Siyue LI1, Shutian ZHAO1, Yongcheng YAO1, and Weitian CHEN1
1AI in Radiology Laboratory, Department of Imaging and Interventioanl Radiology, The Chinese University of Hong Kong, Hongkong, Hong Kong
1AI in Radiology Laboratory, Department of Imaging and Interventioanl Radiology, The Chinese University of Hong Kong, Hongkong, Hong Kong
We investigated the dropout-based
Bayesian semi-supervised network for meniscus segmentation using MRI images. The inclusion of the unannotated data with
uncertainty estimation has the potential to improve the meniscus segmentation.
Figure 4. Visualization of
segmentation results by different methods and corresponding uncertainty map. Green, red and
yellow contour illustrates the ground truth, predictions and overlaid regions
respectively.
Figure 3. Quantitative evaluations
of different deep learning segmentation models.