Brain tumor segmentation using holistically nested neural networks in MRI images

• Published in: Medical physics (Lancaster) Vol. 44; no. 10; pp. 5234 - 5243
• Main Authors: Zhuge, Ying, Krauze, Andra V., Ning, Holly, Cheng, Jason Y., Arora, Barbara C., Camphausen, Kevin, Miller, Robert W.
• Format: Journal Article
• Language: English
• Published: United States 01.10.2017
• Subjects: Brain Neoplasms - diagnostic imaging; brain tumor; convolutional neural networks; Glioma - diagnostic imaging; holistically nested neural networks; Humans; Image Processing, Computer-Assisted - methods; image segmentation; Magnetic Resonance Imaging; MRI image; Neural Networks, Computer; holistically nested neural networks; brain tumor; convolutional neural networks; image segmentation; MRI image

Purpose Gliomas are rapidly progressive, neurologically devastating, largely fatal brain tumors. Magnetic resonance imaging (MRI) is a widely used technique employed in the diagnosis and management of gliomas in clinical practice. MRI is also the standard imaging modality used to delineate the brain...