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  • Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-LGG collection (BraTS-TCGA-LGG)

Summary

This data container describes both computer-aided and manually-corrected segmentation labels for the pre-operative multi-institutional scans of The Cancer Genome Atlas (TCGA) Low Grade Glioma (LGG) collection, publicly available in The Cancer Imaging Archive (TCIA), coupled with a rich panel of radiomic features along with their corresponding skull-stripped and co-registered multimodal (i.e. T1, T1-Gd, T2, T2-FLAIR) magnetic resonance imaging (MRI) volumes in NIfTI format. Pre-operative multimodal MRI scans were identified in the TCGA-LGG collection via radiological assessment. These scans were initially skull-stripped and co-registered, before their tumor segmentation labels were produced by an automated hybrid generative-discriminative method, ranked first during the International multimodal BRAin Tumor Segmentation challenge (BRATS 2015). These segmentation labels were revised and any label misclassifications were manually corrected by an expert board-certified neuroradiologist. The final labels were used to extract a rich panel of imaging features, including intensity, volumetric, morphologic, histogram-based and textural parameters, as well as spatial information and diffusion properties extracted from glioma growth models. The generated computer-aided and manually-revised labels enable quantitative computational and clinical studies without the need to repeat manual annotations whilst allowing for comparison across studies. They can also serve as a set of manually-annotated gold standard labels for performance evaluation in computational challenges. The provided panel of radiomic features may facilitate research integrative of the molecular characterization offered by TCGA, and hence allow associations with molecular markers, clinical outcomes, treatment responses and other endpoints, by researchers without sufficient computational background to extract such features.


Data Access

Please contact the helpdesk to request access to the Test arm of the NIfTI data files (43 Participants, 366 MB).


Data TypeDownload all or Query/FilterLicense
Processed images with segmentations and radiomic features Training set (zip, 536 MB, 65 subjects, 387 images)
BRATS 2018 Test Data Set (zip, 366 MB, 43 subjects, 255 images)

Please contact the helpdesk to request access to these files.

TCIA Restricted

Collections Used in this Third Party Analysis


Below is a list of the Collections used in these analyses:

Source Data TypeDownload all or Query/FilterLicense
Corresponding Original Images from TCGA-LGG (DICOM, 8.5 GB, 108 Subjects) 
Please (for DICOM format files) request both Collections TCGA-LGG and BraTS-TCGA-LGG in your Agreement.

Detailed Description

Data resulting from this experiment is available in the following formats:

  • (source data in DICOM image format)
  • Processed images with segmentations (NIFTI) and radiomic features (CSV):
    • TrainingProcessed images with segmentations and radiomic features - 65 subjects (NIfTI, zip,  536 MB)
    • BraTS Test Data Set - 43 subjects (NIfTI, zip,  366 MB)

Please contact help@cancerimagingarchive.net  with any questions regarding usage.

Citations & Data Usage Policy 

Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution should include references to the following citations:

Data Citation

Bakas S, Akbari H, Sotiras A, Bilello M, Rozycki M, Kirby J, Freymann J, Farahani K, Davatzikos C. (2017) Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-LGG collection [Data Set]. The Cancer Imaging Archive. DOI:  10.7937/K9/TCIA.2017.GJQ7R0EF

Publication Citation

Bakas S, Akbari H, Sotiras A, Bilello M, Rozycki M, Kirby J, Freymann J, Farahani K, Davatzikos C. (2017) Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. Nature Scientific Data, 4:170117 DOI: 10.1038/sdata.2017.117

TCIA Citation

Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. In Journal of Digital Imaging (Vol. 26, Issue 6, pp. 1045–1057). Springer Science and Business Media LLC. https://doi.org/10.1007/s10278-013-9622-7 PMCID: PMC3824915

Other Publications Using This Data

TCIA maintains a list of publications which leverage TCIA data. If you have a publications you'd like to add please contact TCIA's Helpdesk.

