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Gallbladder agenesis: A case report and brief review
Giuseppe Bianco,Francesco Frongillo,Salvatore Agnes,Erida Nure,Nicola Silvestrini 한국간담췌외과학회 2018 Annals of hepato-biliary-pancreatic surgery Vol.22 No.3
Agenesis of the gallbladder and cystic duct represents one of the rarest anomalies of the biliary system, with a reported incidence of 0.007% to 0.027%. Almost half of the patients develop common duct stones and 23% of them manifest signs and symptoms that mimic biliary colic. We present the case of a woman presenting with symptoms of biliary colic. Based on the clinical findings and after abdominal ultrasonography, which showed hyperechoic material in the gallbladder fossa, a laparoscopic cholecystectomy was planned. Laparoscopy failed to reveal either gallbladder or cystic duct. The procedure was continued to further search for ectopic sites of gallbladder. A condition of gallbladder agenesis was hypothesized and the procedure was aborted without dissection of hepatic pedicle or conversion to laparotomy. Agenesis of gallbladder and cystic duct was confirmed via pos-operative magnetic resonance cholangiopancreatography. We report our experience with regard to the challenges associated with the diagnosis and management, and a brief review of the literature of this rare pathology.
Deep learning approach for the segmentation of aneurysmal ascending aorta
Albert Comelli,Navdeep Dahiya,Alessandro Stefano,Viviana Benfante,Giovanni Gentile,Valentina Agnese,Giuseppe M. Raffa,Michele Pilato,Anthony Yezzi,Giovanni Petrucci,Salvatore Pasta 대한의용생체공학회 2021 Biomedical Engineering Letters (BMEL) Vol.11 No.1
Diagnosis of ascending thoracic aortic aneurysm (ATAA) is based on the measurement of the maximum aortic diameter,but size is not a good predictor of the risk of adverse events. There is growing interest in the development of novel imagederivedrisk strategies to improve patient risk management towards a highly individualized level. In this study, the feasibilityand efficacy of deep learning for the automatic segmentation of ATAAs was investigated using UNet, ENet, and ERFNettechniques. Specifically, CT angiography done on 72 patients with ATAAs and different valve morphology (i.e., tricuspidaortic valve, TAV, and bicuspid aortic valve, BAV) were semi-automatically segmented with Mimics software (MaterializeNV, Leuven, Belgium), and then used for training of the tested deep learning models. The segmentation performance interms of accuracy and time inference were compared using several parameters. All deep learning models reported a dicescore higher than 88%, suggesting a good agreement between predicted and manual ATAA segmentation. We found that theENet and UNet are more accurate than ERFNet, with the ENet much faster than UNet. This study demonstrated that deeplearning models can rapidly segment and quantify the 3D geometry of ATAAs with high accuracy, thereby facilitating theexpansion into clinical workflow of personalized approach to the management of patients with ATAAs.