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Quantification of fibrous cap thickness in intracoronary optical coherence tomography with a contour segmentation method based on dynamic programming.

Zahnd G, Karanasos A, van Soest G, Regar E, Niessen W, Gijsen F, van Walsum T - Int J Comput Assist Radiol Surg (2015)

Bottom Line: In the context of preoperative planning and perioperative decision making, intracoronary optical coherence tomography imaging can provide a very detailed characterization of the arterial wall structure.However, visual interpretation of the images is laborious, subject to variability, and therefore not always sufficiently reliable for immediate decision of treatment.Validated on a dataset of 179 images from 21 patients, the present framework could successfully extract the fibrous cap contours.

View Article: PubMed Central - PubMed

Affiliation: Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus Medical Center, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands, g.zahnd@erasmusmc.nl.

ABSTRACT

Objectives: Fibrous cap thickness is the most critical component of plaque stability. Therefore, in vivo quantification of cap thickness could yield valuable information for estimating the risk of plaque rupture. In the context of preoperative planning and perioperative decision making, intracoronary optical coherence tomography imaging can provide a very detailed characterization of the arterial wall structure. However, visual interpretation of the images is laborious, subject to variability, and therefore not always sufficiently reliable for immediate decision of treatment.

Methods: A novel semiautomatic segmentation method to quantify coronary fibrous cap thickness in optical coherence tomography is introduced. To cope with the most challenging issue when estimating cap thickness (namely the diffuse appearance of the anatomical abluminal interface to be detected), the proposed method is based on a robust dynamic programming framework using a geometrical a priori. To determine the optimal parameter settings, a training phase was conducted on 10 patients.

Results: Validated on a dataset of 179 images from 21 patients, the present framework could successfully extract the fibrous cap contours. When assessing minimal cap thickness, segmentation results from the proposed method were in good agreement with the reference tracings performed by a medical expert (mean absolute error and standard deviation of 22 ± 18 μm) and were similar to inter-observer reproducibility (21 ± 19 μm, R = .74), while being significantly faster and fully reproducible.

Conclusion: The proposed framework demonstrated promising performances and could potentially be used for online identification of high-risk plaques.

No MeSH data available.


Related in: MedlinePlus

Representative results of the segmentation framework on eight frames from different pullbacks. For each example, the panel composition is the following. The top row displays the full image with the region of interest (ROI, white arc). The middle row displays an enlarged view of the region delimited by the dashed square in the top row. The automatic lumen segmentation is represented by the cyan line. Within the ROI, tracings of the abluminal interface performed by the segmentation method and the analysts  and  are represented by the magenta, yellow, and green lines, respectively. The bottom row displays the cap thickness (scale in ), automatically computed within the ROI as the distance between the luminal and abluminal contours that were extracted by the segmentation method
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Fig4: Representative results of the segmentation framework on eight frames from different pullbacks. For each example, the panel composition is the following. The top row displays the full image with the region of interest (ROI, white arc). The middle row displays an enlarged view of the region delimited by the dashed square in the top row. The automatic lumen segmentation is represented by the cyan line. Within the ROI, tracings of the abluminal interface performed by the segmentation method and the analysts and are represented by the magenta, yellow, and green lines, respectively. The bottom row displays the cap thickness (scale in ), automatically computed within the ROI as the distance between the luminal and abluminal contours that were extracted by the segmentation method

Mentions: For each analyzed frame of both training and testing sets, the luminal interface was automatically extracted for the entire vessel circumference, and the abluminal interface of the fibrous cap was automatically extracted within the ROI defined by the expert (Fig. 1a, b). Representative examples of resulting segmentation contours are displayed in Fig. 4. The results of our segmentation method, compared to the tracings of both observers and , are presented alongside to the corresponding inter-observer variability in Table 2.Fig. 4


Quantification of fibrous cap thickness in intracoronary optical coherence tomography with a contour segmentation method based on dynamic programming.

Zahnd G, Karanasos A, van Soest G, Regar E, Niessen W, Gijsen F, van Walsum T - Int J Comput Assist Radiol Surg (2015)

Representative results of the segmentation framework on eight frames from different pullbacks. For each example, the panel composition is the following. The top row displays the full image with the region of interest (ROI, white arc). The middle row displays an enlarged view of the region delimited by the dashed square in the top row. The automatic lumen segmentation is represented by the cyan line. Within the ROI, tracings of the abluminal interface performed by the segmentation method and the analysts  and  are represented by the magenta, yellow, and green lines, respectively. The bottom row displays the cap thickness (scale in ), automatically computed within the ROI as the distance between the luminal and abluminal contours that were extracted by the segmentation method
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4563002&req=5

Fig4: Representative results of the segmentation framework on eight frames from different pullbacks. For each example, the panel composition is the following. The top row displays the full image with the region of interest (ROI, white arc). The middle row displays an enlarged view of the region delimited by the dashed square in the top row. The automatic lumen segmentation is represented by the cyan line. Within the ROI, tracings of the abluminal interface performed by the segmentation method and the analysts and are represented by the magenta, yellow, and green lines, respectively. The bottom row displays the cap thickness (scale in ), automatically computed within the ROI as the distance between the luminal and abluminal contours that were extracted by the segmentation method
Mentions: For each analyzed frame of both training and testing sets, the luminal interface was automatically extracted for the entire vessel circumference, and the abluminal interface of the fibrous cap was automatically extracted within the ROI defined by the expert (Fig. 1a, b). Representative examples of resulting segmentation contours are displayed in Fig. 4. The results of our segmentation method, compared to the tracings of both observers and , are presented alongside to the corresponding inter-observer variability in Table 2.Fig. 4

Bottom Line: In the context of preoperative planning and perioperative decision making, intracoronary optical coherence tomography imaging can provide a very detailed characterization of the arterial wall structure.However, visual interpretation of the images is laborious, subject to variability, and therefore not always sufficiently reliable for immediate decision of treatment.Validated on a dataset of 179 images from 21 patients, the present framework could successfully extract the fibrous cap contours.

View Article: PubMed Central - PubMed

Affiliation: Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus Medical Center, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands, g.zahnd@erasmusmc.nl.

ABSTRACT

Objectives: Fibrous cap thickness is the most critical component of plaque stability. Therefore, in vivo quantification of cap thickness could yield valuable information for estimating the risk of plaque rupture. In the context of preoperative planning and perioperative decision making, intracoronary optical coherence tomography imaging can provide a very detailed characterization of the arterial wall structure. However, visual interpretation of the images is laborious, subject to variability, and therefore not always sufficiently reliable for immediate decision of treatment.

Methods: A novel semiautomatic segmentation method to quantify coronary fibrous cap thickness in optical coherence tomography is introduced. To cope with the most challenging issue when estimating cap thickness (namely the diffuse appearance of the anatomical abluminal interface to be detected), the proposed method is based on a robust dynamic programming framework using a geometrical a priori. To determine the optimal parameter settings, a training phase was conducted on 10 patients.

Results: Validated on a dataset of 179 images from 21 patients, the present framework could successfully extract the fibrous cap contours. When assessing minimal cap thickness, segmentation results from the proposed method were in good agreement with the reference tracings performed by a medical expert (mean absolute error and standard deviation of 22 ± 18 μm) and were similar to inter-observer reproducibility (21 ± 19 μm, R = .74), while being significantly faster and fully reproducible.

Conclusion: The proposed framework demonstrated promising performances and could potentially be used for online identification of high-risk plaques.

No MeSH data available.


Related in: MedlinePlus