  1. Abler, D., Andrearczyk, V., Oreiller, V., Garcia, J. B., Vuong, D., Tanadini-Lang, S., . . . Depeursinge, A. (2022). Comparison of MR Preprocessing Strategies and Sequences for Radiomics-Based MGMT Prediction. In A. Crimi & S. Bakas (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Vol. 12963, pp. 367-380): Springer.
  2. Aboussaleh, I., Riffi, J., Mahraz, A. M., & Tairi, H. (2021). Brain Tumor Segmentation Based on Deep Learning's Feature Representation. Journal of Imaging, 7(12), 269. doi:https://doi.org/10.3390/jimaging7120269
  3. Abraham, N., & Khan, N. M. (2020). Multimodal Segmentation with MGF-Net and the Focal Tversky Loss Function. In A. Crimi & S. Bakas (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Vol. 11993, pp. 191-198). Shenzhen, China: Springer.
  4. Agravat, R. R., & Raval, M. S. (2020). Brain Tumor Segmentation and Survival Prediction. In A. Crimi & S. Bakas (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Vol. 11992, pp. 338-348). Shenzhen, China: Springer.
  5. Agravat, R. R., & Raval, M. S. (2021). 3D Semantic Segmentation of Brain Tumor for Overall Survival Prediction. In A. Crimi & S. Bakas (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Vol. 12659, pp. 215-227). Lima, Peru: Springer.
  6. Ahmad, P., Jin, H., Qamar, S., Zheng, R., & Saeed, A. (2021). RD2A: densely connected residual networks using ASPP for brain tumor segmentation. Multimedia Tools and Applications, 80(18), 27069-27094. doi:10.1007/s11042-021-10915-y
  7. Ahmad, P., Qamar, S., Hashemi, S. R., & Shen, L. (2020). Hybrid Labels for Brain Tumor Segmentation. In A. Crimi & S. Bakas (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Vol. 11993, pp. 158-166). Shenzhen, China: Springer.
  8. Ahmad, P., Qamar, S., Shen, L., Rizvi, S. Q. A., Ali, A., & Chetty, G. (2022). MS UNet: Multi-scale 3D UNet for Brain Tumor Segmentation. In A. Crimi & S. Bakas (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Vol. 12963, pp. 30-41): Springer.
  9. Ahmad, P., Qamar, S., Shen, L., & Saeed, A. (2021). Context Aware 3D UNet for Brain Tumor Segmentation. In A. Crimi & S. Bakas (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (pp. 207-218). Lima, Peru: Springer.
  10. Akbar, A. S., Fatichah, C., & Suciati, N. (2021). Modified MobileNet for Patient Survival Prediction. In A. Crimi & S. Bakas (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Vol. 12659, pp. 374-387). Lima, Peru: Springer.
  11. Akbar, A. S., Fatichah, C., & Suciati, N. (2022). Unet3D with Multiple Atrous Convolutions Attention Block for Brain Tumor Segmentation. Paper presented at the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Virtual Event. 
  12. Ali, M. B., Bai, X., Gu, I. Y.-H., Berger, M. S., & Jakola, A. S. (2022). A Feasibility Study on Deep Learning Based Brain Tumor Segmentation Using 2D Ellipse Box Areas. Sensors, 22(14), 5292. doi:https://doi.org/10.3390/s22145292
  13. Ali, M. B., Gu, I. Y., Lidemar, A., Berger, M. S., Widhalm, G., & Jakola, A. S. (2022). Prediction of glioma-subtypes: comparison of performance on a DL classifier using bounding box areas versus annotated tumors. BMC Biomed Eng, 4(1), 4. doi:https://doi.org/10.1186/s42490-022-00061-3
  14. Ali, M. J., Akram, M. T., Saleem, H., Raza, B., & Shahid, A. R. (2021). Glioma Segmentation Using Ensemble of 2D/3D U-Nets and Survival Prediction Using Multiple Features Fusion. In A. Crimi & S. Bakas (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Vol. 12659, pp. 189-199). Lima, Peru: Springer.
