Computational Research

graphic illustration of computer networks

At the intersection of technology and medical imaging, the computational research facet of the UCLA Brain Tumor Imaging Laboratory (BTIL) harnesses the power of data analysis and artificial intelligence. Through advanced computational methods, we delve into complex datasets, extracting meaningful patterns and information that guide our understanding of brain tumor dynamics. By developing sophisticated algorithms, predictive models, and data-driven insights, our computational research amplifies the capabilities of medical imaging, allowing us to uncover nuances in brain tumor behavior that aid in diagnosis, treatment, and research.

Deep Learning and Computer Vision in Medical Imaging

BTIL's expertise lies in leveraging advanced artificial intelligence (AI) technologies to enhance the analysis and interpretation of medical images. Our computational research branch leverages the capabilities of Deep Learning and Computer Vision techniques to unlock meaningful insights from complex medical imaging data.

By training these algorithms on vast datasets, BTIL aims to create automated tools that can assist in various aspects of medical imaging research:

  • Image Segmentation: Delineate regions of interest, enabling accurate identification of tumor boundaries and associated structures.
  • Feature Extraction: Extract intricate features, aiding in the identification of subtle patterns and disease markers.
  • Diagnostic Assistance: Detect anomalies, potentially leading to early and more accurate disease detection.
  • Generative Models: Synthetic data that simulates real medical images, aiding in the augmentation of limited datasets.

These technologies hold the potential to expedite analysis, improve accuracy, and unlock new layers of information within medical images, thereby advancing our understanding of brain tumors and paving the way for more effective diagnostic and treatment approaches.

Selected References

  1.  Synthesizing MR Image Contrast Enhancement Using 3D High-Resolution ConvNets.

    Chen C, Raymond C, Speier W, Jin X, Cloughesy TF, Enzmann D, Ellingson BM, Arnold CW.
    IEEE Trans Biomed Eng. 2023 Feb;70(2):401-412. doi: 10.1109/TBME.2022.3192309. Epub 2023 Jan 19.

  2. Federated learning enables big data for rare cancer boundary detection.

    Pati S, Baid U, Edwards B, Sheller M, ...., Ellingson BM, Cloughesy TF, Raymond C, Oughourlian T, Hagiwara A, Wang C, To MS, Bhardwaj S, Chong C, Agzarian M, Falcão AX, Martins SB, Teixeira BCA, Sprenger F, Menotti D, Lucio DR, LaMontagne P, Marcus D, Wiestler B, Kofler F, Ezhov I, Metz M, Jain R, Lee M, Lui YW, McKinley R, Slotboom J, Radojewski P, Meier R, Wiest R, Murcia D, Fu E, Haas R, T…
    Nat Commun. 2022 Dec 5;13(1):7346. doi: 10.1038/s41467-022-33407-5.