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Learning Active Contour Models for Medical Image Segmentation

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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Learning Active Contour Models for Medical Image Segmentation. / Chen, Xu; Williams, Bryan; Vallabhanehi, Srini et al.
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020. p. 11632-11640.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Chen, X, Williams, B, Vallabhanehi, S, Czanner, G, Williams, R & Zheng, Y 2020, Learning Active Contour Models for Medical Image Segmentation. in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp. 11632-11640, IEEE Conference on Computer Vision and Pattern Recognition 2019, Long Beach, California, United States, 16/06/19. https://doi.org/10.1109/CVPR.2019.01190

APA

Chen, X., Williams, B., Vallabhanehi, S., Czanner, G., Williams, R., & Zheng, Y. (2020). Learning Active Contour Models for Medical Image Segmentation. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 11632-11640). IEEE. https://doi.org/10.1109/CVPR.2019.01190

Vancouver

Chen X, Williams B, Vallabhanehi S, Czanner G, Williams R, Zheng Y. Learning Active Contour Models for Medical Image Segmentation. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE. 2020. p. 11632-11640 Epub 2019 Jun 16. doi: 10.1109/CVPR.2019.01190

Author

Chen, Xu ; Williams, Bryan ; Vallabhanehi, Srini et al. / Learning Active Contour Models for Medical Image Segmentation. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020. pp. 11632-11640

Bibtex

@inproceedings{cf29b0edf3a34c34a6fe891e4b346d4c,
title = "Learning Active Contour Models for Medical Image Segmentation",
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.",
author = "Xu Chen and Bryan Williams and Srini Vallabhanehi and Gabriela Czanner and Rachel Williams and Yalin Zheng",
year = "2020",
month = jan,
day = "9",
doi = "10.1109/CVPR.2019.01190",
language = "English",
isbn = "9781728132945",
pages = "11632--11640",
booktitle = "2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
publisher = "IEEE",
note = "IEEE Conference on Computer Vision and Pattern Recognition 2019, CVPR 2019 ; Conference date: 16-06-2019 Through 20-06-2019",

}

RIS

TY - GEN

T1 - Learning Active Contour Models for Medical Image Segmentation

AU - Chen, Xu

AU - Williams, Bryan

AU - Vallabhanehi, Srini

AU - Czanner, Gabriela

AU - Williams, Rachel

AU - Zheng, Yalin

PY - 2020/1/9

Y1 - 2020/1/9

N2 - 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.

AB - 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.

U2 - 10.1109/CVPR.2019.01190

DO - 10.1109/CVPR.2019.01190

M3 - Conference contribution/Paper

SN - 9781728132945

SP - 11632

EP - 11640

BT - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

PB - IEEE

T2 - IEEE Conference on Computer Vision and Pattern Recognition 2019

Y2 - 16 June 2019 through 20 June 2019

ER -