  15. Aliotta, E., Dutta, S. W., Feng, X., Tustison, N. J., Batchala, P. P., Schiff, D., . . . Patel, S. H. (2020). Automated apparent diffusion coefficient analysis for genotype prediction in lower grade glioma: association with the T2-FLAIR mismatch sign. J Neurooncol, 149(2), 325-335. doi:https://doi.org/10.1007/s11060-020-03611-8
  16. Amian, M., & Soltaninejad, M. (2020). Multi-resolution 3D CNN for MRI Brain Tumor Segmentation and Survival Prediction. In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (pp. 221-230).
  17. Ammari, S., Sallé de Chou, R., Balleyguier, C., Chouzenoux, E., Touat, M., Quillent, A., . . . Assi, T. (2021). A Predictive Clinical-Radiomics Nomogram for Survival Prediction of Glioblastoma Using MRI. Diagnostics, 11(11), 2043. doi:https://doi.org/10.3390/diagnostics11112043
  18. Anand, V. K., Grampurohit, S., Aurangabadkar, P., Kori, A., Khened, M., Bhat, R. S., & Krishnamurthi, G. (2021). Brain Tumor Segmentation and Survival Prediction Using Automatic Hard Mining in 3D CNN Architecture. In A. Crimi & S. Bakas (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Vol. 12659, pp. 310-319). Lima, Peru: Springer.
  19. Antonelli, M., Reinke, A., Bakas, S., Farahani, K., Kopp-Schneider, A., Landman, B. A., . . . Cardoso, M. J. (2022). The Medical Segmentation Decathlon. Nat Commun, 13(1), 4128. doi:10.1038/s41467-022-30695-9
  20. Arora, A., Jayal, A., Gupta, M., Mittal, P., & Satapathy, S. C. (2021). Brain Tumor Segmentation of MRI Images Using Processed Image Driven U-Net Architecture. Computers, 10(11), 139. doi:https://doi.org/10.3390/computers10110139
  21. Ashtari, P., Maes, F., & Van Huffel, S. (2021). Low-Rank Convolutional Networks for Brain Tumor Segmentation. In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (pp. 470-480).
  22. Astaraki, M., Wang, C., Carrizo, G., Toma-Dasu, I., & Smedby, Ö. (2020). Multimodal Brain Tumor Segmentation with Normal Appearance Autoencoder. Paper presented at the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Shenzhen, China. 
  23. Awasthi, N., Pardasani, R., & Gupta, S. (2021). Multi-threshold Attention U-Net (MTAU) Based Model for Multimodal Brain Tumor Segmentation in MRI Scans. In A. Crimi & S. Bakas (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Vol. 12659, pp. 168-178). Lima, Peru: Springer.
  24. Baid, U., Shah, N. A., & Talbar, S. (2020). Brain Tumor Segmentation with Cascaded Deep Convolutional Neural Network. In A. Crimi & S. Bakas (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Vol. 11993, pp. 90-98). Shenzhen, China: Springer.
  25. Bal, A., Banerjee, M., Chaki, R., & Sharma, P. (2021). An efficient brain tumor image classifier by combining multi-pathway cascaded deep neural network and handcrafted features in MR images. Med Biol Eng Comput, 59(7-8), 1495-1527. doi:10.1007/s11517-021-02370-6
  26. Ballestar, L. M., & Vilaplana, V. (2021). MRI Brain Tumor Segmentation and Uncertainty Estimation Using 3D-UNet Architectures. In A. Crimi & S. Bakas (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Vol. 12658, pp. 376-390). Lima, Peru: Springer.
  27. Banerjee, S., Arora, H. S., & Mitra, S. (2020). Ensemble of CNNs for Segmentation of Glioma Sub-regions with Survival Prediction. In A. Crimi & S. Bakas (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Vol. 11993, pp. 37-49). Shenzhen, China: Springer.
  28. Bangalore Yogananda, C. G., Shah, B. R., Vejdani-Jahromi, M., Nalawade, S. S., Murugesan, G. K., Yu, F. F., . . . Maldjian, J. A. (2020). A Fully Automated Deep Learning Network for Brain Tumor Segmentation. Tomography, 6(2), 186-193. doi:https://doi.org/10.18383/j.tom.2019.00026
