{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T08:54:47Z","timestamp":1791968087379,"version":"4.3.4"},"reference-count":33,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,11,29]],"date-time":"2021-11-29T00:00:00Z","timestamp":1638144000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2021,11,29]],"date-time":"2021-11-29T00:00:00Z","timestamp":1638144000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2022,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The prevalence of thyroid diseases has been increasing year by year. In this study, we established and validated a deep learning method (Cascade region\u2010based convolutional neural network, R\u2010CNN) based on ultrasound videos for automatic detection and segmentation of the thyroid gland and its surrounding tissues in order to reduce the workload of radiologists and improve the detection and diagnosis rate of thyroid disease.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Seventy\u2010one patients with normal thyroid ultrasound were included. The ultrasound videos of 59 patients were used as the training dataset, the data of 12 patients were used as the validation dataset, and in addition, the data of 9 patents were used as the testing dataset. Ultrasound videos of thyroid examination, including five standard sections (left and right lobe transverse scan, central isthmus transverse scan, left and right lobe longitudinal scan), were collected from all patients. The radiologists labeled the neck tissues, including anterior cervical muscle, cricoid cartilage, trachea, thyroid gland, endothyroid vessels, carotid artery, internal jugular vein, and esophagus. A large dataset was constructed to train and test the deep learning method. The performance was evaluated using the COCO metrics AP, AP50, and AP75. We compared the Cascade R\u2010CNN with a state\u2010of\u2010the\u2010art method CenterMask in the test dataset.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      We annotated 166817, 34364, and 29227 regions in training, validation and testing samples. The model could achieve a good detection performance for the thyroid left lobe, right lobe, isthmus, muscles, trachea, carotid artery, and jugular vein; the AP\n                      <jats:sub>50<\/jats:sub>\n                      of these tissues were 86.5%, 87.5%, 89.1%, 96.1%, 96.6%, 97.7%, and 91.8%, respectively. In addition, the model showed good segmentation performance for the muscles, trachea, and carotid artery; the AP\n                      <jats:sub>50<\/jats:sub>\n                      of these tissues were 96%, 96.6%, and 97.8%, respectively. For the left lobe, right lobe, isthmus, esophagus, and jugular vein, AP\n                      <jats:sub>50<\/jats:sub>\n                      was \u226586%. However, the segmentation results for the cricoid cartilage and endothyroid vessels were not high (AP\n                      <jats:sub>50<\/jats:sub>\n                      of 53.9% and 48.5%, respectively). For fair comparison, the performance of Cascade R\u2010CNN is better than that of CenterMask for detection and segmentation tasks. The difference was statistically significant (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.05).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The new method could successfully detect and segment the thyroid gland and its surrounding tissues.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.15332","type":"journal-article","created":{"date-parts":[[2021,11,3]],"date-time":"2021-11-03T07:13:26Z","timestamp":1635923606000},"page":"382-392","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Deep learning\u2010based ultrasonic dynamic video detection and segmentation of thyroid gland and its surrounding cervical soft tissues"],"prefix":"10.1002","volume":"49","author":[{"given":"Hongxia","family":"Luo","sequence":"first","affiliation":[{"name":"Department of Ultrasonic Diagnosis, Shenzhen Maternity and Child Healthcare Hospital, Cheeloo College of Medicine Shandong University  Shenzhen Guangdong 518000 China"},{"name":"Department of Ultrasonic Diagnosis, The Second Affiliated Hospital and Yuying Children's Hospital Wenzhou Medical University  Wenzhou Zhejiang 325000 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laifa","family":"Ma","sequence":"additional","affiliation":[{"name":"The College of Computer Science and Electronic Engineering Hunan University  Changsha Hunan 410082 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangqiong","family":"Wu","sequence":"additional","affiliation":[{"name":"The College of Computer Science and Electronic Engineering Hunan University  Changsha Hunan 410082 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guanghua","family":"Tan","sequence":"additional","affiliation":[{"name":"The College of Computer Science and Electronic Engineering Hunan University  Changsha Hunan 410082 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Ultrasonic Diagnosis, The Second Affiliated Hospital and Yuying Children's Hospital Wenzhou Medical University  Wenzhou Zhejiang 325000 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Senmin","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Ultrasonic Diagnosis, The Second Affiliated Hospital and Yuying Children's Hospital Wenzhou Medical University  Wenzhou Zhejiang 325000 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kenli","family":"Li","sequence":"additional","affiliation":[{"name":"The College of Computer Science and Electronic Engineering Hunan University  Changsha Hunan 410082 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Ultrasonic Diagnosis, The Second Affiliated Hospital and Yuying Children's Hospital Wenzhou Medical University  Wenzhou Zhejiang 325000 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shengli","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Ultrasonic Diagnosis, Shenzhen Maternity and Child Healthcare Hospital, Cheeloo College of Medicine Shandong University  Shenzhen Guangdong 518000 China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,11,29]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1089\/thy.2019.0067"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1089\/thy.2015.0020"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2013.6609709"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/jcu.20689"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2017.11.018"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598\u2010018\u201025005\u20107"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmu.2015.10.002"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12957\u2010019\u20101558\u2010z"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1089\/thy.2016.0372"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14301"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacr.2017.01.046"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2015.2492476"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/8087624"},{"key":"e_1_2_9_15_1","doi-asserted-by":"crossref","unstructured":"CicekO AbdulkadirA LienkampSS BroxT&RonnebergerO3D U\u2010Net: learning dense volumetric segmentation from sparse annotation. 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