by INTECH Open Access Publisher .
Written in English
|Contributions||Ean Teng Khor, author|
|The Physical Object|
|Pagination||1 online resource|
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Despite the relatively large number of studies examining the detection and differential diagnosis of specific focal hepatic lesions on Gd-EOB-DTPA-enhanced MR, there have been no reports on the diagnostic performance of Gd-EOB-DTPA for the characterization of incidentally found focal liver by: All lesions were proved by pathologic and MRI findings as primary or metastatic hepatic CEUS, 45 (%) FLLs displayed arterial hyperenhancement and 55 (%) lesions. ORIGINAL RESEARCH Open Access Characterization of hepatic tumors using [11C]metomidate through positron emissiontomography: comparison with [11C]acetateAnne Roivainen1, Alexandru Naum1,2, Heikki Nuutinen3, Rauli Leino4, Heimo Nurmi4, Kjell Någren1,7, Riitta Parkkola4, Johanna Virtanen4, Markku Kallajoki5, Harry Kujari5, Jari Ovaska6, Peter Roberts6 and Marko Cited by: 4.
O A midcycle mass should suggest a follicular cyst. o A patient who complains of pelvic pain in the second half of the menstrual • Particularly useful in identifying fatty or hemorrhagic components to gonadoblastoma, mixed germ cell . Hepatic Cysts. Cysts are the most commonly encountered hepatic lesion, occurring in % of the general population , and have a slight predominance in females (female-male ratio, ) .Hepatic cysts are thought to be of biliary origin as a result of deranged development of the biliary tree (i.e., a hamartoma of biliary origin or so-called “von Meyenburg complex”) .Cited by: / Automated liver lesion characterization using fast kVp switching dual energy computed tomography imaging. Medical Imaging Computer-Aided Diagnosis. editor / Ronald M. Summers ; Nico Karssemeijer. SPIE, (Progress in Biomedical Optics and Imaging - Proceedings of SPIE).Cited by: Abstract: Automatic segmentation of the liver and hepatic lesions is an important step towards deriving quantitative biomarkers for accurate clinical diagnosis and computer-aided decision support systems. This paper presents a method to automatically segment liver and lesions in CT and MRI abdomen images using cascaded fully convolutional neural networks (CFCNs) Cited by:
Hepatic adenoma may be differentiated from other lesions with the use of triple-phase CT scanning or MRI. The lesion appears as a nonspecific, well-circumscribed mass that has a low density (A) (arrow), with a marked centripetal pattern of enhancement during the arterial phase . i Title: Segmentation and modelling of hepatic lesions using Computer Vision: A Review Background: Liver cancer is one of the most lethal cancers worldwide, occurring in both primary liver cancer and as secondary cancer form, derived from metastizations of other primary tumours. Physicians recur to Computed Tomography (CT) for the tasks of visualization of anomalies in . Multiple focal liver lesions can generate diagnosis difficulties in daily practice. This paper present the case of a 53 years old patient with multiple hyperechoic liver lesions suggestive for hepatic hemangiomas, detected during the ultrasonographic exam. Since benign liver lesions are common, liver-imaging strategies should incorporate liver lesion detection and characterisation. Survey examination in patients with a known extra-hepatic malignancy to exclude the presence of hepatic and extra-hepatic involvement is normally undertaken with a contrast-enhanced computed tomography by: