Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
Open accessAbstract
Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset.
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Using transformers as a backbone encoder in computer vision is beneficial due to their capability of modeling long-range dependencies and capturing global context.
“Additionally, in computer vision, using transformers as a backbone encoder is beneficial due to their great capability of modeling long-range dependencies and capturing global context [14, 4].”
From UNETR: Transformers for 3D Medical Image Segmentation (2022) · cites this paper as [14]
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- Authors
- Bakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, A., Shinohara, R. T., Berger, C., Ha, S. M., Rozycki, M., Prastawa, M., Alberts, E., Lipkova, J., Freymann, J., Kirby, J., Bilello, M., Fathallah-Shaykh, H., Wiest, R., Kirschke, J., Wiestler, B., Colen, R., Kotrotsou, A., Lamontagne, P., Marcus, D., Milchenko, M., Nazeri, A., Weber, M.-A., Mahajan, A., Baid, U., Gerstner, E., Kwon, D., Acharya, G., Agarwal, M., Alam, M., Albiol, A., Albiol, A., Albiol, F. J., Alex, V., Allinson, N., Amorim, P. H., Amrutkar, A., Anand, G., Andermatt, S., Arbel, T., Arbelaez, P., Avery, A., Azmat, M., Pranjal, B., Bai, W., Banerjee, S., Barth, B., Batchelder, T., Batmanghelich, K., Battistella, E., Beers, A., Belyaev, M., Bendszus, M., Benson, E., Bernal, J., Bharath, H. N., Biros, G., Bisdas, S., Brown, J., Cabezas, M., Cao, S., Cardoso, J. M., Carver, E. N., Casamitjana, A., Castillo, L. S., Catà, M., Cattin, P., Cerigues, A., Chagas, V. S., Chandra, S., Chang, Y.-J., Chang, S., Chang, K., Chazalon, J., Chen, S., Chen, W., Chen, J. W., Chen, Z., Cheng, K., Choudhury, A. R., Chylla, R., Clérigues, A., Colleman, S., Colmeiro, R. G. R., Combalia, M., Costa, A., Cui, X., Dai, Z., Dai, L., Daza, L. A., Deutsch, E., Ding, C., Dong, C., Dong, S., Dudzik, W., Eaton-Rosen, Z., Egan, G., Escudero, G., Estienne, T., Everson, R., Fabrizio, J., Fan, Y., Fang, L., Feng, X., Ferrante, E., Fidon, L., Fischer, M., French, A. P., Fridman, N., Fu, H., Fuentes, D., Gao, Y., Gates, E., Gering, D., Gholami, A., Gierke, W., Glocker, B., Gong, M., González-Villá, S., Grosges, T., Guan, Y., Guo, S., Gupta, S., Han, W.-S., Han, I. S., Harmuth, K., He, H., Hernández-Sabaté, A., Herrmann, E., Himthani, N., Hsu, W., Hsu, C., Hu, X., Hu, X., Hu, Y., Hu, Y., Hua, R., Huang, T.-Y., Huang, W., Huffel, S. V., Huo, Q., Vivek, H., Iftekharuddin, K. M., Isensee, F., Islam, M., Jackson, A. S., Jambawalikar, S. R., Jesson, A., Jian, W., Jin, P., Jose, V. J. M., Jungo, A., Kainz, B., Kamnitsas, K., Kao, P.-Y., Karnawat, A., Kellermeier, T., Kermi, A., Keutzer, K., Khadir, M. T., Khened, M., Kickingereder, P., Kim, G., King, N., Knapp, H., Knecht, U., Kohli, L., Kong, D., Kong, X., Koppers, S., Kori, A., Krishnamurthi, G., Krivov, E., Kumar, P., Kushibar, K., Lachinov, D., Lambrou, T., Lee, J., Lee, C., Lee, Y., Lee, M., Lefkovits, S., Lefkovits, L., Levitt, J., Li, T., Li, H., Li, W., Li, H., Li, X., Li, Y., Li, H., Li, Z., Li, X., Li, Z., Li, X., Li, W., Lin, Z.