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Integration of anatomical and radiological analysis suggests more segments in the human kidney.

Veronica MacchiEdgardo Enrico Edoardo PicardiAndrea PorzionatoAldo MorraVincenzo FicarraMarious LoukasRichard Shane TubbsRaffaele De Caro
Published in: Clinical anatomy (New York, N.Y.) (2018)
An increasing number of observations have called the general scheme of five renal segments into question: anatomists, radiologists, and surgeons have reported discrepancies between Graves's scheme and morphological observations. The aims of the present study are: (1) to assess the correspondence between a virtual and a real vascular cast of the kidney; (2) to analyze the arterial anatomy with reference to the renal segments. Fifteen kidneys were injected with acrylic resins to obtain vascular casts, which were also analyzed by computed tomography. A mean of 6.3 (range 4-8) avascular fissures was found, indicating a mean of 7.3 segments (range 5-9). In the superior and middle territories there was a single segment in 4 (26.7%) and 8 (53.3%) cases, respectively, and there were two segments in 11 (73.3%) and in 7 (46.7%) cases, respectively. In the inferior territory there was a single segment in two cases (13.3%), two segments in nine (60%), and three segments in four (26.7%). A mean segmental volume of 550.5 mm3 was calculated; the posterior (1,030.1 mm3 , 28.9%) and inferior (450.3 mm3 , 24.2%) segments were the largest. More third order branches were identified in the inferior segments than in the other segments (three branches of the inferior segmental artery in 26.6%). According to these data the inferior segment occupies the inferior pole, extending both anteriorly and posteriorly. In conclusion, the high correspondence between a virtual and a real vascular cast permits more segments to be identified than those described by Graves, and the volume of each segment can be calculated. Clin. Anat., 2018. © 2018 Wiley Periodicals, Inc.
Keyphrases
  • computed tomography
  • magnetic resonance imaging
  • machine learning
  • magnetic resonance
  • deep learning
  • electronic health record
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