학술논문
Comparison of regularization techniques for DCNN-based abdominal aortic aneurysm segmentation
Document Type
Conference
Author
Source
2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) Biomedical Imaging (ISBI 2018), 2018 IEEE 15th International Symposium on. :864-867 Apr, 2018
Subject
Language
ISSN
1945-8452
Abstract
This study compares several state-of-the-art regularization methods applicable to aortic aneurysm segmentation likelihood maps provided by a Deep Convolutional Neural Network (DCNN). These algorithms vary from simple Otsu's thresholding and K-Means clustering, to more complex Level-sets and Conditional Random Fields. Experiments demonstrate that K-means yields the best results for the current application, which poses the question about the need to employ a more sophisticated approach for post-processing the output probability maps.