{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T02:39:13Z","timestamp":1792809553795,"version":"4.5.0"},"reference-count":44,"publisher":"Journal of Neurosurgery Publishing Group (JNSPG)","license":[{"start":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T00:00:00Z","timestamp":1789689600000},"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":[[2026,9,18]]},"abstract":"<jats:sec>\n                    <jats:title>OBJECTIVE<\/jats:title>\n                    <jats:p>Accurate estimation of CSF and brain volume is an important component in evaluating hydrocephalus treatments, including shunt and endoscopic third ventriculostomy procedures. While MRI-based segmentation typically provides precise measurements, metallic artifacts from implanted shunts in patients with hydrocephalus can impede accurate volume determination. This study introduces a method for assessing brain growth in hydrocephalus patients using artifact-affected MR images and presents an efficient, automated AI-based pipeline for hemibrain segmentation and subsequent volume assessment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS<\/jats:title>\n                    <jats:p>This study utilizes imaging data from the Endoscopic versus Shunt Treatment of Hydrocephalus in Infants trial. Pre- and postoperative T2-weighted MR images were obtained in 75 patients. Hemibrain growth curves for the artifact-free hemisphere are proposed to assess postoperative brain growth in MR images with metallic shunt artifacts. An AI-based hemibrain volume estimation pipeline was developed, consisting of a brain\/CSF segmentation model and a hemibrain mask generator. Segmentation labels, including left\/right hemibrain masks and brain\/CSF segmentation maps, were created. The AI pipeline was trained and validated using a manually segmented data subset. The volumes of left and right brain hemispheres after surgery were calculated and analyzed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>Postoperative hemisphere volume ratios approached the normal ratio and remained constant over time, confirming the feasibility of using hemibrain measurements as proxies for whole-brain volume assessment in the presence of metallic artifacts. Additionally, the AI-based pipeline demonstrated high accuracy in generating hemibrain masks and segmenting brain\/CSF, effectively automating the process of hemibrain volume estimation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSIONS<\/jats:title>\n                    <jats:p>Hemibrain volume estimation of the unaffected hemisphere offers a feasible method for assessing brain growth over time. This process can be automated using a highly accurate AI pipeline, providing a valuable tool for monitoring brain growth in pediatric hydrocephalus patients with shunts.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3171\/2026.4.peds25541","type":"journal-article","created":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T00:00:52Z","timestamp":1789516852000},"page":"1-11","source":"Crossref","is-referenced-by-count":0,"title":["Hemibrain growth as a biomarker for whole-brain growth"],"prefix":"10.3171","author":[{"given":"Utkarsh S.","family":"Bajaj","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Pennsylvania State University, University Park, Pennsylvania;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingzhao","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Pennsylvania State University, University Park, Pennsylvania;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kelsey","family":"Templeton","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Yale University, New Haven, Connecticut;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Srijit","family":"Mukherjee","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Pennsylvania State University, University Park, Pennsylvania;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haichuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Pennsylvania State University, University Park, Pennsylvania;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nichol","family":"Nunn","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, University of Utah, Salt Lake City, Utah; and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abhaya V.","family":"Kulkarni","sequence":"additional","affiliation":[{"name":"Division of Neurosurgery, Department of Surgery, The Hospital for Sick Children, University of Toronto, Ontario, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John R. 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