{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T14:08:24Z","timestamp":1769350104446,"version":"3.49.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684802","type":"print"},{"value":"9781643684819","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T00:00:00Z","timestamp":1705017600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,1,12]]},"abstract":"<jats:p>Alzheimer\u2019s disease will lead to the atrophy of the Hippocampus. In order to recognize the position changes of Hippocampus, improve the image contour quality, further improve the accuracy of convolution, and achieve the purpose of accurately extracting image information, convolutional neural network is introduced to recognize the Hippocampus region with brain magnetic resonance imaging, and a method combining multi-level 3D U-NET is proposed based on single-stage U-NET. The results showed that this model could enhance the segmentation performance, significantly improved the segmentation accuracy which had certain clinical significance for the brain to recognize the Hippocampus and the automatic discrimination of Alzheimer\u2019s disease.<\/jats:p>","DOI":"10.3233\/faia231191","type":"book-chapter","created":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T12:55:44Z","timestamp":1705064144000},"source":"Crossref","is-referenced-by-count":1,"title":["Convolutional Neural Network Image Segmentation of Alzheimer\u2019s Disease Based on Multi-Order 3D U-NET"],"prefix":"10.3233","author":[{"given":"Ningsen","family":"Sun","sequence":"first","affiliation":[{"name":"School of Medical Information and Engineering, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai\u2019an, 271016, China"}]},{"given":"Yuan","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Medical Information and Engineering, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai\u2019an, 271016, China"}]},{"given":"Lanhua","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Medical Information and Engineering, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai\u2019an, 271016, China"}]},{"given":"Zhongdong","family":"Han","sequence":"additional","affiliation":[{"name":"School of Medical Information and Engineering, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai\u2019an, 271016, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Electronics, Communications and Networks"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA231191","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T12:55:45Z","timestamp":1705064145000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA231191"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,12]]},"ISBN":["9781643684802","9781643684819"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia231191","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,12]]}}}