{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T12:04:25Z","timestamp":1784981065631,"version":"3.55.0"},"reference-count":74,"publisher":"Journal of Neurosurgery Publishing Group (JNSPG)","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,1]]},"abstract":"<jats:sec>\n                    <jats:title>OBJECTIVE<\/jats:title>\n                    <jats:p>The explosive growth of available high-quality imaging data coupled with new progress in hardware capabilities has enabled a new era of unprecedented performance in brain segmentation tasks. Despite the explosion of new data released by consortiums and groups around the world, most published, closed, or openly available segmentation models have either a limited or an unknown role in pediatric brains. This study explores the utility of state-of-the-art automated ventricular segmentation tools applied to pediatric hydrocephalus. Two popular, fast, whole-brain segmentation tools were used (FastSurfer and QuickNAT) to automatically segment the lateral ventricles and evaluate their accuracy in children with hydrocephalus.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS<\/jats:title>\n                    <jats:p>Forty scans from 32 patients were included in this study. The patients underwent imaging at the University of Minnesota Medical Center or satellite clinics, were between 0 and 18 years old, had an ICD-10 diagnosis that included the word hydrocephalus, and had at least one T1-weighted pre- or postcontrast MPRAGE sequence. Patients with poor quality scans were excluded. Dice similarity coefficient (DSC) scores were used to compare segmentation outputs against manually segmented lateral ventricles.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>Overall, both models performed poorly with DSCs of 0.61 for each segmentation tool. No statistically significant difference was noted between model performance (p = 0.86). Using a multivariate linear regression to examine factors associated with higher DSC performance, male gender (p = 0.66), presence of ventricular catheter (p = 0.72), and MRI magnet strength (p = 0.23) were not statistically significant factors. However, younger age (p = 0.03) and larger ventricular volumes (p = 0.01) were significantly associated with lower DSC values. A large-scale visualization of 196 scans in both models showed characteristic patterns of segmentation failure in larger ventricles.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSIONS<\/jats:title>\n                    <jats:p>Significant gaps exist in current cutting-edge segmentation models when applied to pediatric hydrocephalus. Researchers will need to address these types of gaps in performance through thoughtful consideration of their training data before reaching the ultimate goal of clinical deployment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3171\/2025.2.peds24590","type":"journal-article","created":{"date-parts":[[2025,5,23]],"date-time":"2025-05-23T22:57:54Z","timestamp":1748041074000},"page":"165-172","source":"Crossref","is-referenced-by-count":3,"title":["Automated ventricular segmentation in pediatric hydrocephalus: how close are we?"],"prefix":"10.3171","volume":"36","author":[{"given":"Birra R.","family":"Taha","sequence":"first","affiliation":[{"name":"Departments of Neurosurgery"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gaoxiang","family":"Luo","sequence":"additional","affiliation":[{"name":"Computer Science & Engineering, University of Minnesota, Minneapolis, Minnesota"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anant","family":"Naik","sequence":"additional","affiliation":[{"name":"Departments of Neurosurgery"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luke","family":"Sabal","sequence":"additional","affiliation":[{"name":"Departments of Neurosurgery"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ju","family":"Sun","sequence":"additional","affiliation":[{"name":"Computer Science & Engineering, University of Minnesota, Minneapolis, Minnesota"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert A.","family":"McGovern","sequence":"additional","affiliation":[{"name":"Departments of Neurosurgery"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carolina","family":"Sandoval-Garcia","sequence":"additional","affiliation":[{"name":"Departments of Neurosurgery"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel J.","family":"Guillaume","sequence":"additional","affiliation":[{"name":"Departments of Neurosurgery"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1754","reference":[{"key":"b1","article-title":"U-Net: convolutional networks for biomedical image segmentation","author":"Ronneberger O","unstructured":"Ronneberger O, Fischer P, Brox T. 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