Segmentation and Uncertainty Measures of Cardiac Tissues on Optical Coherence Tomography Via Convolutional Neural Networks

This video program is a part of the Premium package:

Segmentation and Uncertainty Measures of Cardiac Tissues on Optical Coherence Tomography Via Convolutional Neural Networks


  • IEEE MemberUS $11.00
  • Society MemberUS $0.00
  • IEEE Student MemberUS $11.00
  • Non-IEEE MemberUS $15.00
Purchase

Segmentation and Uncertainty Measures of Cardiac Tissues on Optical Coherence Tomography Via Convolutional Neural Networks

0 views
  • Share
Create Account or Sign In to post comments
Segmentation of human cardiac tissue has a great potential to provide critical clinical guidance for Radiofrequency Ablation (RFA). Uncertainty in cardiac tissue segmentation is high because of the ambiguity of the subtle boundary and intra-/inter-physician variations. In this paper, we proposed a deep learning framework for Optical Coherence Tomography (OCT) cardiac segmentation with uncertainty measurement. Our proposed method employs additional dropout layers to assess the uncertainty of pixel-wise label prediction. In addition, we improve the segmentation performance by using focal loss to put more weights on mis-classified examples. Experimental results show that our method achieves high accuracy on pixel-wise label prediction. The feasibility of our method for uncertainty measurement is also demonstrated with excellent correspondence between uncertain regions within OCT images and heterogeneous regions within corresponding histology images.
Segmentation of human cardiac tissue has a great potential to provide critical clinical guidance for Radiofrequency Ablation (RFA). Uncertainty in cardiac tissue segmentation is high because of the ambiguity of the subtle boundary and intra-/inter-physician variations. In this paper, we proposed a deep learning framework for Optical Coherence Tomography (OCT) cardiac segmentation with uncertainty measurement. Our proposed method employs additional dropout layers to assess the uncertainty of pixel-wise label prediction. In addition, we improve the segmentation performance by using focal loss to put more weights on mis-classified examples. Experimental results show that our method achieves high accuracy on pixel-wise label prediction. The feasibility of our method for uncertainty measurement is also demonstrated with excellent correspondence between uncertain regions within OCT images and heterogeneous regions within corresponding histology images.