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Automatic segmentation of anatomical structures from CT scans of thorax for RTP.

Özsavaş EE, Telatar Z, Dirican B, Sağer Ö, Beyzadeoğlu M - Comput Math Methods Med (2014)

Bottom Line: To assess the accuracy, we performed two comparisons between the automatically obtained results and the results obtained manually by an expert.The average volume overlap ratio values range between 94.30 ± 3.93% and 99.11 ± 0.26% on the two different datasets.We obtained the average symmetric surface distance values between the ranges of 0.28 ± 0.21 mm and 0.89 ± 0.32 mm by using the same datasets.

View Article: PubMed Central - PubMed

Affiliation: Electrical and Electronics Engineering Department, Faculty of Engineering, Ankara University, Gölbaşı, 06830 Ankara, Turkey.

ABSTRACT
Modern radiotherapy techniques are vulnerable to delineation inaccuracies owing to the steep dose gradient around the target. In this aspect, accurate contouring comprises an indispensable part of optimal radiation treatment planning (RTP). We suggest a fully automated method to segment the lungs, trachea/main bronchi, and spinal canal accurately from computed tomography (CT) scans of patients with lung cancer to use for RTP. For this purpose, we developed a new algorithm for inclusion of excluded pathological areas into the segmented lungs and a modified version of the fuzzy segmentation by morphological reconstruction for spinal canal segmentation and implemented some image processing algorithms along with them. To assess the accuracy, we performed two comparisons between the automatically obtained results and the results obtained manually by an expert. The average volume overlap ratio values range between 94.30 ± 3.93% and 99.11 ± 0.26% on the two different datasets. We obtained the average symmetric surface distance values between the ranges of 0.28 ± 0.21 mm and 0.89 ± 0.32 mm by using the same datasets. Our method provides favorable results in the segmentation of CT scans of patients with lung cancer and can avoid heavy computational load and might offer expedited segmentation that can be used in RTP.

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Related in: MedlinePlus

Segmentation of the lungs: (a, b) original CT slices, (c, d) rough segmentation of the lung fields of (a, b) in white, (e) lungs in white after eliminating the bronchi from (c), (f) lungs in white after removing intestine from (d), and (g, h) lung contours in red superimposed on the original slices.
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fig3: Segmentation of the lungs: (a, b) original CT slices, (c, d) rough segmentation of the lung fields of (a, b) in white, (e) lungs in white after eliminating the bronchi from (c), (f) lungs in white after removing intestine from (d), and (g, h) lung contours in red superimposed on the original slices.

Mentions: As shown in Figure 3, the main steps of lung segmentation are the rough segmentation of the lung fields, segmentation and elimination of the trachea/main bronchi, and then making the lung fields correction. These steps are followed by the right and left lung separation and inclusion of excluded pathological areas.


Automatic segmentation of anatomical structures from CT scans of thorax for RTP.

Özsavaş EE, Telatar Z, Dirican B, Sağer Ö, Beyzadeoğlu M - Comput Math Methods Med (2014)

Segmentation of the lungs: (a, b) original CT slices, (c, d) rough segmentation of the lung fields of (a, b) in white, (e) lungs in white after eliminating the bronchi from (c), (f) lungs in white after removing intestine from (d), and (g, h) lung contours in red superimposed on the original slices.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4281476&req=5

fig3: Segmentation of the lungs: (a, b) original CT slices, (c, d) rough segmentation of the lung fields of (a, b) in white, (e) lungs in white after eliminating the bronchi from (c), (f) lungs in white after removing intestine from (d), and (g, h) lung contours in red superimposed on the original slices.
Mentions: As shown in Figure 3, the main steps of lung segmentation are the rough segmentation of the lung fields, segmentation and elimination of the trachea/main bronchi, and then making the lung fields correction. These steps are followed by the right and left lung separation and inclusion of excluded pathological areas.

Bottom Line: To assess the accuracy, we performed two comparisons between the automatically obtained results and the results obtained manually by an expert.The average volume overlap ratio values range between 94.30 ± 3.93% and 99.11 ± 0.26% on the two different datasets.We obtained the average symmetric surface distance values between the ranges of 0.28 ± 0.21 mm and 0.89 ± 0.32 mm by using the same datasets.

View Article: PubMed Central - PubMed

Affiliation: Electrical and Electronics Engineering Department, Faculty of Engineering, Ankara University, Gölbaşı, 06830 Ankara, Turkey.

ABSTRACT
Modern radiotherapy techniques are vulnerable to delineation inaccuracies owing to the steep dose gradient around the target. In this aspect, accurate contouring comprises an indispensable part of optimal radiation treatment planning (RTP). We suggest a fully automated method to segment the lungs, trachea/main bronchi, and spinal canal accurately from computed tomography (CT) scans of patients with lung cancer to use for RTP. For this purpose, we developed a new algorithm for inclusion of excluded pathological areas into the segmented lungs and a modified version of the fuzzy segmentation by morphological reconstruction for spinal canal segmentation and implemented some image processing algorithms along with them. To assess the accuracy, we performed two comparisons between the automatically obtained results and the results obtained manually by an expert. The average volume overlap ratio values range between 94.30 ± 3.93% and 99.11 ± 0.26% on the two different datasets. We obtained the average symmetric surface distance values between the ranges of 0.28 ± 0.21 mm and 0.89 ± 0.32 mm by using the same datasets. Our method provides favorable results in the segmentation of CT scans of patients with lung cancer and can avoid heavy computational load and might offer expedited segmentation that can be used in RTP.

Show MeSH
Related in: MedlinePlus