title: Automatic segmentation of anatomical structures from CT scans of thorax for RTP.
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.
authors.affiliation: Electrical and Electronics Engineering Department, Faculty of Engineering, Ankara University, Gölbaşı, 06830 Ankara, Turkey.
authors.forename: Emin Emrah
authors.initials: EE
authors.lastname: Özsavaş
authors.affiliation: Electrical and Electronics Engineering Department, Faculty of Engineering, Ankara University, Gölbaşı, 06830 Ankara, Turkey.
authors.forename: Ziya
authors.initials: Z
authors.lastname: Telatar
authors.affiliation: Radiation Oncology Department, Gülhane Military Medical Academy, Etlik, 06018 Ankara, Turkey.
authors.forename: Bahar
authors.initials: B
authors.lastname: Dirican
authors.affiliation: Radiation Oncology Department, Gülhane Military Medical Academy, Etlik, 06018 Ankara, Turkey.
authors.forename: Ömer
authors.initials: Ö
authors.lastname: Sağer
authors.affiliation: Radiation Oncology Department, Gülhane Military Medical Academy, Etlik, 06018 Ankara, Turkey.
authors.forename: Murat
authors.initials: M
authors.lastname: Beyzadeoğlu
mesh_terms: D000465:Algorithms; D001980:Bronchi / Q000473:pathology; D017143:Fuzzy Logic; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008168:Lung / Q000000981:diagnostic imaging; D008175:Lung Neoplasms / Q000000981:diagnostic imaging; D010363:Pattern Recognition, Automated; D011857:Radiographic Image Interpretation, Computer-Assisted; D013902:Radiography, Thoracic; D011878:Radiotherapy; D011880:Radiotherapy Planning, Computer-Assisted; D015203:Reproducibility of Results; D012984:Software; D013131:Spine / Q000000981:diagnostic imaging; D014057:Tomography, X-Ray Computed; D014132:Trachea / Q000000981:diagnostic imaging