-S., Lin, F., Lio, P., Liu, C., Liu, B., Liu, X., Liu, M., Liu, J., Liu, L., Llado, X., Lopez, M. M., Lorenzo, P. R., Lu, Z., Luo, L., Luo, Z., Ma, J., Ma, K., Mackie, T., Madabushi, A., Mahmoudi, I., Maier-Hein, K. H., Maji, P., Mammen, C., Mang, A., Manjunath, B., Marcinkiewicz, M., McDonagh, S., McKenna, S., McKinley, R., Mehl, M., Mehta, S., Mehta, R., Meier, R., Meinel, C., Merhof, D., Meyer, C., Miller, R., Mitra, S., Moiyadi, A., Molina-Garcia, D., Monteiro, M. A., Mrukwa, G., Myronenko, A., Nalepa, J., Ngo, T., Nie, D., Ning, H., Niu, C., Nuechterlein, N. K., Oermann, E., Oliveira, A., Oliveira, D. D., Oliver, A., Osman, A. F., Ou, Y.-N., Ourselin, S., Paragios, N., Park, M. S., Paschke, B., Pauloski, J. G., Pawar, K., Pawlowski, N., Pei, L., Peng, S., Pereira, S. M., Perez-Beteta, J., Perez-Garcia, V. M., Pezold, S., Pham, B., Phophalia, A., Piella, G., Pillai, G., Piraud, M., Pisov, M., Popli, A., Pound, M. P., Pourreza, R., Prasanna, P., Prkovska, V., Pridmore, T. P., Puch, S., Puybareau, É., Qian, B., Qiao, X., Rajchl, M., Rane, S., Rebsamen, M., Ren, H., Ren, X., Revanuru, K., Rezaei, M., Rippel, O., Rivera, L. C., Robert, C., Rosen, B., Rueckert, D., Safwan, M., Salem, M., Salvi, J., Sanchez, I., Sánchez, I., Santos, H. M., Sartor, E., Schellingerhout, D., Scheufele, K., Scott, M. R., Scussel, A. A., Sedlar, S., Serrano-Rubio, J. P., Shah, N. J., Shah, N., Shaikh, M., Shankar, B. U., Shboul, Z., Shen, H., Shen, D., Shen, L., Shen, H., Shenoy, V., Shi, F., Shin, H. E., Shu, H., Sima, D., Sinclair, M., Smedby, O., Snyder, J. M., Soltaninejad, M., Song, G., Soni, M., Stawiaski, J., Subramanian, S., Sun, L., Sun, R., Sun, J., Sun, K., Sun, Y., Sun, G., Sun, S., Suter, Y. R., Szilagyi, L., Talbar, S., Tao, D., Tao, D., Teng, Z., Thakur, S., Thakur, M. H., Tharakan, S., Tiwari, P., Tochon, G., Tran, T., Tsai, Y. M., Tseng, K.-L., Tuan, T. A., Turlapov, V., Tustison, N., Vakalopoulou, M., Valverde, S., Vanguri, R., Vasiliev, E., Ventura, J., Vera, L., Vercauteren, T., Verrastro, C., Vidyaratne, L., Vilaplana, V., Vivekanandan, A., Wang, G., Wang, Q., Wang, C. J., Wang, W., Wang, D., Wang, R., Wang, Y., Wang, C., Wang, G., Wen, N., Wen, X., Weninger, L., Wick, W., Wu, S., Wu, Q., Wu, Y., Xia, Y., Xu, Y., Xu, X., Xu, P., Yang, T.-L., Yang, X., Yang, H.-Y., Yang, J., Yang, H., Yang, G., Yao, H., Ye, X., Yin, C., Young-Moxon, B., Yu, J., Yue, X., Zhang, S., Zhang, A., Zhang, K., Zhang, X., Zhang, L., Zhang, X., Zhang, Y., Zhang, L., Zhang, J., Zhang, X., Zhang, T., Zhao, S., Zhao, Y., Zhao, X., Zhao, L., Zheng, Y., Zhong, L., Zhou, C., Zhou, X., Zhou, F., Zhu, H., Zhu, J., Zhuge, Y., Zong, W., Kalpathy-Cramer, J., Farahani, K., Davatzikos, C., Leemput, K. V., & Menze, B.
