Automatically designed machine vision system for the localization of CCA transverse section in ultrasound images

Beneš, Radek; Karasek, Jan; Bürget, Radim; Říha, Kamil

doi:10.1016/j.cmpb.2012.08.014

Cited by 39 publications

(8 citation statements)

References 19 publications

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“…First of all, localization of the arteries, especially the ICA and ECA is challenging. Benes et al shows localization of the CCA with success rate of 82.7%, and state of the art work shows a sensitivity of 98.1% on a public database, but the localization success rate is unclear. However, the performance of localization algorithms for the ICA and ECA in humans has not been reported in literature so far, only on rat’s carotid arteries.…”

Section: Discussionmentioning

confidence: 99%

Automated 3D geometry segmentation of the healthy and diseased carotid artery in free‐hand, probe tracked ultrasound images

et al. 2020

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Purpose Rupture of an arterosclerotic plaque in the carotid artery is a major cause of stroke. Biomechanical analysis of plaques is under development aiming to aid the clinician in the assessment of plaque vulnerability. Patient‐specific three‐dimensional (3D) geometry assessment of the carotid artery, including the bifurcation, is required as input for these biomechanical models. This requires a high‐resolution, 3D, noninvasive imaging modality such as ultrasound (US). In this study, a high‐resolution two‐dimensional (2D) linear array in combination with a magnetic probe tracking device and automatic segmentation method was used to assess the geometry of the carotid artery. The advantages of using this system over a 3D ultrasound probe are its higher resolution (spatial and temporal) and its larger field of view. Methods A slow sweep (v = ± 5 mm/s) was made over the subject’s neck so that the full geometry of the bifurcated geometry of the carotid artery is captured. An automated segmentation pipeline was developed. First, the Star‐Kalman method was used to approximate the center and size of the vessels for every frame. Images were filtered with a Gaussian high‐pass filter before conversion into the 2D monogenic signals, and multiscale asymmetry features were extracted from these data, enhancing low lateral wall‐lumen contrast. These images, in combination with the initial ellipse contours, were used for an active deformable contour model to segment the vessel lumen. To segment the lumen–plaque boundary, Otsu’s automatic thresholding method was used. Distension of the wall due to the change in blood pressure was removed using a filter approach. Finally, the contours were converted into a 3D hexahedral mesh for a patient‐specific solid mechanics model of the complete arterial wall. Results The method was tested on 19 healthy volunteers and on 3 patients. The results were compared to manual segmentation performed by three experienced observers. Results showed an average Hausdorff distance of 0.86 mm and an average similarity index of 0.91 for the common carotid artery (CCA) and 0.88 for the internal and external carotid artery. For the total algorithm, the success rate was 89%, in 4 out of 38 datasets the ICA and ECA were not sufficient visible in the US images. Accurate 3D hexahedral meshes were successfully generated from the segmented images . Conclusions With this method, a subject‐specific biomechanical model can be constructed directly from a hand‐held 2D US measurement, within 10 min, with a minimal user input. The performance of the proposed segmentation algorithm is comparable to or better than algorithms previously described in literature. Moreover, the algorithm is able to segment the CCA, ICA, and ECA including the carotid bifurcation in transverse B‐mode images in both healthy and diseased arteries.

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Section: Discussionmentioning

confidence: 99%

Automated 3D geometry segmentation of the healthy and diseased carotid artery in free‐hand, probe tracked ultrasound images

et al. 2020

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“…In Ref. [57], evolutionary algorithms were used to automatically construct an algorithm to segment the carotid artery. The algorithm built by the system segmented 91.49% of the test images.…”

Section: Image Segmentation and Tracking Of The Target Vesselmentioning

confidence: 99%

Assistive technology for ultrasound-guided central venous catheter placement

2017

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This study evaluated the existing technology used to improve the safety and ease of ultrasound-guided central venous catheterization. Electronic database searches were conducted in Scopus, IEEE, Google Patents, and relevant conference databases (SPIE, MICCAI, and IEEE conferences) for related articles on assistive technology for ultrasound-guided central venous catheterization. A total of 89 articles were examined and pointed to several fields that are currently the focus of improvements to ultrasound-guided procedures. These include improving needle visualization, needle guides and localization technology, image processing algorithms to enhance and segment important features within the ultrasound image, robotic assistance using probe-mounted manipulators, and improving procedure ergonomics through in situ projections of important information. Probe-mounted robotic manipulators provide a promising avenue for assistive technology developed for freehand ultrasound-guided percutaneous procedures. However, there is currently a lack of clinical trials to validate the effectiveness of these devices.

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“…At present, multi-parametric image analysis is frequently discussed within the scientific community [5]. This technique, even though it can be based on traditional segmentation methods (thresholding, active contours), exploits information obtained from more images or modalities at the same time.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of brain tumor parts in magnetic resonance images

Mikulka

Bürget

Říha

et al. 2013

2013 36th International Conference on Telecommunications and Signal Processing (TSP)

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The problem most frequently encountered in the practical processing of medical images consists in the lack of instruments enabling machine evaluation of the images. A typical example of this situation is perfusion analysis of brain tumor types. The first and very significant step lies in the segmentation of individual parts of the brain tumor; after segmentation, the rate of penetration by the applied contrast agent is observed in the parts. The common method, in which a high error rate has to be considered, is to mark these tumor portions manually. Within the second step of the segmentation procedure, the monitoring of perfusion in the segmented tissues is realized together with the correlation to model cases. The quality of brain tissue segmentation exerts significant influence on the quality of evaluation of perfusion parameters; consequently, the tumor type recognition is also influenced. This means that the design of a suitable, accurate, and reproducible method constitutes a critical point within perfusion analysis. In this context, reproducibility is an important aspect owing to the preservation of segmentation conditions in monitoring the development of a tumor in time. The authors describe classification methods enabling the segmentation of images acquired via magnetic resonance tomography.

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Automatically designed machine vision system for the localization of CCA transverse section in ultrasound images

Cited by 39 publications

References 19 publications

Automated 3D geometry segmentation of the healthy and diseased carotid artery in free‐hand, probe tracked ultrasound images

Automated 3D geometry segmentation of the healthy and diseased carotid artery in free‐hand, probe tracked ultrasound images

Assistive technology for ultrasound-guided central venous catheter placement

Segmentation of brain tumor parts in magnetic resonance images

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