Detecting Anatomical Landmarks From Limited Medical Imaging Data Using Two-Stage Task-Oriented Deep Neural Networks

• Published in: IEEE transactions on image processing Vol. 26; no. 10; pp. 4753 - 4764
• Main Authors: Zhang, Jun, Liu, Mingxia, Shen, Dinggang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Anatomical landmark detection; Artificial neural networks; Biological neural networks; Biomedical imaging; Brain; Computed tomography; Computer architecture; deep convolutional neural networks; Landmarks; limited medical imaging data; Machine learning; Magnetic resonance imaging; Medical imaging; Neural networks; Patches (structures); Prostate; Real time; Target recognition; task-oriented; Testing; Three-dimensional displays; Training; Training data

One of the major challenges in anatomical landmark detection, based on deep neural networks, is the limited availability of medical imaging data for network learning. To address this problem, we present a two-stage task-oriented deep learning method to detect large-scale anatomical landmarks simulta...