Literature DB >> 25861085

Accurate vessel segmentation with constrained B-snake.

Shinichi Tamura.   

Abstract

We describe an active contour framework with accurate shape and size constraints on the vessel cross-sectional planes to produce the vessel segmentation. It starts with a multiscale vessel axis tracing in a 3D computed tomography (CT) data, followed by vessel boundary delineation on the cross-sectional planes derived from the extracted axis. The vessel boundary surface is deformed under constrained movements on the cross sections and is voxelized to produce the final vascular segmentation. The novelty of this paper lies in the accurate contour point detection of thin vessels based on the CT scanning model, in the efficient implementation of missing contour points in the problematic regions and in the active contour model with accurate shape and size constraints. The main advantage of our framework is that it avoids disconnected and incomplete segmentation of the vessels in the problematic regions that contain touching vessels (vessels in close proximity to each other), diseased portions (pathologic structure attached to a vessel), and thin vessels. It is particularly suitable for accurate segmentation of thin and low contrast vessels. Our method is evaluated and demonstrated on CT data sets from our partner site, and its results are compared with three related methods. Our method is also tested on two publicly available databases and its results are compared with the recently published method. The applicability of the proposed method to some challenging clinical problems, the segmentation of the vessels in the problematic regions, is demonstrated with good results on both quantitative and qualitative experimentations; our segmentation algorithm can delineate vessel boundaries that have level of variability similar to those obtained manually.

Entities:  

Mesh:

Year:  2015        PMID: 25861085     DOI: 10.1109/TIP.2015.2417683

Source DB:  PubMed          Journal:  IEEE Trans Image Process        ISSN: 1057-7149            Impact factor:   10.856


  10 in total

1.  Multi-perspective label based deep learning framework for cerebral vasculature segmentation in whole-brain fluorescence images.

Authors:  Yuxin Li; Tong Ren; Junhuai Li; Xiangning Li; Anan Li
Journal:  Biomed Opt Express       Date:  2022-06-01       Impact factor: 3.562

2.  Deep Learning Based Real-Time Semantic Segmentation of Cerebral Vessels and Cranial Nerves in Microvascular Decompression Scenes.

Authors:  Ruifeng Bai; Xinrui Liu; Shan Jiang; Haijiang Sun
Journal:  Cells       Date:  2022-06-02       Impact factor: 7.666

3.  Combining deep learning with anatomical analysis for segmentation of the portal vein for liver SBRT planning.

Authors:  Bulat Ibragimov; Diego Toesca; Daniel Chang; Albert Koong; Lei Xing
Journal:  Phys Med Biol       Date:  2017-11-10       Impact factor: 3.609

4.  Toward Improving Safety in Neurosurgery with an Active Handheld Instrument.

Authors:  Sara Moccia; Simone Foti; Arpita Routray; Francesca Prudente; Alessandro Perin; Raymond F Sekula; Leonardo S Mattos; Jeffrey R Balzer; Wendy Fellows-Mayle; Elena De Momi; Cameron N Riviere
Journal:  Ann Biomed Eng       Date:  2018-07-16       Impact factor: 3.934

5.  A Unified Level Set Framework Combining Hybrid Algorithms for Liver and Liver Tumor Segmentation in CT Images.

Authors:  Zhou Zheng; Xuechang Zhang; Huafei Xu; Wang Liang; Siming Zheng; Yueding Shi
Journal:  Biomed Res Int       Date:  2018-08-09       Impact factor: 3.411

6.  Inter/intra-frame constrained vascular segmentation in X-ray angiographic image sequence.

Authors:  Shuang Song; Chenbing Du; Ying Chen; Danni Ai; Hong Song; Yong Huang; Yongtian Wang; Jian Yang
Journal:  BMC Med Inform Decis Mak       Date:  2019-12-19       Impact factor: 2.796

7.  Accelerated vascular aging: Ethnic differences in basilar artery length and diameter, and its association with cardiovascular risk factors and cerebral small vessel disease.

Authors:  Carole H Sudre; Stefano Moriconi; Rafael Rehwald; Lorna Smith; Therese Tillin; Josephine Barnes; David Atkinson; Sébastien Ourselin; Nish Chaturvedi; Alun D Hughes; H Rolf Jäger; M Jorge Cardoso
Journal:  Front Cardiovasc Med       Date:  2022-07-28

8.  Hepatic Vein and Arterial Vessel Segmentation in Liver Tumor Patients.

Authors:  Haopeng Kuang; Zhongwei Yang; Xukun Zhang; Jinpeng Tan; Xiaoying Wang; Lihua Zhang
Journal:  Comput Intell Neurosci       Date:  2022-09-23

9.  Axis-Guided Vessel Segmentation Using a Self-Constructing Cascade-AdaBoost-SVM Classifier.

Authors:  Xin Hu; Yuanzhi Cheng; Deqiong Ding; Dianhui Chu
Journal:  Biomed Res Int       Date:  2018-03-18       Impact factor: 3.411

Review 10.  Computational Methods for Liver Vessel Segmentation in Medical Imaging: A Review.

Authors:  Marcin Ciecholewski; Michał Kassjański
Journal:  Sensors (Basel)       Date:  2021-03-12       Impact factor: 3.576

  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.