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Due to the poorly defined anatomical boundaries and low signal\u2010to\u2010noise ratio, fully supervised methods for segmentation of the lateral ventricles in 3D US images require a large dataset of annotated images by trained physicians, which is tedious, time\u2010consuming, and expensive. Training fully supervised segmentation methods on a small dataset may lead to overfitting and hence reduce its generalizability. Semi\u2010supervised learning (SSL) methods for 3D US segmentation may be able to address these challenges but most existing SSL methods have been developed for magnetic resonance or computed tomography (CT) images.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To develop a fast, lightweight, and accurate SSL method, specifically for 3D US images, that will use unlabeled data towards improving segmentation performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We propose an SSL framework that leverages the shape\u2010encoding ability of an autoencoder network to enforce complex shape and size constraints on a 3D U\u2010Net segmentation model. The autoencoder created pseudo\u2010labels, based on the 3D U\u2010Net predicted segmentations, that enforces shape constraints. An adversarial discriminator network then determined whether images came from the labeled or unlabeled data distributions. We used 887 3D US images, of which 87 had manually annotated labels and 800 images were unlabeled. Training\/validation\/testing sets of 25\/12\/50, 25\/12\/25 and 50\/12\/25 images were used for model experimentation. The Dice similarity coefficient (DSC), mean absolute surface distance (MAD), and absolute volumetric difference (VD) were used as metrics for comparing to other benchmarks. The baseline benchmark was the fully supervised vanilla 3D U\u2010Net while dual task consistency, shape\u2010aware semi\u2010supervised network, correlation\u2010aware mutual learning, and 3D U\u2010Net Ensemble models were used as state\u2010of\u2010the\u2010art benchmarks with DSC, MAD, and VD as comparison metrics. The Wilcoxon signed\u2010rank test was used to test statistical significance between algorithms for DSC and VD with the threshold being\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.05 and corrected to\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.01 using the Bonferroni correction. The random\u2010access memory (RAM) trace and number of trainable parameters were used to compare the computing efficiency between models.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Relative to the baseline 3D U\u2010Net model, our shape\u2010encoding SSL method reported a mean DSC improvement of 6.5%, 7.7%, and 4.1% with a 95% confidence interval of 4.2%, 5.7%, and 2.1% using image data splits of 25\/12\/50, 25\/12\/25, and 50\/12\/25, respectively. Our method only used a 1GB increase in RAM compared to the baseline 3D U\u2010Net and required less than half the RAM and trainable parameters compared to the 3D U\u2010Net ensemble method.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Based on our extensive literature survey, this is one of the first reported works to propose an SSL method designed for segmenting organs in 3D US images and specifically one that incorporates unlabeled data for segmenting neonatal cerebral lateral ventricles. When compared to the state\u2010of\u2010the\u2010art SSL and fully supervised learning methods, our method yielded the highest DSC and lowest VD while being computationally efficient.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.17242","type":"journal-article","created":{"date-parts":[[2024,6,10]],"date-time":"2024-06-10T18:05:15Z","timestamp":1718042715000},"page":"6134-6148","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Semi\u2010supervised learning framework with shape encoding for neonatal ventricular segmentation from 3D ultrasound"],"prefix":"10.1002","volume":"51","author":[{"given":"Zachary","family":"Szentimrey","sequence":"first","affiliation":[{"name":"School of Engineering University of Guelph  Guelph Ontario Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdullah","family":"Al\u2010Hayali","sequence":"additional","affiliation":[{"name":"School of Engineering University of Guelph  Guelph Ontario Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sandrine","family":"de Ribaupierre","sequence":"additional","affiliation":[{"name":"Department of Clinical Neurological Sciences London Health Sciences Centre  London Ontario Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aaron","family":"Fenster","sequence":"additional","affiliation":[{"name":"Robarts Research Institute University of Western Ontario  London Ontario Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eranga","family":"Ukwatta","sequence":"additional","affiliation":[{"name":"School of Engineering University of Guelph  Guelph Ontario Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,6,10]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031\u20109155\/58\/21\/7513"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1117\/1.jmi.5.2.026001"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1067\/mpd.2001.111822"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1136\/fn.87.1.f37"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ultrasmedbio.2015.11.010"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ultrasmedbio.2004.07.017"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1651\u20102227.2010.01830.x"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1117\/1.jmi.3.4.046003"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598\u2010020\u201080783\u20103"},{"key":"e_1_2_8_11_1","doi-asserted-by":"crossref","unstructured":"MartinM SciollaB SdikaM WangX QuetinP DelachartreP Automatic segmentation of the cerebral ventricle in neonates using deep learning with 3D reconstructed freehand ultrasound imaging. 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