  29. Bangalore Yogananda, C. G., Wagner, B., Nalawade, S. S., Murugesan, G. K., Pinho, M. C., Fei, B., . . . Maldjian, J. A. (2020). Fully Automated Brain Tumor Segmentation and Survival Prediction of Gliomas Using Deep Learning and MRI. In A. Crimi & S. Bakas (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Vol. 11993, pp. 99-112). Shenzhen, China: Springer.
  30. Ben Ahmed, K., Hall, L. O., Goldgof, D. B., & Gatenby, R. (2022). Ensembles of Convolutional Neural Networks for Survival Time Estimation of High-Grade Glioma Patients from Multimodal MRI. Diagnostics, 12(2), 345. doi:https://doi.org/10.3390/diagnostics12020345
  31. Ben Naceur, M., Akil, M., Saouli, R., & Kachouri, R. (2020). Deep Convolutional Neural Networks for Brain Tumor Segmentation: Boosting Performance Using Deep Transfer Learning: Preliminary Results. In A. Crimi & S. Bakas (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Vol. 11993, pp. 303-315). Shenzhen, China: Springer.
  32. Bertels, J., Robben, D., Vandermeulen, D., & Suetens, P. (2020). Optimization with Soft Dice Can Lead to a Volumetric Bias. In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (pp. 89-97).
  33. Bhadani, S., Mitra, S., & Banerjee, S. (2020). Fuzzy volumetric delineation of brain tumor and survival prediction. Soft Computing, 24(17), 13115-13134. doi:10.1007/s00500-020-04728-8
  34. Bhalerao, M., & Thakur, S. (2020). Brain Tumor Segmentation Based on 3D Residual U-Net. In A. Crimi & S. Bakas (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Vol. 11993, pp. 218-225). Shenzhen, China: Springer.
  35. Biratu, E. S., Schwenker, F., Debelee, T. G., Kebede, S. R., Negera, W. G., & Molla, H. T. (2021). Enhanced Region Growing for Brain Tumor MR Image Segmentation. J Imaging, 7(2), 22. doi:https://doi.org/10.3390/jimaging7020022
  36. Bommineni, V. L. (2021). PieceNet: A Redundant UNet Ensemble. In A. Crimi & S. Bakas (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Vol. 12659, pp. 331-341). Lima, Peru: Springer.
  37. Bouget, D., Eijgelaar, R. S., Pedersen, A., Kommers, I., Ardon, H., Barkhof, F., . . . Solheim, O. (2021). Glioblastoma Surgery Imaging–Reporting and Data System: Validation and Performance of the Automated Segmentation Task. Cancers, 13(18), 4674. doi:https://doi.org/10.3390/cancers13184674
  38. Boutry, N., Chazalon, J., Puybareau, E., Tochon, G., Talbot, H., & Géraud, T. (2020). Using Separated Inputs for Multimodal Brain Tumor Segmentation with 3D U-Net-like Architectures. In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (pp. 187-199).
  39. Buatois, T., Puybareau, É., Tochon, G., & Chazalon, J. (2020). Two Stages CNN-Based Segmentation of Gliomas, Uncertainty Quantification and Prediction of Overall Patient Survival. In A. Crimi & S. Bakas (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Vol. 11993, pp. 167-178). Shenzhen, China: Springer.
  40. Bukhari, S. T., & Mohy-ud-Din, H. (2022). E1D3 U-Net for Brain Tumor Segmentation: Submission to the RSNA-ASNR-MICCAI BraTS 2021 challenge. In A. Crimi & S. Bakas (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (pp. 276-288).
  41. Carmo, D., Rittner, L., & Lotufo, R. (2021). MultiATTUNet: Brain Tumor Segmentation and Survival Multitasking. In A. Crimi & S. Bakas (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Vol. 12658, pp. 424-434). Lima, Peru: Springer.
  42. Carré, A., Battistella, E., Niyoteka, S., Sun, R., Deutsch, E., & Robert, C. (2022). AutoComBat: a generic method for harmonizing MRI-based radiomic features. Sci Rep, 12(1), 12762. doi:10.1038/s41598-022-16609-1
