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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

Effects of different neighborhoods used in Section 2.2.5, Stage 2, Step 3: (a) original CT slice, (b) segmented right lung, (c) propagated right lung using 5 × 5 × 5 neighborhood, (d) propagated right lung using 7 × 7 × 7 neighborhood, and (e) propagated right lung using 9 × 9 × 9 neighborhood. Using 7 × 7 × 7 neighborhood, excluded pathological areas are included into the lungs successfully.
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fig7: Effects of different neighborhoods used in Section 2.2.5, Stage 2, Step 3: (a) original CT slice, (b) segmented right lung, (c) propagated right lung using 5 × 5 × 5 neighborhood, (d) propagated right lung using 7 × 7 × 7 neighborhood, and (e) propagated right lung using 9 × 9 × 9 neighborhood. Using 7 × 7 × 7 neighborhood, excluded pathological areas are included into the lungs successfully.

Mentions: Herein, neighborhood of 7 × 7 × 7 region was chosen experimentally by testing this approach on the CT scans from the Department of Radiation Oncology, Gülhane Military Medical Academy. These CT scans belong to 10 patients with limited-stage small cell lung cancer. Each of the patients has one tumor. Gross tumor volumes (GTVs) are 36.7, 16.4, 85.8, 33.0, 28.7, 40.8, 66.5, 21.7, 45.5, and 93.2 cc. Figure 7 shows the effects of different neighborhoods used in Stage 2, Step 3. After all, as shown in Figure 8, excluded pathological areas are included into the lungs.


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)

Effects of different neighborhoods used in Section 2.2.5, Stage 2, Step 3: (a) original CT slice, (b) segmented right lung, (c) propagated right lung using 5 × 5 × 5 neighborhood, (d) propagated right lung using 7 × 7 × 7 neighborhood, and (e) propagated right lung using 9 × 9 × 9 neighborhood. Using 7 × 7 × 7 neighborhood, excluded pathological areas are included into the lungs successfully.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig7: Effects of different neighborhoods used in Section 2.2.5, Stage 2, Step 3: (a) original CT slice, (b) segmented right lung, (c) propagated right lung using 5 × 5 × 5 neighborhood, (d) propagated right lung using 7 × 7 × 7 neighborhood, and (e) propagated right lung using 9 × 9 × 9 neighborhood. Using 7 × 7 × 7 neighborhood, excluded pathological areas are included into the lungs successfully.
Mentions: Herein, neighborhood of 7 × 7 × 7 region was chosen experimentally by testing this approach on the CT scans from the Department of Radiation Oncology, Gülhane Military Medical Academy. These CT scans belong to 10 patients with limited-stage small cell lung cancer. Each of the patients has one tumor. Gross tumor volumes (GTVs) are 36.7, 16.4, 85.8, 33.0, 28.7, 40.8, 66.5, 21.7, 45.5, and 93.2 cc. Figure 7 shows the effects of different neighborhoods used in Stage 2, Step 3. After all, as shown in Figure 8, excluded pathological areas are included into the lungs.

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