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Learning Active Contour Models for Medical Image Segmentation

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Published
  • Xu Chen
  • Bryan Williams
  • Srini Vallabhanehi
  • Gabriela Czanner
  • Rachel Williams
  • Yalin Zheng
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Publication date9/01/2020
Host publication2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
PublisherIEEE
Pages11632-11640
Number of pages9
ISBN (Electronic)9781728132938
ISBN (Print)9781728132945
<mark>Original language</mark>English
EventIEEE Conference on Computer Vision and Pattern Recognition 2019 - Long Beach, California, United States
Duration: 16/06/201920/06/2019

Conference

ConferenceIEEE Conference on Computer Vision and Pattern Recognition 2019
Abbreviated titleCVPR 2019
Country/TerritoryUnited States
CityLong Beach, California
Period16/06/1920/06/19

Conference

ConferenceIEEE Conference on Computer Vision and Pattern Recognition 2019
Abbreviated titleCVPR 2019
Country/TerritoryUnited States
CityLong Beach, California
Period16/06/1920/06/19

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.