  43. Carré, A., Deutsch, E., & Robert, C. (2022). Automatic Brain Tumor Segmentation with a Bridge-Unet Deeply Supervised Enhanced with Downsampling Pooling Combination, Atrous Spatial Pyramid Pooling, Squeeze-and-Excitation and EvoNorm. In A. Crimi & S. Bakas (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Vol. 12963, pp. 253-266): Springer.
  44. Chan, H.-W., Weng, Y.-T., & Huang, T.-Y. (2020). Automatic Classification of Brain Tumor Types with the MRI Scans and Histopathology Images. Paper presented at the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries Shenzhen, China,. 
  45. Chato, L., Kachroo, P., & Latifi, S. (2021). An Automatic Overall Survival Time Prediction System for Glioma Brain Tumor Patients Based on Volumetric and Shape Features. In A. Crimi & S. Bakas (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Vol. 12659, pp. 352-365). Lima, Peru: Springer.
  46. Chen, D. T., Chen, A. T., & Wang, H. (2022). Simple and Fast Convolutional Neural Network Applied to Median Cross Sections for Predicting the Presence of MGMT Promoter Methylation in FLAIR MRI Scans. In A. Crimi & S. Bakas (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Vol. 12962, pp. 227-238). Virtual Event: Springer.
  47. Chen, M., Wu, Y., & Wu, J. (2020). Aggregating Multi-scale Prediction Based on 3D U-Net in Brain Tumor Segmentation. In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (pp. 142-152).
  48. Cheng, K., Hu, C., Yin, P., Su, Q., Zhou, G., Wu, X., . . . Yang, W. (2021). Glioma Sub-region Segmentation on Multi-parameter MRI with Label Dropout. In A. Crimi & S. Bakas (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Vol. 12659, pp. 420-430). Lima, Peru: Springer.
  49. Cheng, X., Jiang, Z., Sun, Q., & Zhang, J. (2020). Memory-Efficient Cascade 3D U-Net for Brain Tumor Segmentation. In A. Crimi & S. Bakas (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Vol. 11992, pp. 242-253). Shenzhen, China: Springer.
  50. Cho, J., & Park, J. (2023). Multi-modal Transformer for Brain Tumor Segmentation. In S. Bakas, A. Crimi, U. Baid, S. Malec, M. Pytlarz, B. Baheti, M. Zenk, & R. Dorent (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Vol. 13769, pp. 138-148). Singapore: Springer.
  51. Choi, Y., Al-masni, M. A., & Kim, D.-H. (2022). 3D CMM-Net with Deeper Encoder for Semantic Segmentation of Brain Tumors in BraTS2021 Challenge. In A. Crimi & S. Bakas (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Vol. 12962, pp. 333-343): Springer.
  52. Cirillo, M. D., Abramian, D., & Eklund, A. (2021). Vox2Vox: 3D-GAN for Brain Tumour Segmentation. In A. Crimi & S. Bakas (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Vol. 12658, pp. 274-284). Lima, Peru: SPringer.
  53. Colman, J., Zhang, L., Duan, W., & Ye, X. (2021). DR-Unet104 for Multimodal MRI Brain Tumor Segmentation. In A. Crimi & S. Bakas (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Vol. 12659, pp. 410-419). Lima, Peru: Springer.
  54. Crimi, A., & Bakas, S. (2020). Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
    5th International Workshop, BrainLes 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Revised Selected Papers, Part I (A. Crimi & S. Bakas Eds.  Vol. 11992). Shenzhen, China: Springer International Publishing.
  55. Crimi, A., & Bakas, S. (2021). Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
    6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers, Part II (A. Crimi & S. Bakas Eds.  Vol. 12659). Lima, Peru: Springer.
