{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T04:08:14Z","timestamp":1748750894176,"version":"3.41.0"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,5,31]],"date-time":"2025-05-31T00:00:00Z","timestamp":1748649600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,5,31]],"date-time":"2025-05-31T00:00:00Z","timestamp":1748649600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100013165","name":"Otto A. Malm Lahjoitusrahasto","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100013165","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Sakari Alhopuron s\u00e4\u00e4ti\u00f6"},{"DOI":"10.13039\/100008723","name":"Suomen L\u00e4\u00e4ketieteen S\u00e4\u00e4ti\u00f6","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100008723","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005609","name":"University of Turku","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100005609","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Netw Model Anal Health Inform Bioinforma"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Background<\/jats:title>\n            <jats:p>\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:mmultiscripts>\n                      <mml:mrow\/>\n                      <mml:mrow\/>\n                      <mml:mn>15<\/mml:mn>\n                    <\/mml:mmultiscripts>\n                  <\/mml:math>\n                <\/jats:alternatives>\n              <\/jats:inline-formula>O-water positron emission tomography (PET) imaging enables noninvasive quantification of renal blood flow. While there are several existing methods automatically locating the volume of interest (VOI) for a kidney, separate cortex and medulla VOIs are needed for PET modeling due their functional differences. To assist in this, we introduce a new three-dimensional segmentation algorithm for kidney.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Materials and methods<\/jats:title>\n            <jats:p>Our algorithm chooses an initial kidney VOI, finds its longitudinal axis and the direction of the non-convex part, and then creates cortex and medulla VOIs and removes the renal vessel area from them. We evaluated the algorithm by using it to define cortex and medulla VOIs of left and right kidneys in dynamic total-body <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:mmultiscripts>\n                      <mml:mrow\/>\n                      <mml:mrow\/>\n                      <mml:mn>15<\/mml:mn>\n                    <\/mml:mmultiscripts>\n                  <\/mml:math>\n                <\/jats:alternatives>\n              <\/jats:inline-formula>O-water PET images of 35 human patients. For all the 70 kidneys, we plotted the cross-section of the VOIs from three different anatomic directions and asked two expert physicians to assess their quality. Additionally, we computed cortical and medullary renal blood flow estimates by fitting a compartment model to the mean time-activity curves of our VOIs.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>According to the evaluation by the physicians, the cortex and medulla VOIs were mostly correct in all three directions for 78.6% of the total 70 kidneys and correct in at least one direction for 94.3% of the kidneys. The segmentation inaccuracies were typically caused by the algorithm placing cortex VOI partially outside of the kidney or in the medulla. However, regardless of these inaccuracies, all the VOIs were accurate enough to be used for compartment modeling. The resulting cortical and medullary blood flow were very close to the values reported in earlier studies with similar patient populations.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>Our proposed algorithm can be used to create an automatic pipeline for accurate quantification of cortical and medullary tracer perfusion after a PET scan.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1007\/s13721-025-00534-0","type":"journal-article","created":{"date-parts":[[2025,5,31]],"date-time":"2025-05-31T04:41:38Z","timestamp":1748666498000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["New 3D segmentation algorithm for modeling of kidney in positron emission tomography images"],"prefix":"10.1007","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7775-7656","authenticated-orcid":false,"given":"Oona","family":"Rainio","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8786-9160","authenticated-orcid":false,"given":"Aino","family":"Latva-Rasku","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0368-3746","authenticated-orcid":false,"given":"Jussi","family":"Hirvonen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3156-9593","authenticated-orcid":false,"given":"Juhani","family":"Knuuti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0982-8360","authenticated-orcid":false,"given":"Riku","family":"Kl\u00e9n","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,5,31]]},"reference":[{"key":"534_CR1","doi-asserted-by":"publisher","first-page":"6582","DOI":"10.1007\/s00330-020-07008-z","volume":"30","author":"M Arif","year":"2020","unstructured":"Arif M et al (2020) Clinically significant prostate cancer detection and segmentation in low-risk patients using a convolutional neural network on multi-parametric MRI. 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