Ectopic spleen and left-sided vena cava in Beckwith-Wiedemann syndrome

Beckwith-Wiedemann syndrome (BWS) is a congenital overgrowth syndrome characterized by anterior abdominal wall defects, macroglossia, and gigantism. A variety of other abnormalities have been described, however association with ectopic spleen and left-sided vena cava has not been reported previously. We report ectopic spleen, left-sided vena cava and the other abdominal imaging findings of an adult BWS case who came up to date without any follow-up from the early childhood. (C) 2002 Elsevier Science Ltd. All rights reserved

A case report of a completely vanished liver graft after auxiliary partial orthotopic liver transplantation

BACKGROUNDAuxiliary partial orthotopic liver transplantation is an alternative technique for the treatment of patients with fulminant hepatic failure and metabolic liver disease. It provides temporary support of liver function until sufficient regeneration of the native liver. Pediatric patients have a long life expectancy and are best candidates to benefit from the interruption of antirejection treatment. DESCRIPTION OF CASEA 4-year-old boy underwent auxiliary partial orthotopic liver transplantation for fulminant hepatic failure using a cadaveric left lateral segment of liver. One year after auxiliary partial orthotopic liver transplantation, the patient's native liver was determined to be completely normal and he was doing well. The patient was then gradually weaned from the immunosuppression over the course of one year. The graft was undetectable on follow-up computerized tomography performed before complete cessation of immunosuppression, leading to the diagnosis of "vanishing graft syndrome". CONCLUSIONGraft atrophy commonly occurs after auxiliary partial orthotopic liver transplantation due to cessation of antirejection therapy. But to our knowledge, complete graft disappearance is a rare occurrence reported in the English literature. Timing for withdrawal of the immunosuppression is an important decision to be made in this technique. Hippokratia 2015; 19 (3): 274-277

Automatic segmentation of small intestine in computed tomography scans [Bilgisayarli Tomografi Görüntülerinde Ince Ba?irsa?in Otomatik Olarak Bölütlenmesi]

Electric Electronics, Computer Science, Biomedical Engineerings Meeting, EBBT 2016 -- 26 April 2016 through 27 April 2016 --In recent years, computer aided diagnosis (CAD) systems are intelligent systems that use digital image processing methods to process radiological images and aim to shorten the diagnosis time. The first essential step of CAD systems is segmentation of specific areas in radiological images. In this study an adaptive segmentation system was developed to reveal small intestine on CT images. © 2016 IEEE

Automated computer-aided diagnosis of splenic lesions due to abdominal trauma

Tulum, Gökalp (Arel Author), Osman, Onur (Arel Author)Background: Computer-aided detection in the setting of trauma presents unique challenges due to variations in shape and attenuation of the injured organs based on the timing and severity of the injury. We developed and validated an automated computer-aided diagnosis algorithm to detect splenic lesions such as laceration, contusion, subcapsular hematoma, perisplenic hematoma, and active extravasation using computed tomography (CT) images in patients sustaining blunt or penetrating abdominal trauma. Methods: We categorized the splenic pathologies into three groups: contusion/laceration, hematoma, and active extravasation. We first analyzed the spleen and perisplenic region by estimating the mean value and standard deviation of the spleen. We determined adaptive threshold values based on the histogram of the area and detected the lesions after morphological operations and volumetric comparisons. Results: The overall performance of the three computer-aided diagnosis (CAD) algorithms is an accuracy of 0.80, sensitivity of 0.95, specificity of 0.67, and a diagnostic odds ratio (DOR) of 40 with a 95 % confidence interval (CI): 14 to 117. The CAD of perisplenic hematoma had the highest diagnosis rates with an accuracy of 0.90, a sensitivity of 0.95, specificity of 0.80, and DOR of 76 with a 95 % CI: 13 to 442. Conclusions: We developed a new algorithm to detect post-traumatic splenic lesions automatically and with high accuracy. Our method could potentially lead to the automated diagnosis of all traumatic abdominal pathologies