Acta neurochirurgica
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Acta neurochirurgica · Oct 2020
Low incidence of true Sternberg's canal defects among lateral sphenoid sinus encephaloceles.
Spontaneous sphenoid sinus cerebrospinal fluid (CSF) encephaloceles have been postulated to arise from a persistent Sternberg's canal. However, recent evidence has questioned this embryological etiology. We examined the anatomic location of a series of lateral sphenoid sinus encephaloceles to determine if they corresponded with the location of Sternberg's canal. ⋯ No evidence was found to support the existence of a classic Sternberg canal CSF leak, supporting the hypothesis that most sphenoid spontaneous leaks likely occur secondary to chronically elevated ICP. Rare cases may be related to a weakness in the sphenoid wall in the region of Sternberg's canal.
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Acta neurochirurgica · Oct 2020
Selective perioperative steroid supplementation protocol in patients undergoing endoscopic transsphenoidal surgery for pituitary adenomas.
There is no consensus regarding the use of perioperative steroids for transsphenoidal pituitary surgery. We audited the effectiveness and safety of our selective perioperative steroid supplementation protocol in patients with pituitary adenomas. ⋯ Our steroid sparing protocol was both safe and effective. The 15% incidence of protocol deviations is a reminder that the rigorous usage of checklists is mandatory for successful clinical practice.
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Acta neurochirurgica · Oct 2020
Semantic segmentation of cerebrospinal fluid and brain volume with a convolutional neural network in pediatric hydrocephalus-transfer learning from existing algorithms.
For the segmentation of medical imaging data, a multitude of precise but very specific algorithms exist. In previous studies, we investigated the possibility of segmenting MRI data to determine cerebrospinal fluid and brain volume using a classical machine learning algorithm. It demonstrated good clinical usability and a very accurate correlation of the volumes to the single area determination in a reproducible axial layer. This study aims to investigate whether these established segmentation algorithms can be transferred to new, more generalizable deep learning algorithms employing an extended transfer learning procedure and whether medically meaningful segmentation is possible. ⋯ Using the presented methods, we showed that conventional segmentation algorithms can be transferred to new advances in deep learning with comparable accuracy, generating a large number of training data sets with relatively little effort. A clinically meaningful segmentation possibility was demonstrated.