Gradient-based enhancement of tubular structures in medical images

Moreno, Rodrigo; Smedby, Örjan

doi:10.1016/j.media.2015.07.001

Cited by 25 publications

(24 citation statements)

References 54 publications

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“…Gradient flux-based algorithms are quite popular (e.g. [175], [176], [177], [178], [179], [180]) since gradient flux allows overcoming intensity inhomogeneities, resulting in the segmentation of the entire vascular tree without contour leakages.…”

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

470

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

470

263

View full text Add to dashboard Cite

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“…The main observation in [1] is that the LODG at vessel structures exhibit ring-like patterns as shown in Fig. 1.…”

Section: Gradient-based Vesselnessmentioning

confidence: 95%

“…We recently proposed a vesselness method that is based on the analysis of the filtered local orientation distribution of the gradient (LODG) in the spherical harmonics domain [1]. Despite the fact the method yields better results than previously proposed methods, its main drawback is its computational cost.…”

Section: Introductionmentioning

confidence: 99%

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Vesselness estimation through higher-order orientation tensors

Moreno

Smedby

2016

2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)

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“…The most popular rely on the information of the Hessian matrix, typically named vesselness (vessel enhancement) filters [173,71] combined with thinning algorithms for centerline extraction [124]. We do not go into detail about these problems; here are just referenced some recent methods for vessel enhancement by Xiao et al [222], Yang et al [225], and Moreno et al [144,145] and for centerline extraction by Kumar et al [117,118], and Sironi et al [184]. …”

Section: Segmentationmentioning

confidence: 99%

Guidance and Visualization for Brain Tumor Surgery

Marreiros¹,

Miguel²

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Image guidance and visualization play an important role in modern surgery to help surgeons perform their surgical procedures. Here, the focus is on neurosurgery applications, in particular brain tumor surgery where a craniotomy (opening of the skull) is performed to access directly the brain region to be treated. In this type of surgery, once the skull is opened the brain can change its shape, and this deformation is known as brain shift. Moreover, the boundaries of many types of tumors are difficult to identify by the naked eye from healthy tissue. The main goal of this work was to study and develop image guidance and visualization methods for tumor surgery in order to overcome the problems faced in this type of surgery.Due to brain shift the magnetic resonance dataset acquired before the operation (preoperatively) no longer corresponds to the anatomy of the patient during the operation (intraoperatively). For this reason, in this work methods were studied and developed to compensate for this deformation. To guide the deformation methods, information of the superficial vessel centerlines of the brain was used. A method for accurate (approximately 1 mm) reconstruction of the vessel centerlines using a multiview camera system was developed. It uses geometrical constraints, relaxation labeling, thin plate spline filtering and finally mean shift to find the correct correspondences between the camera images.A complete non-rigid deformation pipeline was initially proposed and evaluated with an animal model. From these experiments it was observed that although the traditional non-rigid registration methods (in our case coherent point drift) were able to produce satisfactory vessel correspondences between preoperative and intraoperative vessels, in some specific areas the results were suboptimal. For this reason a new method was proposed that combined the coherent point drift and thin plate spline semilandmarks. This combination resulted in an accurate (below 1 mm) non-rigid registration method, evaluated with simulated data where artificial deformations were performed.Besides the non-rigid registration methods, a new rigid registration method to obtain the rigid transformation between the magnetic resonance dataset and the neuronavigation coordinate systems was also developed.Once the rigid transformation and the vessel correspondences are known, i the thin plate spline can be used to perform the brain shift deformation. To do so, we have used two approaches: a direct and an indirect. With the direct approach, an image is created that represents the deformed data, and with the indirect approach, a new volume is first constructed and only after that can the deformed image be created. A comparison of these two approaches, implemented for the graphics processing units, in terms of performance and image quality, was performed. The indirect method was superior in terms of performance if the sampling along the ray is high, in comparison to the voxel grid, while the direct was superior otherwise. The image quality an...

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Gradient-based enhancement of tubular structures in medical images

Cited by 25 publications

References 54 publications

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

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

Vesselness estimation through higher-order orientation tensors

Guidance and Visualization for Brain Tumor Surgery

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