{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T11:08:00Z","timestamp":1751368080263,"version":"3.40.5"},"reference-count":24,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T00:00:00Z","timestamp":1631232000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Scientific Programming"],"published-print":{"date-parts":[[2021,9,10]]},"abstract":"<jats:p>To study the influence of different sequences of magnetic resonance imaging (MRI) images on the segmentation of hepatocellular carcinoma (HCC) lesions, the U-Net was improved. Moreover, deep fusion network (DFN), data enhancement strategy, and random data (RD) strategy were introduced, and a multisequence MRI image segmentation algorithm based on DFN was proposed. The segmentation experiments of single-sequence MRI image and multisequence MRI image were designed, and the segmentation result of single-sequence MRI image was compared with those of convolutional neural network (FCN) algorithm. In addition, RD experiment and single-input experiment were also designed. It was found that the sensitivity (0.595\u2009\u00b1\u20090.145) and DSC (0.587\u2009\u00b1\u20090.113) obtained by improved U-Net were significantly higher than the sensitivity (0.405\u2009\u00b1\u20090.098) and DSC (0.468\u2009\u00b1\u20090.115, <jats:inline-formula>\n                     <a:math xmlns:a=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\">\n                        <a:mi>P<\/a:mi>\n                        <a:mo>&lt;<\/a:mo>\n                        <a:mn>0.05<\/a:mn>\n                     <\/a:math>\n                  <\/jats:inline-formula>) obtained by U-Net. The sensitivity of multisequence MRI image segmentation algorithm based on DFN (0.779\u2009\u00b1\u20090.015) was significantly higher than that of FCN algorithm (0.604\u2009\u00b1\u20090.056, <jats:inline-formula>\n                     <c:math xmlns:c=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M2\">\n                        <c:mi>P<\/c:mi>\n                        <c:mo>&lt;<\/c:mo>\n                        <c:mn>0.05<\/c:mn>\n                     <\/c:math>\n                  <\/jats:inline-formula>). The multisequence MRI image segmentation algorithm based on the DFN had higher indicators for liver cancer lesions than those of the improved U-Net. When RD was added, it not only increased the DSC of the single-sequence network enhanced by the hepatocyte-specific magnetic resonance contrast agent (Gd-EOB-DTPA) by 1% but also increased the DSC of the multisequence MRI image segmentation algorithm based on DFN by 7.6%. In short, the improved U-Net can significantly improve the recognition rate of small lesions in liver cancer patients. The addition of RD strategy improved the segmentation indicators of liver cancer lesions of the DFN and can fuse image features of multiple sequences, thereby improving the accuracy of lesion segmentation.<\/jats:p>","DOI":"10.1155\/2021\/4614234","type":"journal-article","created":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T22:20:09Z","timestamp":1631312409000},"page":"1-13","source":"Crossref","is-referenced-by-count":6,"title":["Automatic Segmentation Algorithm of Magnetic Resonance Image in Diagnosis of Liver Cancer Patients under Deep Convolutional Neural Network"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9722-1720","authenticated-orcid":true,"given":"Jinling","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Oncology, Wuhan Fourth Hospital (Puai Hospital, Tongji Medical College, Huazhong University of Science and Technology), Wuhan 430033, Hubei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8733-8845","authenticated-orcid":true,"given":"Jun","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Hepatological Surgery, Wuhan No. 1 Hospital, Wuhan 430000, Hubei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2950-3547","authenticated-orcid":true,"given":"Min","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Emergency, Huangshi Hospital of Traditional Chinese Medicine of Edong Medical Group, Huangshi 435000, Hubei, China"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1007\/s11548-019-01935-z"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.26343"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14659"},{"issue":"11","key":"4","first-page":"344","article-title":"Optimization analysis of urban function regional planning based on big data and gis technology","volume":"55","author":"X. 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