- Affiliations
- Centre de Vision Numérique (FR)
- Institut Gustave Roussy (FR)
- ORCID
- Bakas, Spyridon
- Reyes, Mauricio
- Crimi, Alessandro
- Bilello, Michel
- Wiest, Roland
- Wiestler, Benedikt
- Baid, Ujjwal
- Gerstner, Elizabeth
- Kwon, Dongjin
- Albiol, Francisco J
- Allinson, Nigel
- Batmanghelich, Kayhan
- Beers, Andrew
- Bisdas, Sotirios
- Brown, James
- Cabezas, Mariano
- Carver, Eric N
- Casamitjana, Adrià
- Cattin, Philippe
- Chang, Ken
- Chazalon, Joseph
- Ding, Changxing
- Eaton-Rosen, Zach
- Egan, Gary
- Fidon, Lucas
- French, Andrew P
- Gong, Mingming
- Grosges, T
- Hernández-Sabaté, Aura
- Huang, Teng-Yi
- Huang, Weilin
- Islam, Mobarakol
- Kamnitsas, Konstantinos
- Lambrou, Tryphon
- Lio, Pietro
- Maier-Hein, Klaus H
- Mang, Andreas
- McDonagh, S
- McKinley, Richard
- Meyer, Craig
- Piella, Gemma
- Piraud, Marie
- Puybareau, Élodie
- Rane, Swapnil
- Robert, Charlotte
- Rosen, Bruce
- Sartor, Emmett
- Shen, Haipeng
- Shen, Dinggang
- Shen, Linlin
- Sun, Roger
- Thakur, Siddhesh
- Tochon, Guillaume
- Vanguri, Rami
- Vercauteren, Tom
- Wang, Chunliang
- Yang, Guang
- Davatzikos, Christos
- Leemput, Koen van
- Menze, Bjoern
- Venue
- Apollo - University of Cambridge Repository
- Publisher
- Apollo - University of Cambridge Repository
- Published
- 2018-11-05
- ISSN
- 2331-8422 (ISSN-L)
- OpenAlex
- W2900298334
- Cited by
- 1,441
- References
- 3
- Citations by year
- 2026: 17 · 2025: 129 · 2024: 237 · 2023: 217 · 2022: 199 · 2021: 304 · 2020: 239 · 2019: 96 · 2018: 1
- Type
- posted content
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- arXiv (Cornell University) (submittedVersion)
- Spiral (Imperial College London) (submittedVersion)
- UPCommons institutional repository (Universitat Politècnica de Catalunya) (submittedVersion, public-domain)
- Apollo (University of Cambridge) (submittedVersion)
- HAL (submittedVersion)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, other-oa)
- Research Explorer (The University of Manchester) (submittedVersion)
- arXiv (Cornell University)
- Apollo
- Funders
- National Science Foundation
- University of Pennsylvania
- Emory University
- Case Western Reserve University
- Thomas Jefferson University
- Inselspital, Universitätsspital Bern
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (169607)
- Instituto Tecnológico de Costa Rica
- Debreceni Egyetem
- National Institutes of Health (KFS-3979-08-2016, R01‐grant, CA189523)
- University of Texas MD Anderson Cancer Center
- National Cancer Institute (KFS-3979-08-2016, U24CA189523)
- National Institute of Neurological Disorders and Stroke (R01NS042645, KFS-3979-08-2016)
- Topics
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Genetics › Glioma Diagnosis and Treatment
- Keywords
- brain tumor segmentation, glioma, multiparametric MRI, tumor progression assessment, overall survival prediction, BraTS, longitudinal MRI, machine learning algorithm comparison, gross total resection, tumor heterogeneity
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