{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T20:20:17Z","timestamp":1778185217158,"version":"3.51.4"},"reference-count":18,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2021,3,24]],"date-time":"2021-03-24T00:00:00Z","timestamp":1616544000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,3,24]],"date-time":"2021-03-24T00:00:00Z","timestamp":1616544000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"published-print":{"date-parts":[[2021,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Purpose\u00a0<\/jats:title>\n                <jats:p>Electrode bending observed after stereotactic interventions is typically not accounted for in either computer-assisted planning algorithms, where straight trajectories are assumed, or in quality assessment, where only metrics related to entry and target points are reported. Our aim is to provide a fully automated and validated pipeline for the prediction of stereo-electroencephalography (SEEG) electrode bending.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods\u00a0<\/jats:title>\n                <jats:p>We transform electrodes of 86 cases into a common space and compare features-based and image-based neural networks on their ability to regress local displacement (<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\mathbf{lu} $$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mi>lu<\/mml:mi>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula>) or electrode bending (<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\hat{\\mathbf{eb }}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mover>\n                      <mml:mi>eb<\/mml:mi>\n                      <mml:mo>^<\/mml:mo>\n                    <\/mml:mover>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula>). Electrodes were stratified into six groups based on brain structures at the entry and target point. Models, both with and without Monte Carlo (MC) dropout, were trained and validated using tenfold cross-validation.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results\u00a0<\/jats:title>\n                <jats:p>mage-based models outperformed features-based models for all groups, and models that predicted <jats:inline-formula><jats:alternatives><jats:tex-math>$$\\mathbf{lu} $$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mi>lu<\/mml:mi>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula> performed better than for <jats:inline-formula><jats:alternatives><jats:tex-math>$$\\hat{\\mathbf{eb }}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mover>\n                      <mml:mi>eb<\/mml:mi>\n                      <mml:mo>^<\/mml:mo>\n                    <\/mml:mover>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula>. Image-based model prediction with MC dropout resulted in lower mean squared error (MSE) with improvements up to 12.9% (<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\mathbf{lu} $$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mi>lu<\/mml:mi>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula>) and 39.9% (<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\hat{\\mathbf{eb }}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mover>\n                      <mml:mi>eb<\/mml:mi>\n                      <mml:mo>^<\/mml:mo>\n                    <\/mml:mover>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula>), compared to no dropout. Using an image of brain tissue types (cortex, white and deep grey matter) resulted in similar, and sometimes better performance, compared to using a T1-weighted MRI when predicting <jats:inline-formula><jats:alternatives><jats:tex-math>$$\\mathbf{lu} $$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mi>lu<\/mml:mi>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula>. When inferring trajectories of image-based models (brain tissue types), 86.9% of trajectories had an MSE<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\le 1$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mrow>\n                      <mml:mo>\u2264<\/mml:mo>\n                      <mml:mn>1<\/mml:mn>\n                    <\/mml:mrow>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula>\u00a0mm.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion\u00a0<\/jats:title>\n                <jats:p>An image-based approach regressing local displacement with an image of brain tissue types resulted in more accurate electrode bending predictions compared to other approaches, inputs, and outputs. Future work will investigate the integration of electrode bending into planning and quality assessment algorithms.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s11548-021-02347-8","type":"journal-article","created":{"date-parts":[[2021,3,24]],"date-time":"2021-03-24T10:02:47Z","timestamp":1616580167000},"page":"789-798","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Patient-specific prediction of SEEG electrode bending for stereotactic neurosurgical planning"],"prefix":"10.1007","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2866-1324","authenticated-orcid":false,"given":"Alejandro","family":"Granados","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuxuan","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Oeslle","family":"Lucena","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vejay","family":"Vakharia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roman","family":"Rodionov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sjoerd B.","family":"Vos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anna","family":"Miserocchi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrew W.","family":"McEvoy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John S.","family":"Duncan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rachel","family":"Sparks","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S\u00e9bastien","family":"Ourselin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,3,24]]},"reference":[{"issue":"9","key":"2347_CR1","doi-asserted-by":"publisher","first-page":"1976","DOI":"10.1109\/TMI.2015.2418298","volume":"34","author":"MJ Cardoso","year":"2015","unstructured":"Cardoso MJ, Modat M, Wolz R, Melbourne A, Cash D, Rueckert D, Ourselin S (2015) Geodesic information flows: spatially-variant graphs and their application to segmentation and fusion. 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