{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T02:38:16Z","timestamp":1792809496228,"version":"4.5.0"},"reference-count":35,"publisher":"Journal of Neurosurgery Publishing Group (JNSPG)","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,2]]},"abstract":"<jats:sec>\n                    <jats:title>OBJECTIVE<\/jats:title>\n                    <jats:p>Imaging evaluation of the cerebral ventricles is important for clinical decision-making in pediatric hydrocephalus. Although quantitative measurements of ventricular size, over time, can facilitate objective comparison, automated tools for calculating ventricular volume are not structured for clinical use. The authors aimed to develop a fully automated deep learning (DL) model for pediatric cerebral ventricle segmentation and volume calculation for widespread clinical implementation across multiple hospitals.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS<\/jats:title>\n                    <jats:p>The study cohort consisted of 200 children with obstructive hydrocephalus from four pediatric hospitals, along with 199 controls. Manual ventricle segmentation and volume calculation values served as \u201cground truth\u201d data. An encoder-decoder convolutional neural network architecture, in which T2-weighted MR images were used as input, automatically delineated the ventricles and output volumetric measurements. On a held-out test set, segmentation accuracy was assessed using the Dice similarity coefficient (0 to 1) and volume calculation was assessed using linear regression. Model generalizability was evaluated on an external MRI data set from a fifth hospital. The DL model performance was compared against FreeSurfer research segmentation software.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>Model segmentation performed with an overall Dice score of 0.901 (0.946 in hydrocephalus, 0.856 in controls). The model generalized to external MR images from a fifth pediatric hospital with a Dice score of 0.926. The model was more accurate than FreeSurfer, with faster operating times (1.48 seconds per scan).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSIONS<\/jats:title>\n                    <jats:p>The authors present a DL model for automatic ventricle segmentation and volume calculation that is more accurate and rapid than currently available methods. With near-immediate volumetric output and reliable performance across institutional scanner types, this model can be adapted to the real-time clinical evaluation of hydrocephalus and improve clinician workflow.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3171\/2020.6.peds20251","type":"journal-article","created":{"date-parts":[[2020,12,4]],"date-time":"2020-12-04T02:08:43Z","timestamp":1607047723000},"page":"131-138","source":"Crossref","is-referenced-by-count":49,"title":["Artificial intelligence for automatic cerebral ventricle segmentation and volume calculation: a clinical tool for the evaluation of pediatric hydrocephalus"],"prefix":"10.3171","volume":"27","author":[{"given":"Jennifer L.","family":"Quon","sequence":"first","affiliation":[{"name":"Department of Neurosurgery, Stanford University School of Medicine;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michelle","family":"Han","sequence":"additional","affiliation":[{"name":"Stanford University School of Medicine;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lily H.","family":"Kim","sequence":"additional","affiliation":[{"name":"Stanford University School of Medicine;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mary Ellen","family":"Koran","sequence":"additional","affiliation":[{"name":"Department of Radiology, Stanford University School of Medicine;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leo C.","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Urology, Stanford University School of Medicine;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Edward H.","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Stanford University School of Engineering, Stanford, California;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jason","family":"Wright","sequence":"additional","affiliation":[{"name":"Department of Radiology, Seattle Children\u2019s Hospital, University of Washington School of Medicine, Seattle, Washington;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vijay","family":"Ramaswamy","sequence":"additional","affiliation":[{"name":"Section of Neuro-Oncology, The Hospital for Sick Children, University of Toronto, Ontario, Canada;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert M.","family":"Lober","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Dayton Children\u2019s Hospital, Wright State University Boonshoft School of Medicine, Dayton, Ohio;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael D.","family":"Taylor","sequence":"additional","affiliation":[{"name":"Division of Neurosurgery, The Hospital for Sick Children, University of Toronto, Ontario, Canada;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gerald A.","family":"Grant","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Stanford University School of Medicine;"},{"name":"Divisions of Pediatric Neurosurgery and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samuel H.","family":"Cheshier","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, University of Utah School of Medicine, Salt Lake City, Utah; and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John R. 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