Local-global active contour model based on tensor-based representation for 3D ultrasound vessel segmentation
© 2021 Institute of Physics and Engineering in Medicine..
Three-dimensional (3D) vessel segmentation can provide full spatial information about an anatomic structure to help physicians gain increased understanding of vascular structures, which plays an utmost role in many medical image-processing and analysis applications. The purpose of this paper aims to develop a 3D vessel-segmentation method that can improve segmentation accuracy in 3D ultrasound (US) images. We propose a 3D tensor-based active contour model method for accurate 3D vessel segmentation. With our method, the contrast-independent multiscale bottom-hat tensor representation and local-global information are captured. This strategy ensures the effective extraction of the boundaries of vessels from inhomogeneous and homogeneous regions without being affected by the noise and low-contrast of the 3D US images. Experimental results in clinical 3D US and public 3D Multiphoton Microscopy datasets are used for quantitative and qualitative comparison with several state-of-the-art vessel segmentation methods. Clinical experiments demonstrate that our method can achieve a smoother and more accurate boundary of the vessel object than competing methods. The mean SE, SP and ACC of the proposed method are: 0.7768 ± 0.0597, 0.9978 ± 0.0013 and 0.9971 ± 0.0015 respectively. Experiments on the public dataset show that our method can segment complex vessels in different medical images with noise and low- contrast.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:66 |
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| Enthalten in: |
Physics in medicine and biology - 66(2021), 11 vom: 28. Mai |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Dong, Jiahui [VerfasserIn] |
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| Links: |
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| Themen: |
3d ultrasound vessel segmentation |
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| Anmerkungen: |
Date Completed 25.11.2021 Date Revised 25.11.2021 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1088/1361-6560/abfc92 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM324635028 |
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| 520 | |a Three-dimensional (3D) vessel segmentation can provide full spatial information about an anatomic structure to help physicians gain increased understanding of vascular structures, which plays an utmost role in many medical image-processing and analysis applications. The purpose of this paper aims to develop a 3D vessel-segmentation method that can improve segmentation accuracy in 3D ultrasound (US) images. We propose a 3D tensor-based active contour model method for accurate 3D vessel segmentation. With our method, the contrast-independent multiscale bottom-hat tensor representation and local-global information are captured. This strategy ensures the effective extraction of the boundaries of vessels from inhomogeneous and homogeneous regions without being affected by the noise and low-contrast of the 3D US images. Experimental results in clinical 3D US and public 3D Multiphoton Microscopy datasets are used for quantitative and qualitative comparison with several state-of-the-art vessel segmentation methods. Clinical experiments demonstrate that our method can achieve a smoother and more accurate boundary of the vessel object than competing methods. The mean SE, SP and ACC of the proposed method are: 0.7768 ± 0.0597, 0.9978 ± 0.0013 and 0.9971 ± 0.0015 respectively. Experiments on the public dataset show that our method can segment complex vessels in different medical images with noise and low- contrast | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a 3d ultrasound vessel segmentation | |
| 650 | 4 | |a active contour model | |
| 650 | 4 | |a tensor-based representation | |
| 700 | 1 | |a Ai, Danni |e verfasserin |4 aut | |
| 700 | 1 | |a Fan, Jingfan |e verfasserin |4 aut | |
| 700 | 1 | |a Deng, Qiaoling |e verfasserin |4 aut | |
| 700 | 1 | |a Song, Hong |e verfasserin |4 aut | |
| 700 | 1 | |a Cheng, Zhigang |e verfasserin |4 aut | |
| 700 | 1 | |a Liang, Ping |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Yongtian |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Jian |e verfasserin |4 aut | |
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