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First, the horizontal-vertical interaction mechanism facilitates complex communication paths in the vertical and horizontal dimensions, and it has the ability to capture a wide range of context dependencies. Second, the feature-fused unit is introduced to adjust the network's receptive field, which enhances the ability of acquiring multi-scale context information. Third, the multiple side-outputs strategy intelligently combines feature maps to generate more accurate and detailed change maps. Finally, experiments were carried out on the self-established jaw cyst dataset and compared with different specialist physicians to evaluate its clinical usability. The research results indicate that the Matthews correlation coefficient (Mcc), Dice, and Jaccard of HIMS-Net were 93.61, 93.66 and 88.10% respectively, which may contribute to rapid and accurate diagnosis in clinical practice.&lt;\/p&gt;\n\n\t      &lt;\/abstract&gt;<\/jats:p>","DOI":"10.3934\/mbe.2024178","type":"journal-article","created":{"date-parts":[[2024,2,23]],"date-time":"2024-02-23T09:01:32Z","timestamp":1708678892000},"page":"4036-4055","source":"Crossref","is-referenced-by-count":1,"title":["HIMS-Net: Horizontal-vertical interaction and multiple side-outputs network for cyst segmentation in jaw images"],"prefix":"10.3934","volume":"21","author":[{"given":"Xiaoliang","family":"Jiang","sequence":"first","affiliation":[{"name":"College of Mechanical Engineering, Quzhou University, Quzhou 324000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huixia","family":"Zheng","sequence":"additional","affiliation":[{"name":"Department of Stomatology, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou 324000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenfei","family":"Yuan","sequence":"additional","affiliation":[{"name":"Department of Stomatology, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou 324000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kun","family":"Lan","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Quzhou University, Quzhou 324000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaoyang","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Science, University of Macau, Macau 999078, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"2321","reference":[{"key":"key-10.3934\/mbe.2024178-1","doi-asserted-by":"publisher","unstructured":"P. Wang, J. Z. Peng, M. Pedersoli, Y. F. Zhou, C. M. Zhang, C. Desrosiers, CAT: Constrained adversarial training for anatomically-plausible semi-supervised segmentation, <i>IEEE Trans. Med. Imaging<\/i>, <b>42<\/b> (2023), 2146\u20132161. https:\/\/doi.org\/10.1109\/TMI.2023.3243069","DOI":"10.1109\/TMI.2023.3243069"},{"key":"key-10.3934\/mbe.2024178-2","doi-asserted-by":"publisher","unstructured":"L. Zhang, K. J. Zhang, H. W. Pan, SUNet plus plus: A deep network with channel attention for small-scale object segmentation on 3D medical images, <i>Tsinghua Sci. Technol<\/i>., <b>28<\/b> (2023), 628\u2013638. https:\/\/doi.org\/10.26599\/TST.2022.9010023","DOI":"10.26599\/TST.2022.9010023"},{"key":"key-10.3934\/mbe.2024178-3","doi-asserted-by":"publisher","unstructured":"D. D. Meng, S. Li, B. Sheng, H. Wu, S. Q. Tian, W. J. 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Bioinf.<\/i>, <b>20<\/b> (2023), 2494\u20132505. https:\/\/doi.org\/10.1109\/TCBB.2022.3188148","DOI":"10.1109\/TCBB.2022.3188148"},{"key":"key-10.3934\/mbe.2024178-6","unstructured":"O. Ronneberger, P. Fischer, T. Brox, U-net: Convolutional networks for biomedical image segmentation, in <i>International Conference on Medical Image Computing and Computer-assisted Intervention<\/i>, Munich, Germany, (2015), 234\u2013241. <ext-link ext-link-type=\"uri\" xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28\">https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28<\/ext-link>"},{"key":"key-10.3934\/mbe.2024178-7","doi-asserted-by":"publisher","unstructured":"A. Sharma, P. K. Mishra, DRI-UNet: dense residual-inception UNet for nuclei identification in microscopy cell images, <i>Neural Comput. 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Wu, UNet 3+: A full-scale connected UNet for medical image segmentation, in <i>IEEE International Conference on Acoustics, Speech and Signal Processing<\/i>, Barcelona, Spain, (2020), 1055\u20131059. <ext-link ext-link-type=\"uri\" xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/doi.org\/10.1109\/ICASSP40776.2020.9053405\">https:\/\/doi.org\/10.1109\/ICASSP40776.2020.9053405<\/ext-link>"},{"key":"key-10.3934\/mbe.2024178-34","doi-asserted-by":"publisher","unstructured":"K. Wang, S. Liang, S. Zhong, Q. Feng, Y. Zhang, Breast ultrasound image segmentation: A coarse-to-fine fusion convolutional neural network, <i>Med. Phys<\/i>., <b>48<\/b> (2021), 4262\u20134278. https:\/\/doi.org\/10.1002\/mp.15006","DOI":"10.1002\/mp.15006"},{"key":"key-10.3934\/mbe.2024178-35","doi-asserted-by":"publisher","unstructured":"J. Wang, P. Lv, H. Wang, C. Shi, SAR-U-Net: Squeeze-and-excitation block and atrous spatial pyramid pooling based residual U-Net for automatic liver ct segmentation, <i>Comput. Methods Programs Biomed.<\/i>, <b>208<\/b> (2021), 106268. https:\/\/doi.org\/10.1016\/j.cmpb.2021.106268","DOI":"10.1016\/j.cmpb.2021.106268"},{"key":"key-10.3934\/mbe.2024178-36","doi-asserted-by":"publisher","unstructured":"G. Xiao, B. Zhu, Y. Zhang, H. Gao, FCSNet: A quantitative explanation method for surface scratch defects during belt grinding based on deep learning, <i>Comput. Ind<\/i>., <b>144<\/b> (2023), 103793. https:\/\/doi.org\/10.1016\/j.compind.2022.103793","DOI":"10.1016\/j.compind.2022.103793"},{"key":"key-10.3934\/mbe.2024178-37","doi-asserted-by":"publisher","unstructured":"A. Iqbal, M. Sharif, M. A. Khan, W. Nisar, M. Alhaisoni, FF-UNet: A u-shaped deep convolutional neural network for multimodal biomedical image segmentation, <i>Cognit. 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