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1. 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

2. A multi-institutional meningioma MRI dataset for automated multi-sequence image segmentation.

Author

LaBella, Dominic, Khanna, Omaditya, McBurney-Lin, Shan, Mclean, Ryan, Nedelec, Pierre, Rashid, Arif, Tahon, Nourel, Altes, Talissa, Baid, Ujjwal, Bhalerao, Radhika, Dhemesh, Yaseen, Floyd, Scott, Godfrey, Devon, Hilal, Fathi, Janas, Anastasia, Kazerooni, Anahita, Kent, Collin, Kirkpatrick, John, Kofler, Florian, Leu, Kevin, Maleki, Nazanin, Menze, Bjoern, Pajot, Maxence, Reitman, Zachary, Rudie, Jeffrey, Saluja, Rachit, Velichko, Yury, Wang, Chunhao, Warman, Pranav, Sollmann, Nico, Diffley, David, Nandolia, Khanak, Warren, Daniel, Hussain, Ali, Fehringer, John, Bronstein, Yulia, Deptula, Lisa, Stein, Evan, Taherzadeh, Mahsa, Portela de Oliveira, Eduardo, Haughey, Aoife, Kontzialis, Marinos, Saba, Luca, Turner, Benjamin, Brüßeler, Melanie, Ansari, Shehbaz, Gkampenis, Athanasios, Weiss, David, Mansour, Aya, Shawali, Islam, Yordanov, Nikolay, Stein, Joel, Hourani, Roula, Moshebah, Mohammed, Abouelatta, Ahmed, Rizvi, Tanvir, Willms, Klara, Martin, Dann, Okar, Abdullah, DAnna, Gennaro, Taha, Ahmed, Sharifi, Yasaman, Faghani, Shahriar, Kite, Dominic, Pinho, Marco, Haider, Muhammad, Alonso-Basanta, Michelle, Rauschecker, Andreas, Nada, Ayman, Aboian, Mariam, Flanders, Adam, Bakas, Spyridon, Calabrese, Evan, and Villanueva-Meyer, Javier

Subjects
Meningioma, Humans, Magnetic Resonance Imaging, Meningeal Neoplasms, Male, Female, Image Processing, Computer-Assisted, Middle Aged, Aged
Abstract

Meningiomas are the most common primary intracranial tumors and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on brain MRI for diagnosis, treatment planning, and longitudinal treatment monitoring. However, automated, objective, and quantitative tools for non-invasive assessment of meningiomas on multi-sequence MR images are not available. Here we present the BraTS Pre-operative Meningioma Dataset, as the largest multi-institutional expert annotated multilabel meningioma multi-sequence MR image dataset to date. This dataset includes 1,141 multi-sequence MR images from six sites, each with four structural MRI sequences (T2-, T2/FLAIR-, pre-contrast T1-, and post-contrast T1-weighted) accompanied by expert manually refined segmentations of three distinct meningioma sub-compartments: enhancing tumor, non-enhancing tumor, and surrounding non-enhancing T2/FLAIR hyperintensity. Basic demographic data are provided including age at time of initial imaging, sex, and CNS WHO grade. The goal of releasing this dataset is to facilitate the development of automated computational methods for meningioma segmentation and expedite their incorporation into clinical practice, ultimately targeting improvement in the care of meningioma patients.

Published
2024

3. 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 - 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

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