  56. Dai, C., Wang, S., Raynaud, H., Mo, Y., Angelini, E., Guo, Y., & Bai, W. (2021). Self-training for Brain Tumour Segmentation with Uncertainty Estimation and Biophysics-Guided Survival Prediction. In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (pp. 514-523).
  57. Dai, Z., Wen, N., & Carver, E. (2022). Brain Tumor Segmentation Using Non-local Mask R-CNN and Single Model Ensemble. Paper presented at the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Virtual Event. 
  58. Daza, L., Gómez, C., & Arbeláez, P. (2021). Cerberus: A Multi-headed Network for Brain Tumor Segmentation. In A. Crimi & S. Bakas (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Vol. 12659, pp. 342-351). Lima, Peru: Springer.
  59. Demoustier, M., Khemir, I., Nguyen, Q. D., Martin-Gaffé, L., & Boutry, N. (2022). Residual 3D U-Net with Localization for Brain Tumor Segmentation. Paper presented at the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Virtual Event. 
  60. Do, T.-B.-T., Trinh, D.-L., Tran, M.-T., Lee, G.-S., Kim, S.-H., & Yang, H.-J. (2022). Deep Learning Based Ensemble Approach for 3D MRI Brain Tumor Segmentation. In A. Crimi & S. Bakas (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (pp. 210-221).
  61. Druzhinina, P., Kondrateva, E., Bozhenko, A., Yarkin, V., Sharaev, M., & Kurmukov, A. (2022). BRATS2021: Exploring Each Sequence in Multi-modal Input for Baseline U-net Performance. In A. Crimi & S. Bakas (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Vol. 12962, pp. 194-203): Springer.
  62. Duncan, C., Roxas, F., Jani, N., Maksimovic, J., Bramlet, M., Sutton, B., & Koyejo, S. (2021). Some New Tricks for Deep Glioma Segmentation. In A. Crimi & S. Bakas (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Vol. 12659, pp. 320-330). Lima, Peru: Springer.
  63. Eder, M., Moser, E., Holzinger, A., Jean-Quartier, C., & Jeanquartier, F. (2022). Interpretable Machine Learning with Brain Image and Survival Data. BioMedInformatics, 2(3), 492-510. doi:https://doi.org/10.3390/biomedinformatics2030031
  64. Ellis, D. G., & Aizenberg, M. R. (2021). Trialing U-Net Training Modifications for Segmenting Gliomas Using Open Source Deep Learning Framework. In A. Crimi & S. Bakas (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Vol. 12659, pp. 40-49). Lima, Peru: Springer.
  65. Elmezain, M., Mahmoud, A., Mosa, D. T., & Said, W. (2022). Brain Tumor Segmentation Using Deep Capsule Network and Latent-Dynamic Conditional Random Fields. J Imaging, 8(7), 190. doi:https://doi.org/10.3390/jimaging8070190
  66. Emchinov, A. (2022). A Deep Learning Approach to Glioblastoma Radiogenomic Classification Using Brain MRI. In A. Crimi & S. Bakas (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Vol. 12963, pp. 345-356). Virtual Event: Springer Cham.
  67. Estienne, T., Lerousseau, M., Vakalopoulou, M., Alvarez Andres, E., Battistella, E., Carre, A., . . . Deutsch, E. (2020). Deep Learning-Based Concurrent Brain Registration and Tumor Segmentation. Front Comput Neurosci, 14, 17. doi:10.3389/fncom.2020.00017
  68. Faghani, S., Khosravi, B., Moassefi, M., Conte, G. M., & Erickson, B. J. (2023). A Comparison of Three Different Deep Learning-Based Models to Predict the MGMT Promoter Methylation Status in Glioblastoma Using Brain MRI. J Digit Imaging. doi:https://doi.org/10.1007/s10278-022-00757-x
  69. Farzana, W., Temtam, A. G., Shboul, Z. A., Rahman, M. M., Sadique, M. S., & Iftekharuddin, K. M. (2022). Radiogenomic Prediction of MGMT Using Deep Learning with Bayesian Optimized Hyperparameters. In A. Crimi & S. Bakas (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Vol. 12963, pp. 357-366): Springer.
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