{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T06:09:35Z","timestamp":1776319775924,"version":"3.50.1"},"reference-count":68,"publisher":"Journal of Neurosurgery Publishing Group (JNSPG)","issue":"5","license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,11,1]]},"abstract":"<jats:sec>\n                    <jats:title>OBJECTIVE<\/jats:title>\n                    <jats:p>This study aimed to develop and evaluate an automated segmentation model for pediatric brain tumors in the posterior fossa region and the surrounding anatomy to facilitate 3D visualization of pediatric posterior fossa tumors for preoperative planning and patient education.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS<\/jats:title>\n                    <jats:p>Annotations were created for a total of 29 pediatric posterior fossa tumor patients, and an additional cohort of 32 patients was included for only tumor segmentation. Six nnU-nets were trained for automatic semantic segmentation of the tumor, cerebrum, cerebellum, brainstem, ventricles, and venous structures. Performance metrics were set at a minimum Dice similarity coefficient (DSC) of 0.85 and a maximum 95th percentile Hausdorff distance (HD95) of 5 mm to ensure clinically relevant 3D visualization.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>The 6 networks created predictions for all patients in the test set. The median DSC and HD95 scores met the predefined performance criteria with median (IQR) DSC of 0.90 (0.093) for tumor segmentation, 0.97 (0.003) cerebrum, 0.97 (0.015) cerebellum, 0.91 (0.014) brainstem, 0.94 (0.038) ventricles, and 0.89 (0.077) venous structures. Overall high performance was found for automatic segmentation of the tumor and surrounding anatomy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSIONS<\/jats:title>\n                    <jats:p>This study showed the successful development of an automatic segmentation algorithm tailored for pediatric posterior fossa tumors and relevant surrounding anatomy, enabling efficient 3D visualization for preoperative planning and patient education. This study provides a valuable basis for future research aimed at implementing advanced 3D visualization techniques for preoperative planning and patient education in pediatric neurosurgery.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3171\/2025.5.peds24567","type":"journal-article","created":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T15:03:45Z","timestamp":1757689425000},"page":"593-600","source":"Crossref","is-referenced-by-count":3,"title":["Automatic segmentation of pediatric brain tumors in the posterior fossa and the surrounding anatomy: an efficient method for optimized preoperative planning and patient education"],"prefix":"10.3171","volume":"36","author":[{"given":"Kevin","family":"Klein Gunnewiek","sequence":"first","affiliation":[{"name":"Department of Neuro-oncology, Princess M\u00e1xima Center for Pediatric Oncology, Utrecht;"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mathijs","family":"de Boer","sequence":"additional","affiliation":[{"name":"Image Sciences Institute, University Medical Center Utrecht;"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tim","family":"Fick","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, University Medical Center Utrecht, The Netherlands; and"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tristan P. C.","family":"van Doormaal","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, University Medical Center Utrecht, The Netherlands; and"},{"name":"Department of Neurosurgery, University Hospital of Z\u00fcrich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eelco W.","family":"Hoving","sequence":"additional","affiliation":[{"name":"Department of Neuro-oncology, Princess M\u00e1xima Center for Pediatric Oncology, Utrecht;"},{"name":"Department of Neurosurgery, University Medical Center Utrecht, The Netherlands; and"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1754","reference":[{"issue":"4","key":"b1","series-title":"J Digit Imaging","first-page":"1014","article-title":"A holographic augmented reality interface for visualizing of mri data and planning of neurosurgical procedures","volume":"34","author":"Morales Mojica CM","year":"2021","unstructured":"Morales Mojica CM, Velazco-Garcia JD, Pappas EP,  A holographic augmented reality interface for visualizing of mri data and planning of neurosurgical procedures. 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