Specific features of designing a database for neuro-oncological 3D MRI images to be used in training artificial intelligence

The research was aimed at analyzing current approaches to the organization and design methodology of visualization database built on the basis of computer vision. Such approaches are necessary for effective development of diagnostic systems using artificial intelligence (AI). A training data set of high quality is a mandatory prerequisite for that. Material and methods. The paper presents the technology for designing an annotated database (SBT Dataset) that contains about 1000 clinical cases based on the archived data acquired by the Federal Neurosurgical Center, Novosibirsk, Russia including data on patients with astrocytoma, glioblastoma, meningioma, neurinoma, and patients with metastases of somatic tumors. Each case is represented by a preoperative MRI. The Results and discussion. The dataset was built (SBT Dataset) containing segmented 3D MRI images of 5 types of brain tumors with 991 verified observations. Each case is represented by four MRI sequences T1-WI, T1C (with Gd-contrast), T2-WI and T2-FLAIR with histological and histochemical postoperative confirmation. Tumors segmentation with verification of the tumor core elements boundaries and perifocal edema was approved by two certified experienced neuroradiologists. Conclusion. The database built during the research is comparable in its volume and quality (verification level) with the state-of-the-art databases. The methodological approaches proposed in this paper were focused on designing the high-quality medical computer vision systems. The database was used to create artificial intelligence systems with the “physician assistant” functions for preoperative MRI diagnostics in neurosurgery. 

1. McDonald R.J., Schwartz K.M., Eckel L.J., Diehn F.E., Hunt C.H., Bartholmai B.J., Erickson B.J., Kallmes D.F. The effects of changes in utilization and technological advancements of cross-sectional imaging on radiologist workload. Acad Radiol. 2015;22(9):1191– 1198. doi: 10.1016/j.acra.2015.05.007

2. Зрелов А.А., Нечаева А.С., Воинов Н.Е. Обновленная классификация первичных опухолей центральной нервной системы как основа персонализированного подхода к терапии пациентов. Рос. ж. персонализ. мед. 2022;2(4):6–13. doi: 10.18705/2782-3806-2022-2-4-6-13

3. Zrelov A.A., Nechaeva A.S., Voinov N.E. Updated classification of tumors of the central nervous system as the basis for individual patient therapy. Rossiyskiy zhurnal personalizirovannoy meditsiny = Russian Journal for Personalized Medicine. 2022;2(4):6–13. [In Russian]. doi: 10.18705/2782-3806-2022-2-4-6-13

4. Общий регламент защиты персональных данных (GDPR) Европейского союза. Режим доступа: https://gdpr-text.com/ru/ General Data Protection Regulation (GDPR) of the European Union. Available at: https://gdpr-text.com/en/

5. Yushkevich P.A., Piven J., Hazlett H.C., Smith R.G., Ho S., Gee J.C., Gerig G. User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neu roimage. 2006;31(3):1116–1128. doi: 10.1016/j.neuroimage.2006.01.015

6. Fedorov A., Beichel R., Kalpathy-Cramer J., Finet J., Fillion-Robin J.-C., Pujol S., Bauer C., Jennings D., Fennessy F.M., Sonka M., … Kikinis R. 3d slicer as an image computing platform for the quantitative imaging network. Magn. Reson. Imaging. 2012;30(9):1323–1341. doi: 10.1016/j.mri.2012.05.001

7. Menze B.H., Jakab A., Bauer S., Kalpathy-Cramer J., Farahani K., Kirby J., Burren Y,. Porz N., Slotboomy J., Wiest R. … van Leemput K. The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging. 2015;34(10):1993–2024. doi: 10.1109/TMI.2014.2377694

8. Bakas S., Akbari H., Sotiras A., Bilello M., Rozycki M., Kirby J.S., Freymann J.B., Farahani K., Davatzikos C. Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data: 2017;4:170117. doi: 10.1038/sdata.2017.117

9. Baid U., Ghodasara S., Mohan S., Bilello M., Calabrese E., Colak E., Farahani K., Kalpathy-Cramer J., Kitamura F.C., Pati S., Prevedello L.M. … Bakas S. The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain tumor segmentation and radiogenomic classification. Computer Vision and Pattern Recognition. 2021;2107.02314. doi: 10.48550/arXiv.2107.02314

10. Cheng Jun. Brain tumor dataset. figshare. Dataset. Available at: https://doi.org/10.6084/m9. figshare.1512427.v5

11. The Cancer Imaging Archive. The cancer imaging archive collection (TCIA). Available at: https:// www.cancerimagingarchive.net/collections/

12. Groza V., Tuchinov B., Pavlovskiy E., Amelina E., Amelin M., Golushko S., Letyagin A. Data preprocessing via multi-sequences MRI mixture to improve brain tumor segmentation. Bioinformatics and Biomedical Engineering. 2020;12108:695–704. doi: 10.1007/978-3-030-45385-5_62

13. Futreg M., Miles, A., Marcinkiewicz M., Ribalta P. Optimized U-net for brain tumor segmentation. In: Brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries. 2022;12963:15–29. doi: 10.1007/978-3-031-09002-8_2

14. Zhang W., Wu Y., Yang B., Hu S., Wu L., Dhelimd S. Overview of multi-modal brain tumor MR image segmentation. Healthcare (Basel). 2021;9(8):1051. doi: 10.3390/healthcare9081051

15. Liu Z., Tong L., Chen L., Jiang Z., Zhou F., Zhang Q., Zhang X., Jin Y., Zhou H. Deep learning based brain tumor segmentation: a survey. Complex Intell. Syst. 2022. Available at: doi.org/10.1007/s40747- 022-00815-5