Full-term neonates may have asymptomatic cranial injuries at birth and head ultrasound screening could be useful for early diagnosis. The aim of this study was to assess the prevalence and type of intracranial abnormalities and the usefulness of head ultrasound screening in these infants. Head ultrasound screening was performed on all full-term neonates (gestational age between 37 and 42 weeks), born at Sant'Anna University Hospital of Ferrara, Italy, from June 1, 2008 through May 31, 2013. Ultrasound findings were categorized into three groups: normal, minor, and major anomalies. All full-term neonates (6771) born at our hospital underwent head ultrasound screening. One hundred fourteen of 6771 (1.7%) presented ultrasound abnormalities, whereas 6657 were normal or exhibited insignificant findings. In 101 of 114 (88.6%), abnormalities were minor, and only 13 infants had major abnormalities (0.19% of all full-term newborns). All neonates with major abnormalities presented with either microcephaly or abnormal neurological evaluations. Only one individual with major abnormalities was detected exclusively by ultrasound. The number of significant anomalies detected by head ultrasound screening in asymptomatic full-term neonates born during the study period was low. Therefore, there is no indication for routine general head ultrasound screening in these patients. However, even if low, in neonates who have neurological abnormalities, risk factors or suspected brain malformations, head ultrasound screening may play an important role in the early diagnosis of intracranial anomalies. Copyright 2017 Elsevier Inc. All rights reserved.
AIMS--To establish whether abnormalities in the course of the vertebral artery occur and whether they are relevant to arterial injury associated with head and neck movements. METHODS--Twenty vertebral arteries were carefully dissected at necropsy and abnormalities in course were noted, along with any other bony or cartilaginous cervical anomalies. The effect of head and neck movement on these vessels was studied before a detailed histomorphometric examination was undertaken on sections of the excised arteries. RESULTS--Five vessels had an abnormal course. One vessel entered the transverse foramina of the fifth cervical vertebra rather than the sixth, but was otherwise normal. In two subjects both vertebral arteries were abnormal in the upper cervical portion with, in each case, a straight left vertebral artery and a right vertebral artery with a deficient loop, closely applied to the atlanto-axial joint. Both of these subjects also had completely ossified stylohyoid ligaments and the arteries visibly stretched with modest head and neck movements. Histology revealed variable degrees of smooth muscle disarray in the tunica media of two of the arteries with loop deficiencies. The circumference of one of the straight arteries was smaller than expected but in all other measured histomorphometric parameters these vessels appeared normal. CONCLUSIONS--Vertebral artery loops are deficient in a number of subjects. This finding is important given the recently described biomechanical susceptibility of the vertebral artery to longitudinal extension and may explain the smooth muscle changes, in that this may represent attempts at arterial wall remodelling. Subjects with such loop deficiencies may be more susceptible to a variety of head and neck insults and such abnormalities should be sought at necropsy in subjects who die as a result of fatal vertebral artery injury. Images PMID:7560170
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Femur segmentation is well established and widely used in computer-assisted orthopedic surgery. However, most of the robust segmentation methods such as statistical shape models (SSM) require human intervention to provide an initial position for the SSM. In this paper, we propose to overcome this problem and provide a fully automatic femur segmentation method for CT images based on primitive shape recognition and SSM. Femur segmentation in CT scans was performed using primitive shape recognition based on a robust algorithm such as the Hough transform and RANdom SAmple Consensus. The proposed method is divided into 3 steps: (1) detection of the femoral head as sphere and the femoral shaft as cylinder in the SSM and the CT images, (2) rigid registration between primitives of SSM and CT image to initialize the SSM into the CT image, and (3) fitting of the SSM to the CT image edge using an affine transformation followed by a nonlinear fitting. The automated method provided good results even with a high number of outliers. The difference of segmentation error between the proposed automatic initialization method and a manual initialization method is less than 1 mm. The proposed method detects primitive shape position to initialize the SSM into the target image. Based on primitive shapes, this method overcomes the problem of inter-patient variability. Moreover, the results demonstrate that our method of primitive shape recognition can be used for 3D SSM initialization to achieve fully automatic segmentation of the femur. 2ff7e9595c
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