Fast interactive medical image segmentation with weakly supervised deep learning method.
Journal:
International journal of computer assisted radiology and surgery
Published Date:
Jul 11, 2020
Abstract
PURPOSE: To achieve accurate image segmentation, which is the first critical step in medical image analysis and interventions, using deep neural networks seems a promising approach provided sufficiently large and diverse annotated data from experts. However, annotated datasets are often limited because it is prone to variations in acquisition parameters and require high-level expert's knowledge, and manually labeling targets by tracing their contour is often laborious. Developing fast, interactive, and weakly supervised deep learning methods is thus highly desirable.
Authors
Keywords
Brachytherapy
Databases, Factual
Deep Learning
Diagnosis, Computer-Assisted
Echocardiography
Humans
Image Processing, Computer-Assisted
Male
Neural Networks, Computer
Observer Variation
Pattern Recognition, Automated
Prostate
Prostatic Neoplasms
Reproducibility of Results
Tomography, X-Ray Computed
Ultrasonography