Automated Brain Metastases Detection Framework for T1-Weighted Contrast-Enhanced 3D MRI

• Published in: IEEE journal of biomedical and health informatics Vol. 24; no. 10; pp. 2883 - 2893
• Main Authors: Dikici, Engin, Ryu, John L., Demirer, Mutlu, Bigelow, Matthew, White, Richard D., Slone, Wayne, Erdal, Barbaros Selnur, Prevedello, Luciano M.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; Brain; Brain - diagnostic imaging; Brain cancer; brain metastases; Brain Neoplasms - diagnostic imaging; Brain Neoplasms - secondary; Comparative analysis; computer-aided detection; convolutional neural networks; Datasets; Deep Learning; Elastic deformation; Gadolinium; Humans; Image Interpretation, Computer-Assisted - methods; Imaging, Three-Dimensional - methods; Informatics; Lesions; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; medical image analysis; Metastases; Metastasis; Neural networks; Neural Networks, Computer; Neuroimaging; scale-space representations; Sensitivity; Sensitivity and Specificity; Strain; Three-dimensional displays; Training

Brain Metastases (BM) complicate 20-40% of cancer cases. BM lesions can present as punctate (1 mm) foci, requiring high-precision Magnetic Resonance Imaging (MRI) in order to prevent inadequate or delayed BM treatment. However, BM lesion detection remains challenging partly due to their structural s...