection rates of all algorithms diminished with decreasing tumor size, particularly for tumors smaller than 100 mm3. In conclusion, algorithms for BM segmentation require further refinement to balance high sensitivity in lesion detection with the minimization of false positives and negatives. 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., Competing Interests: Conflicts of Interest No conflicts of interest to disclose.

Published
2024

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

Author

Kazerooni AF, Khalili N, Liu X, Haldar D, Jiang Z, Anwar SM, Albrecht J, Adewole M, Anazodo U, Anderson H, Bagheri S, Baid U, Bergquist T, Borja AJ, Calabrese E, Chung V, Conte GM, Dako F, Eddy J, Ezhov I, Familiar A, Farahani K, Haldar S, Iglesias JE, Janas A, Johansen E, Jones BV, Kofler F, LaBella D, Lai HA, Van Leemput K, Li HB, Maleki N, McAllister AS, Meier Z, Menze B, Moawad AW, Nandolia KK, Pavaine J, Piraud M, Poussaint T, Prabhu SP, Reitman Z, Rodriguez A, Rudie JD, Shaikh IS, Shah LM, Sheth N, Shinohara RT, Tu W, Viswanathan K, Wang C, Ware JB, Wiestler B, Wiggins W, Zapaishchykova A, Aboian M, Bornhorst M, de Blank P, Deutsch M, Fouladi M, Hoffman L, Kann B, Lazow M, Mikael L, Nabavizadeh A, Packer R, Resnick A, Rood B, Vossough A, Bakas S, and Linguraru MG

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
2024

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

Author

Li HB, Conte GM, Anwar SM, Kofler F, Ezhov I, van Leemput K, Piraud M, Diaz M, Cole B, Calabrese E, Rudie J, Meissen F, Adewole M, Janas A, Kazerooni AF, LaBella D, Moawad AW, Farahani K, Eddy J, Bergquist T, Chung V, Shinohara RT, Dako F, Wiggins W, Reitman Z, Wang C, Liu X, Jiang Z, Familiar A, Johanson E, Meier Z, Davatzikos C, Freymann J, Kirby J, Bilello M, Fathallah-Shaykh HM, Wiest R, Kirschke J, Colen RR, Kotrotsou A, Lamontagne P, Marcus D, Milchenko M, Nazeri A, Weber MA, Mahajan A, Mohan S, Mongan J, Hess C, Cha S, Villanueva-Meyer J, Colak E, Crivellaro P, Jakab A, Albrecht J, Anazodo U, Aboian M, Yu T, Chung V, Bergquist T, Eddy J, Albrecht J, Baid U, Bakas S, Linguraru MG, Menze B, Iglesias JE, and Wiestler B

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.

Published
2023

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

Author

Adewole M, Rudie JD, Gbdamosi A, Toyobo O, Raymond C, Zhang D, Omidiji O, Akinola R, Suwaid MA, Emegoakor A, Ojo N, Aguh K, Kalaiwo C, Babatunde G, Ogunleye A, Gbadamosi Y, Iorpagher K, Calabrese E, Aboian M, Linguraru M, Albrecht J, Wiestler B, Kofler F, Janas A, LaBella D, Kzerooni AF, Li HB, Iglesias JE, Farahani K, Eddy J, Bergquist T, Chung V, Shinohara RT, Wiggins W, Reitman Z, Wang C, Liu X, Jiang Z, Familiar A, Van Leemput K, Bukas C, Piraud M, Conte GM, Johansson E, Meier Z, Menze BH, Baid U, Bakas S, Dako F, Fatade A, and Anazodo UC

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.

Published
2023

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

Author

LaBella D, Adewole M, Alonso-Basanta M, Altes T, Anwar SM, Baid U, Bergquist T, Bhalerao R, Chen S, Chung V, Conte GM, Dako F, Eddy J, Ezhov I, Godfrey D, Hilal F, Familiar A, Farahani K, Iglesias JE, Jiang Z, Johanson E, Kazerooni AF, Kent C, Kirkpatrick J, Kofler F, Leemput KV, Li HB, Liu X, Mahtabfar A, McBurney-Lin S, McLean R, Meier Z, Moawad AW, Mongan J, Nedelec P, Pajot M, Piraud M, Rashid A, Reitman Z, Shinohara RT, Velichko Y, Wang C, Warman P, Wiggins W, Aboian M, Albrecht J, Anazodo U, Bakas S, Flanders A, Janas A, Khanna G, Linguraru MG, Menze B, Nada A, Rauschecker AM, Rudie J, Tahon NH, Villanueva-Meyer J, Wiestler B, and Calabrese E

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

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