{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T21:55:21Z","timestamp":1777067721306,"version":"3.51.4"},"reference-count":19,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2021,6,22]],"date-time":"2021-06-22T00:00:00Z","timestamp":1624320000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,6,22]],"date-time":"2021-06-22T00:00:00Z","timestamp":1624320000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Scuola Superiore Sant\u2019Anna"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"published-print":{"date-parts":[[2021,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background and objectives<\/jats:title>\n                <jats:p>Fetal head-circumference (HC) measurement from ultrasound (US) images provides useful hints for assessing fetal growth. Such measurement is performed manually during the actual clinical practice, posing issues relevant to intra- and inter-clinician variability. This work presents a fully automatic, deep-learning-based approach to HC delineation, which we named Mask-R<jats:inline-formula><jats:alternatives><jats:tex-math>$$^{2}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:msup>\n                      <mml:mrow\/>\n                      <mml:mn>2<\/mml:mn>\n                    <\/mml:msup>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula>CNN. It advances our previous work in the field and performs HC distance-field regression in an end-to-end fashion, without requiring a priori HC localization nor any postprocessing for outlier removal.\n<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>Mask-R<jats:inline-formula><jats:alternatives><jats:tex-math>$$^{2}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:msup>\n                      <mml:mrow\/>\n                      <mml:mn>2<\/mml:mn>\n                    <\/mml:msup>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula>CNN follows the Mask-RCNN architecture, with a backbone inspired by feature-pyramid networks, a region-proposal network and the ROI align. The Mask-RCNN segmentation head is here modified to regress the HC distance field.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Mask-R<jats:inline-formula><jats:alternatives><jats:tex-math>$$^{2}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:msup>\n                      <mml:mrow\/>\n                      <mml:mn>2<\/mml:mn>\n                    <\/mml:msup>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula>CNN was tested on the <jats:italic>HC18 Challenge<\/jats:italic> dataset, which consists of 999 training and 335 testing images. With a comprehensive ablation study, we showed that Mask-R<jats:inline-formula><jats:alternatives><jats:tex-math>$$^{2}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:msup>\n                      <mml:mrow\/>\n                      <mml:mn>2<\/mml:mn>\n                    <\/mml:msup>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula>CNN achieved a mean absolute difference of 1.95\u00a0mm (standard deviation\u00a0<jats:inline-formula><jats:alternatives><jats:tex-math>$$=\\pm 1.92$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mrow>\n                      <mml:mo>=<\/mml:mo>\n                      <mml:mo>\u00b1<\/mml:mo>\n                      <mml:mn>1.92<\/mml:mn>\n                    <\/mml:mrow>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula>\u00a0mm), outperforming other approaches in the literature.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>With this work, we proposed an end-to-end model for HC distance-field regression. With our experimental results, we showed that Mask-R<jats:inline-formula><jats:alternatives><jats:tex-math>$$^{2}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:msup>\n                      <mml:mrow\/>\n                      <mml:mn>2<\/mml:mn>\n                    <\/mml:msup>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula>CNN may be an effective support for clinicians for assessing fetal growth.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s11548-021-02430-0","type":"journal-article","created":{"date-parts":[[2021,6,22]],"date-time":"2021-06-22T13:02:47Z","timestamp":1624366967000},"page":"1711-1718","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Mask-R$$^{2}$$CNN: a distance-field regression version of Mask-RCNN for fetal-head delineation in ultrasound images"],"prefix":"10.1007","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4494-8907","authenticated-orcid":false,"given":"Sara","family":"Moccia","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maria Chiara","family":"Fiorentino","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emanuele","family":"Frontoni","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,6,22]]},"reference":[{"issue":"8","key":"2430_CR1","doi-asserted-by":"publisher","first-page":"e0200,412","DOI":"10.1371\/journal.pone.0200412","volume":"13","author":"TL van den Heuvel","year":"2018","unstructured":"van den Heuvel TL, de Bruijn D, de Korte CL, van Ginneken B (2018) Automated measurement of fetal head circumference using 2D ultrasound images. 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