Abstract: Segmentation of acute pulmonary embolism in computed tomography pulmonary angiography using the deep learning method
Introduction: Pulmonary embolism is a type of thromboembolism seen in the main pulmonary artery and its branches. This study aimed to diagnose acute pulmonary embolism using the deep learning method in computed tomographic pulmonary angiography (CTPA) and perform the segmentation of pulmonary embolism data.
Materials And Methods: The CTPA images of patients diagnosed with pulmonary embolism who underwent scheduled imaging were retrospectively evaluated.
Purpose: We aimed to present a case who developed intestinal ischemia and associated perforation and abscess due to Superior Mesenteric Vein (SMV) thrombosis caused by post-COVID-19 syndrome and discuss the preoperative Computed Tomography (CT) imaging findings used in diagnosis.
Case Presentation: A 58-year-old patient presented to our clinic with a complaint of acute abdominal pain. His CT examination revealed thrombosis in SMV, congestion in the mesenteric venous structures, contamination in the mesentery, and thickening and dilatation of the jejunal loops due to ischemia.
Background: The typical findings of COVID-19 pneumonia include multilobar groundglass opacities and consolidation areas observed predominantly in the basal and peripheral parts of both lungs in computed tomography.
Objective: The current study aimed to correlate indeterminate lesions of COVID-19 pneumonia detected on computed tomography with the results of the reverse transcription-polymerase chain reaction (RT-PCR) test.
Methods: Patients with high-resolution computed tomography images that were reported to contain indeterminate lesions in terms of COVID-19 pneumonia were included retrospectively in the study.