{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:28:39Z","timestamp":1750220919537,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":19,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,10,23]],"date-time":"2019-10-23T00:00:00Z","timestamp":1571788800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,10,23]]},"DOI":"10.1145\/3369166.3369188","type":"proceedings-article","created":{"date-parts":[[2020,1,14]],"date-time":"2020-01-14T04:20:27Z","timestamp":1578975627000},"page":"40-46","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-modal Image Fusion based Anatomical Shape Model for Low-contrast Anterior Visual Pathway and Medial Rectus Muscle Segmentation in CT Images"],"prefix":"10.1145","author":[{"given":"Guoyu","family":"Hu","sequence":"first","affiliation":[{"name":"Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Electronics, Beijing Institute of Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianjun","family":"Zhu","sequence":"additional","affiliation":[{"name":"Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Electronics, Beijing Institute of Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yining","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Southeast University, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Interventional Ultrasound, Chinese PLA General Hospital, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ping","family":"Liang","sequence":"additional","affiliation":[{"name":"Department of Interventional Ultrasound, Chinese PLA General Hospital, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Optics and Electronics, Beijing Institute of Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,1,13]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Automated Surgical Approach Planning for Complex Skull Base Targets: Development and Validation of a Cost Function and Semantic At-las[J]. Surgical innovation","author":"Aghdasi N","year":"2018","unstructured":"Aghdasi N , Whipple M , Humphreys I M , Automated Surgical Approach Planning for Complex Skull Base Targets: Development and Validation of a Cost Function and Semantic At-las[J]. Surgical innovation , 2018 , 25(5): 476--484. Aghdasi N, Whipple M, Humphreys I M, et al. Automated Surgical Approach Planning for Complex Skull Base Targets: Development and Validation of a Cost Function and Semantic At-las[J]. Surgical innovation, 2018, 25(5): 476--484."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/aacb65"},{"key":"e_1_3_2_1_3_1","volume-title":"Atlas pre-selection strategies to enhance the efficiency and accuracy of multi-atlas brain segmentation tools[J]. PloS one","author":"Ye C","year":"2018","unstructured":"Ye C , Ma T , Wu D , Atlas pre-selection strategies to enhance the efficiency and accuracy of multi-atlas brain segmentation tools[J]. PloS one , 2018 , 13(7): e0200294. Ye C, Ma T, Wu D, et al. Atlas pre-selection strategies to enhance the efficiency and accuracy of multi-atlas brain segmentation tools[J]. PloS one, 2018, 13(7): e0200294."},{"key":"e_1_3_2_1_4_1","volume-title":"Atlas-based shape analysis and classification of retinal optical coherence tomography images using the functional shape (fshape) framework[J]. Medical image analysis","author":"Lee S","year":"2017","unstructured":"Lee S , Charon N , Charlier B , Atlas-based shape analysis and classification of retinal optical coherence tomography images using the functional shape (fshape) framework[J]. Medical image analysis , 2017 , 35: 570--581. Lee S, Charon N, Charlier B, et al. Atlas-based shape analysis and classification of retinal optical coherence tomography images using the functional shape (fshape) framework[J]. Medical image analysis, 2017, 35: 570--581."},{"volume-title":"Simultaneous total intracranial","author":"Huo Y","key":"e_1_3_2_1_5_1","unstructured":"Huo Y , Asman A J , Plassard A J , Simultaneous total intracranial volume and posterior fossa volume estimation using multi-atlas label fusion[J]. Human brain mapping, 2017 , 38(2): 599--616. Huo Y, Asman A J, Plassard A J, et al. Simultaneous total intracranial volume and posterior fossa volume estimation using multi-atlas label fusion[J]. Human brain mapping, 2017, 38(2): 599--616."},{"key":"e_1_3_2_1_6_1","first-page":"725916","article-title":"Automatic segmentation of the optic nerves and chiasm in CT and MR using the atlas-navigated optimal medial axis and deformable-model algorithm[C]\/\/Medical Imaging 2009","volume":"7259","author":"Noble J H","year":"2009","unstructured":"Noble J H , Dawant B M . Automatic segmentation of the optic nerves and chiasm in CT and MR using the atlas-navigated optimal medial axis and deformable-model algorithm[C]\/\/Medical Imaging 2009 : Image Processing. International Society for Optics and Photonics , 2009 , 7259 : 725916 . Noble J H, Dawant B M. Automatic segmentation of the optic nerves and chiasm in CT and MR using the atlas-navigated optimal medial axis and deformable-model algorithm[C]\/\/Medical Imaging 2009: Image Processing. International Society for Optics and Photonics, 2009, 7259: 725916.","journal-title":"Image Processing. International Society for Optics and Photonics"},{"key":"e_1_3_2_1_7_1","first-page":"109530P","article-title":"Coupled active shape models for automated segmentation and landmark localization in high-resolution CT of the foot and ankle[C]\/\/Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging","volume":"10953","author":"Brehler M","year":"2019","unstructured":"Brehler M , Islam A , Vogelsang L , Coupled active shape models for automated segmentation and landmark localization in high-resolution CT of the foot and ankle[C]\/\/Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging . International Society for Optics and Photonics , 2019 , 10953 : 109530P . Brehler M, Islam A, Vogelsang L, et al. Coupled active shape models for automated segmentation and landmark localization in high-resolution CT of the foot and