Adrenal Tumor Vessels Segmentation Using Convolutional Neural Network in Computed Tomography Angiography

Wangyuan Zhao; Hongchao He; Jun Zhao; Jianqi Sun

doi:10.1109/EMBC.2019.8856398

Adrenal Tumor Vessels Segmentation Using Convolutional Neural Network in Computed Tomography Angiography

Wangyuan Zhao
, Hongchao He
, Jun Zhao
, Jianqi Sun

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Scopus citations

Abstract

The adrenal glands are important endocrine glands in humans. They are in complex environments with thin vessels around them. It's meaningful to get the accurate dissection before surgery. However, images used in hospitals are now unable to help doctors with many surgeries, which are produced by digital subtraction angiography. In this study, we used a 3D U-Net model to segment the adrenal tumor vessels in 3D computed tomography angiography slices. The model was evaluated by dice similarity coefficient (DSC) and mean intersection over union (MIoU) with the manually labeled ground truth. The DSC in this model is 94.69% and the MIoU is 90.22%.

Original language	English (US)
Title of host publication	2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1006-1009
Number of pages	4
ISBN (Electronic)	9781538613115
DOIs	https://doi.org/10.1109/EMBC.2019.8856398
State	Published - Jul 2019
Externally published	Yes
Event	41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 - Berlin, Germany Duration: Jul 23 2019 → Jul 27 2019

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)	1557-170X

Conference

Conference	41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
Country/Territory	Germany
City	Berlin
Period	7/23/19 → 7/27/19

ASJC Scopus subject areas

Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Health Informatics

Access to Document

10.1109/EMBC.2019.8856398

Cite this

Zhao, W., He, H., Zhao, J., & Sun, J. (2019). Adrenal Tumor Vessels Segmentation Using Convolutional Neural Network in Computed Tomography Angiography. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 (pp. 1006-1009). Article 8856398 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2019.8856398

Adrenal Tumor Vessels Segmentation Using Convolutional Neural Network in Computed Tomography Angiography. / Zhao, Wangyuan; He, Hongchao; Zhao, Jun et al.
2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1006-1009 8856398 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Zhao, W, He, H, Zhao, J & Sun, J 2019, Adrenal Tumor Vessels Segmentation Using Convolutional Neural Network in Computed Tomography Angiography. in 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019., 8856398, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Institute of Electrical and Electronics Engineers Inc., pp. 1006-1009, 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019, Berlin, Germany, 7/23/19. https://doi.org/10.1109/EMBC.2019.8856398

Zhao W, He H, Zhao J, Sun J. Adrenal Tumor Vessels Segmentation Using Convolutional Neural Network in Computed Tomography Angiography. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1006-1009. 8856398. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC.2019.8856398

Zhao, Wangyuan ; He, Hongchao ; Zhao, Jun et al. / Adrenal Tumor Vessels Segmentation Using Convolutional Neural Network in Computed Tomography Angiography. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 1006-1009 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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abstract = "The adrenal glands are important endocrine glands in humans. They are in complex environments with thin vessels around them. It's meaningful to get the accurate dissection before surgery. However, images used in hospitals are now unable to help doctors with many surgeries, which are produced by digital subtraction angiography. In this study, we used a 3D U-Net model to segment the adrenal tumor vessels in 3D computed tomography angiography slices. The model was evaluated by dice similarity coefficient (DSC) and mean intersection over union (MIoU) with the manually labeled ground truth. The DSC in this model is 94.69\% and the MIoU is 90.22\%.",

author = "Wangyuan Zhao and Hongchao He and Jun Zhao and Jianqi Sun",

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AB - The adrenal glands are important endocrine glands in humans. They are in complex environments with thin vessels around them. It's meaningful to get the accurate dissection before surgery. However, images used in hospitals are now unable to help doctors with many surgeries, which are produced by digital subtraction angiography. In this study, we used a 3D U-Net model to segment the adrenal tumor vessels in 3D computed tomography angiography slices. The model was evaluated by dice similarity coefficient (DSC) and mean intersection over union (MIoU) with the manually labeled ground truth. The DSC in this model is 94.69% and the MIoU is 90.22%.

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Adrenal Tumor Vessels Segmentation Using Convolutional Neural Network in Computed Tomography Angiography

Abstract

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