{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T18:12:32Z","timestamp":1790273552574,"version":"4.1.0"},"reference-count":20,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2025,5,25]],"date-time":"2025-05-25T00:00:00Z","timestamp":1748131200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,5,25]],"date-time":"2025-05-25T00:00:00Z","timestamp":1748131200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100006302","name":"Universidad de Alcal\u00e1","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100006302","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>\n                      <jats:bold>Purpose<\/jats:bold>\n                    <\/jats:title>\n                    <jats:p>Minimally-invasive surgery (MIS) reduces the trauma compared to open surgery but is challenging for endophytic lesion localisation. Augmented reality (AR) is a promising assistance, which superimposes a preoperative 3D lesion model onto the MIS images. It requires solving the difficult problem of 3D model to MIS image registration. We propose MIS-NeRF, a neural radiance field (NeRF) which provides high-fidelity intraoperative 3D reconstruction, used to bootstrap iterative closest point (ICP) registration.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>\n                      <jats:bold>Methods<\/jats:bold>\n                    <\/jats:title>\n                    <jats:p>Existing NeRF methods break down in MIS because of the moving light source and specular highlights. The proposed MIS-NeRF is adapted to these conditions. First, it incorporates the camera centre as an additional input to the radiance function, which allows MIS-NeRF to handle the moving light source. Second, it uses a modified volume rendering which handles specular highlights. Third, it uses a regularised compound loss to enhance surface reconstruction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>\n                      <jats:bold>Results<\/jats:bold>\n                    <\/jats:title>\n                    <jats:p>\n                      MIS-NeRF was tested on three synthetic datasets and retrospectively on four laparoscopic surgeries. It successfully reconstructed high-fidelity liver and uterus surfaces, reducing common artefacts including high-frequency noise and bumps caused by specular highlights. ICP registration achieved faithful alignment between the preoperative and intraoperative 3D models, with an average error of 3.25\u00a0mm, outperforming the second-best method by a\n                      <jats:inline-formula>\n                        <jats:alternatives>\n                          <jats:tex-math>$$15\\%$$<\/jats:tex-math>\n                          <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                            <mml:mrow>\n                              <mml:mn>15<\/mml:mn>\n                              <mml:mo>%<\/mml:mo>\n                            <\/mml:mrow>\n                          <\/mml:math>\n                        <\/jats:alternatives>\n                      <\/jats:inline-formula>\n                      margin.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>\n                      <jats:bold>Conclusion<\/jats:bold>\n                    <\/jats:title>\n                    <jats:p>MIS-NeRF improves AR-based lesion localisation by facilitating accurate 3D model registration to multiple MIS images.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s11548-025-03429-7","type":"journal-article","created":{"date-parts":[[2025,5,25]],"date-time":"2025-05-25T12:29:43Z","timestamp":1748176183000},"page":"1481-1490","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["MIS-NeRF: neural radiance fields in minimally-invasive surgery"],"prefix":"10.1007","volume":"20","author":[{"given":"Samad Barri","family":"Khojasteh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Fuentes-Jimenez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0622-4884","authenticated-orcid":false,"given":"Daniel","family":"Pizarro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yamid","family":"Espinel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adrien","family":"Bartoli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,5,25]]},"reference":[{"key":"3429_CR1","doi-asserted-by":"crossref","unstructured":"Adagolodjo Y, Trivisonne R, Haouchine N, Cotin S, Courtecuisse H (2017) Silhouette-based pose estimation for deformable organs application to surgical augmented reality. 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Our study received ethical approval (IRB00008526-2019-CE58) issued by CPP Sud-Est VI in Clermont-Ferrand, France.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"The source code and materials used in this study are publicly available at\n                      \n                      .","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code and datasets"}}]}}