Learning Active Contour Models for Medical Image Segmentation

Chen, Xu and Williams, Bryan and Vallabhanehi, Srini and Czanner, Gabriela and Williams, Rachel and Zheng, Yalin (2020) Learning Active Contour Models for Medical Image Segmentation. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, USA, pp. 11632-11640. ISBN 9781728132945

Full text not available from this repository.

Abstract

Image segmentation is an important step in medical image processing and has been widely studied and developed for refinement of clinical analysis and applications. New models based on deep learning have improved results but are restricted to pixel-wise fitting of the segmentation map. Our aim was to tackle this limitation by developing a new model based on deep learning which takes into account the area inside as well as outside the region of interest as well as the size of boundaries during learning. Specifically, we propose a new loss function which incorporates area and size information and integrates this into a dense deep learning model. We evaluated our approach on a dataset of more than 2,000 cardiac MRI scans. Our results show that the proposed loss function outperforms other mainstream loss function Cross-entropy on two common segmentation networks. Our loss function is robust while using different hyperparameter lambda.

Item Type:
Contribution in Book/Report/Proceedings
ID Code:
136719
Deposited By:
Deposited On:
25 Sep 2019 10:45
Refereed?:
Yes
Published?:
Published
Last Modified:
24 Nov 2020 11:55