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  • Küçük Resim
    Öğe
    Effectiveness of Shear Wave Elastography in the diagnosis of acute pancreatitis on admission
    (SOC ROMANA ULTRASONOGRAFE MEDICINA BIOLOGIE-SRUMB, 2018) Durmaz, Mehmet Sedat.; Arslan, Serdar.; Ozbakir, Bora.; Gungor, Gokhan.; Tolu, Ismet.; Arslan, Fatma Zeynep.; Sivri, Mesut.; Koplay, M.
    Aim: We aimed to investigate the effectiveness of shear wave elastography (SWE) in the diagnosis of acute pancreatitis (AP). Material and methods: The pancreatic parenchyma of 50 patients whose clinical and laboratory findings were indicative of AP and of 70 healthy, asymptomatic volunteer participants with normal laboratory values was examined using SWE. Computed tomography was performed in all patients with AP on admission. Elastographic measurements were performed by manually drawing the contours of the pancreatic parenchyma using the free region of interest. The quantitative SWE values (meters/second [m/s], kilopascal [kPa]) of the patients and asymptomatic volunteers group were compared. Results: The mean SWE value of the pancreatic parenchyma was 2.60 +/- 1.63 m/s in the asymptomatic volunteers and 3.48 +/- 0.52 m/s in patients with AP, with a statistically significant difference (p < 0.001, t=-3.685). The mean SWE value of the pancreatic parenchyma was 23.77 +/- 6.72 kPa in the asymptomatic volunteers and 45.71 +/- 10.72 kPa in patients with AP, indicating a significant difference (p < 0.001, t=-3.685). AP can be diagnosed with a sensitivity and specificity of 98.0% when 29.45 kPa was designated as cut-off value and with a 96.0% sensitivity and 98.3% specificity when 2.77 m/s was designated as the cut-off value. The superiority of SWE was found over B-mode US and CECT in the diagnosis of AP on admission. Conclusion: SWE can be used as an effective imaging method with high sensitivity and specificity for the diagnosis of AP. It may be used as an important imaging method to assist in the diagnosis of AP especially when B-mode US and CECT findings are normal.
  • Küçük Resim
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    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.