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Öğe An efficient pipeline for abdomen segmentation in CT images(SPRINGER, 2018) Koyuncu, Hasan.; Koyuncu, Hasan.; Sivri, Mesut.; Erdogan, Hasan.Computed tomography (CT) scans usually include some disadvantages due to the nature of the imaging procedure, and these handicaps prevent accurate abdomen segmentation. Discontinuous abdomen edges, bed section of CT, patient information, closeness between the edges of the abdomen and CT, poor contrast, and a narrow histogram can be regarded as the most important handicaps that occur in abdominal CT scans. Currently, one or more handicaps can arise and prevent technicians obtaining abdomen images through simple segmentation techniques. In other words, CT scans can include the bed section of CT, a patient's diagnostic information, low-quality abdomen edges, low-level contrast, and narrow histogram, all in one scan. These phenomena constitute a challenge, and an efficient pipeline that is unaffected by handicaps is required. In addition, analysis such as segmentation, feature selection, and classification has meaning for a real-time diagnosis system in cases where the abdomen section is directly used with a specific size. A statistical pipeline is designed in this study that is unaffected by the handicaps mentioned above. Intensity-based approaches, morphological processes, and histogram-based procedures are utilized to design an efficient structure. Performance evaluation is realized in experiments on 58 CT images (16 training, 16 test, and 26 validation) that include the abdomen and one or more disadvantage(s). The first part of the data (16 training images) is used to detect the pipeline's optimum parameters, while the second and third parts are utilized to evaluate and to confirm the segmentation performance. The segmentation results are presented as the means of six performance metrics. Thus, the proposed method achieves remarkable average rates for training/test/validation of 98.95/99.36/99.57% (jaccard), 99.47/99.67/99.79% (dice), 100/99.91/99.91% (sensitivity), 98.47/99.23/99.85% (specificity), 99.38/99.63/99.87% (classification accuracy), and 98.98/99.45/99.66% (precision). In summary, a statistical pipeline performing the task of abdomen segmentation is achieved that is not affected by the disadvantages, and the most detailed abdomen segmentation study is performed for the use before organ and tumor segmentation, feature extraction, and classification.Öğe Two-dimensional shear wave elastography in the assessment of salivary gland ınvolvement in primary sjogren's syndrome(WILEY, 2019) Arslan, Serdar.; Durmaz, Mehmet Sedat.; Erdogan, Hasan.; Esmen, Serpil Ergulu.; Turgut, Bekir.; Iyisoy, Mehmet Sinan.Objectives The aim of this study was to investigate the diagnostic performance of two-dimensional (2D) shear wave elastography (SWE) in the assessment of salivary gland involvement in primary Sjogren's syndrome (pSS). Methods Fifty-three patients with pSS and 30 healthy volunteers were included. The echogenicity of all submandibular and parotid glands was evaluated with B-mode ultrasound, and their elasticity was assessed with 2D SWE. The mean and standard deviation of the shear wave speed and elasticity modes on 2D SWE were calculated. Results The mean shear wave speed and elasticity mode values for the submandibular and parotid glands were significantly higher in the patients with pSS (P < .05). The mean elasticity of the shear wave speed mode was best able to differentiate the parotid glands of patients with pSS from those of healthy volunteers at a cutoff value of 2.48 m/s, whereas the mean elasticity of the elasticity mode was best able to differentiate the submandibular glands of patients with pSS from those of healthy volunteers at a cutoff value of 21 kPa. Conclusions Two-dimensional SWE is an effective technique for assessment of the parenchyma of the salivary glands in patients with pSS and predicts interstitial fibrosis and the severity of histologic damage.