ankle[C]\/\/Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging. International Society for Optics and Photonics, 2019, 10953: 109530P.","journal-title":"International Society for Optics and Photonics"},{"key":"e_1_3_2_1_8_1","first-page":"1490","article-title":"Computerized radiogrammetry of third metacarpal using watershed and active appearance model[C]\/\/2018 IEEE International Conference on Industrial Technology (ICIT)","volume":"2018","author":"Areeckal A S","unstructured":"Areeckal A S , Sam M , David S S . Computerized radiogrammetry of third metacarpal using watershed and active appearance model[C]\/\/2018 IEEE International Conference on Industrial Technology (ICIT) . IEEE , 2018 : 1490 -- 1495 . Areeckal A S, Sam M, David S S. Computerized radiogrammetry of third metacarpal using watershed and active appearance model[C]\/\/2018 IEEE International Conference on Industrial Technology (ICIT). IEEE, 2018: 1490--1495.","journal-title":"IEEE"},{"key":"e_1_3_2_1_9_1","volume-title":"Geometrical model-based segmentation of the organs of sight on CT images[J]. Medical physics","author":"Bekes G","year":"2008","unstructured":"Bekes G , M\u00e1t\u00e9 E , Ny\u00fal L G , Geometrical model-based segmentation of the organs of sight on CT images[J]. Medical physics , 2008 , 35(2): 735--743. Bekes G, M\u00e1t\u00e9 E, Ny\u00fal L G, et al. Geometrical model-based segmentation of the organs of sight on CT images[J]. Medical physics, 2008, 35(2): 735--743."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1117\/12.2255580"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2535222"},{"key":"e_1_3_2_1_12_1","volume-title":"Head and Neck Auto-Segmentation Challenge (MICCAI)","author":"Chen A.","year":"2015","unstructured":"Chen , A. and B.M. Dawant , A multi-atlas approach for the automatic segmentation of multiple structures in head and neck CT images . Head and Neck Auto-Segmentation Challenge (MICCAI) , Munich , 2015 . Chen, A. and B.M. Dawant, A multi-atlas approach for the automatic segmentation of multiple structures in head and neck CT images. Head and Neck Auto-Segmentation Challenge (MICCAI), Munich, 2015."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.4.3.034501"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2018.04.001"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.irbm.2015.06.001"},{"key":"e_1_3_2_1_16_1","volume-title":"Interleaved 3D-CNN s for joint segmentation of small-volume structures in head and neck CT images[J]. Medical physics","author":"Ren X","year":"2018","unstructured":"Ren X , Xiang L , Nie D , Interleaved 3D-CNN s for joint segmentation of small-volume structures in head and neck CT images[J]. Medical physics , 2018 , 45(5): 2063--2075. Ren X, Xiang L, Nie D, et al. Interleaved 3D-CNN s for joint segmentation of small-volume structures in head and neck CT images[J]. Medical physics, 2018, 45(5): 2063--2075."},{"key":"e_1_3_2_1_17_1","volume-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation. in International Conference on Medical Image Computing & Computer-assisted Intervention.","author":"Ronneberger O., P.","year":"2015","unstructured":"Ronneberger , O., P. Fischer and T. Brox . U-Net: Convolutional Networks for Biomedical Image Segmentation. in International Conference on Medical Image Computing & Computer-assisted Intervention. 2015 . Ronneberger, O., P. Fischer and T. Brox. U-Net: Convolutional Networks for Biomedical Image Segmentation. in International Conference on Medical Image Computing & Computer-assisted Intervention. 2015."},{"key":"e_1_3_2_1_18_1","first-page":"97840R","article-title":"Fast correspondences for statistical shape models of brain structures[C]\/\/Medical Imaging 2016","volume":"9784","author":"Bernard F","year":"2016","unstructured":"Bernard F , Vlassis N , Gemmar P , Fast correspondences for statistical shape models of brain structures[C]\/\/Medical Imaging 2016 : Image Processing. International Society for Optics and Photonics , 2016 , 9784 : 97840R . Bernard F, Vlassis N, Gemmar P, et al. Fast correspondences for statistical shape models of brain structures[C]\/\/Medical Imaging 2016: Image Processing. International Society for Optics and Photonics, 2016, 9784: 97840R.","journal-title":"Image Processing. International Society for Optics and Photonics"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"crossref","unstructured":"Braga J Dumoncel J Duployer B etal The Kromdraai hominins revisited with an updated portrayal of differences between Australopithecus africanus and Paranthropus robustus[M]\/\/Kromdraai. A birthplace of Paranthropus in the cradle of humankind. Sun Press Johannesburg 2016:  49--68.  Braga J Dumoncel J Duployer B et al. The Kromdraai hominins revisited with an updated portrayal of differences between Australopithecus africanus and Paranthropus robustus[M]\/\/Kromdraai. A birthplace of Paranthropus in the cradle of humankind. Sun Press Johannesburg 2016: 49--68.","DOI":"10.18820\/9781928355076"}],"event":{"name":"ICBBS 2019: 2019 8th International Conference on Bioinformatics and Biomedical Science","sponsor":["Beijing University of Technology","Harbin Inst. Technol. Harbin Institute of Technology"],"location":"Beijing China","acronym":"ICBBS 2019"},"container-title":["Proceedings of the 2019 8th International Conference on Bioinformatics and Biomedical Science"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3369166.3369188","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3369166.3369188","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T23:53:06Z","timestamp":1750204386000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3369166.3369188"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10,23]]},"references-count":19,"alternative-id":["10.1145\/3369166.3369188","10.1145\/3369166"],"URL":"https:\/\/doi.org\/10.1145\/3369166.3369188","relation":{},"subject":[],"published":{"date-parts":[[2019,10,23]]},"assertion":[{"value":"2020-01-13","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}