Accurate Vessel Segmentation With Constrained B-Snake

Cheng, Yuanzhi; Hu, Xin; Ji, Wang; Wang, Yadong; Tamura, Shinichi

doi:10.1109/tip.2015.2417683

Cited by 80 publications

(60 citation statements)

References 31 publications

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“…In [74], a B-snake active contour [161] is employed to segment retinal vessels. F ext consists of (i) a Gradient Vector Flow (GVF) term [162], which describes how the gradient vectors of an image-derived edge-map diffuses inside the image domain, and (ii) a force contributions that impose constrains on the S evolution, such as vessel cross-section shape, position and size.…”

Section: ) Parametricmentioning

confidence: 99%

Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics

Moccia

Momi

Hadji

et al. 2018

Computer Methods and Programs in Biomedicine

454

263

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Abstract-Background: Blood vessel segmentation is a topic of high interest in medical image analysis since the analysis of vessels is crucial for diagnosis, treatment planning and execution, and evaluation of clinical outcomes in different fields, including laryngology, neurosurgery and ophthalmology. Automatic or semiautomatic vessel segmentation can support clinicians in performing these tasks. Different medical imaging techniques are currently used in clinical practice and an appropriate choice of the segmentation algorithm is mandatory to deal with the adopted imaging technique characteristics (e.g. resolution, noise and vessel contrast).Objective: This paper aims at reviewing the most recent and innovative blood vessel segmentation algorithms. Among the algorithms and approaches considered, we deeply investigated the most novel blood vessel segmentation including machine learning, deformable model, and tracking-based approaches.Method: This paper analyzes more than 100 articles focused on blood vessel segmentation methods. For each analyzed approach, summary tables are presented reporting imaging technique used, anatomical region and performance measures employed. Benefits and disadvantages of each method are highlighted.Discussion: Despite the constant progress and efforts addressed in the field, several issues still need to be overcome. A relevant limitation consists in the segmentation of pathological vessels. Unfortunately, not consistent research effort has been addressed to this issue yet. Research is needed since some of the main assumptions made for healthy vessels (such as linearity and circular cross-section) do not hold in pathological tissues, which on the other hand require new vessel model formulations. Moreover, image intensity drops, noise and low contrast still represent an important obstacle for the achievement of a high-quality enhancement. This is particularly true for optical imaging, where the image quality is usually lower in terms of noise and contrast with respect to magnetic resonance and computer tomography angiography. Conclusion:No single segmentation approach is suitable for all the different anatomical region or imaging modalities, thus the primary goal of this review was to provide an up to date source of information about the state of the art of the vessel segmentation algorithms so that the most suitable methods can be chosen according to the specific task.

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Section: ) Parametricmentioning

confidence: 99%

Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics

Moccia

Momi

Hadji

et al. 2018

Computer Methods and Programs in Biomedicine

454

263

View full text Add to dashboard Cite

show abstract

“…The conventional methods used to detect CAD include 20 catheter guided techniques X-ray angiography, optical coherence tomography (OCT) and intra-vascular ultrasound (IVUS); however, the invasive nature make these methods time consuming and sensitive as a considerable risk is involved.…”

Section: Abnormalitiesmentioning

confidence: 99%

“…al. [20] proposed a novel idea of 2D cross-section based boundary detection of 3D vessels. According to the proposed method, the vessel axis was computed in the first stage using multi-scale Hessian analysis.…”

mentioning

confidence: 99%

A hybrid energy model for region based curve evolution – Application to CTA coronary segmentation

Jawaid

Rajani

Liatsis

et al. 2017

Computer Methods and Programs in Biomedicine

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link AbstractBackground and Objective: State-of-the-art medical imaging techniques have enabled non-invasive imaging of the internal organs. However, high volumes of imaging data make manual interpretation and delineation of abnormalities cumbersome for clinicians. These challenges have driven intensive research into efficient medical image segmentation. In this work, we propose a hybrid regionbased energy formulation for effective segmentation in computed tomography angiography (CTA) imagery.Methods: The proposed hybrid energy couples an intensity-based local term with an efficient discontinuity-based global model of the image for optimal segmentation. The segmentation is achieved using a level set formulation due to the computational robustness. After validating the statistical significance of the hybrid energy, we applied the proposed model to solve an important clinical problem of 3D coronary segmentation. An improved seed detection method is used to initialize the level set evolution. Moreover, we employed an auto-correction feature that captures the emerging peripheries during the curve evolution for completeness of the coronary tree. sults demonstrate the effectiveness of the proposed framework with a consistent mean sensitivity and specificity measures of 80% across the CTA data. Moreover, a high degree of agreement with respect to the inter-observer differences justifies the generalization of the proposed method.Conclusions: The proposed method is effective to segment the coronary tree from the CTA volume based on hybrid image based energy, which can improve the clinicians ability to detect arterial abnormalities.

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“…Alternative enhancement approaches including context-based voting [18], region growing [19]–[21], Gaussian mixture models [13], [22], active contours [23], [24], graph-cut [25] and statistical shape analysis [25], [26] do not specifically address PV segmentation problem. Recently, Zeng et al [27] applied single-layer neural networks for segmentation for liver vasculature which was successful on segmentation of thin and medium-size but demonstrated limited success on PV segmentation.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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Automated segmentation of portal vein (PV) for liver radiotherapy planning is a challenging task due to potentially low vasculature contrast, complex PV anatomy and image artifacts originated from fiducial markers and vasculature stents. In this paper, we propose a novel framework for automated PV segmentation from computed tomography (CT) images. We apply convolutional neural networks (CNN) to learn consistent appearance patterns of PV using a training set of CT images with reference annotations and then enhance PV in previously unseen CT images. Markov Random Fields (MRF) were further used to smooth the CNN enhancement results and remove isolated mis-segmented regions. Finally, CNN-MRF-based enhancement was augmented with PV centerline detection that relied on PV anatomical properties such as tubularity and branch composition. The framework was validated on a clinical database with 72 CT images of patients scheduled to liver stereotactic body radiation therapy. The obtained segmentation accuracy was DSC = 0.83 and η = 1.08 mm in terms of the median Dice coefficient and mean symmetric surface distance, respectively, when segmentation is encompassed into the PV region of interest. The obtained results indicate that CNN and anatomy analysis can be used for accurate segmentation of PV and potentially integrated into liver radiation therapy planning.

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Accurate Vessel Segmentation With Constrained B-Snake

Cited by 80 publications

References 31 publications

Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics

Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics

A hybrid energy model for region based curve evolution – Application to CTA coronary segmentation

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

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