Deep Learning-Based Image Segmentation on Multimodal Medical Imaging

• Published in: IEEE transactions on radiation and plasma medical sciences Vol. 3; no. 2; pp. 162 - 169
• Main Authors: Guo, Zhe, Li, Xiang, Huang, Heng, Guo, Ning, Li, Quanzheng
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Biomedical imaging; Clinical decision making; Computed tomography; Computed tomography (CT); Computer vision; convolutional neural network (CNN); Decision making; Deep learning; Empirical analysis; Feature extraction; Image analysis; Image processing; Image segmentation; Imaging techniques; Machine learning; Magnetic resonance imaging; magnetic resonance imaging (MRI); Medical imaging; Medical research; multimodal image; Neural networks; Positron emission; Positron emission tomography; positron emission tomography (PET); Soft tissues; Tomography; Tumors; Convolutional Neural Network; Computed Tomography (CT); Magnetic Resonance Imaging (MRI); Multi-modal Image; Positron Emission Tomography (PET)

Multimodality medical imaging techniques have been increasingly applied in clinical practice and research studies. Corresponding multimodal image analysis and ensemble learning schemes have seen rapid growth and bring unique value to medical applications. Motivated by the recent success of applying...