Brain CT registration using hybrid supervised convolutional neural network
Image registration is an essential step in the automated interpretation of the brain computed tomography (CT) images of patients with acute cerebrovascular disease (ACVD). However, performing brain CT registration accurately and rapidly remains greatly challenging due to the large intersubject anato...
Saved in:
| Published in | Biomedical engineering online Vol. 20; no. 1; p. 131 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
BioMed Central Ltd
29.12.2021
BioMed Central BMC |
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | Image registration is an essential step in the automated interpretation of the brain computed tomography (CT) images of patients with acute cerebrovascular disease (ACVD). However, performing brain CT registration accurately and rapidly remains greatly challenging due to the large intersubject anatomical variations, low resolution of soft tissues, and heavy computation costs. To this end, the HSCN-Net, a hybrid supervised convolutional neural network, was developed for precise and fast brain CT registration.
HSCN-Net generated synthetic deformation fields using a simulator as one supervision for one reference-moving image pair to address the problem of lack of gold standards. Furthermore, the simulator was designed to generate multiscale affine and elastic deformation fields to overcome the registration challenge posed by large intersubject anatomical deformation. Finally, HSCN-Net adopted a hybrid loss function constituted by deformation field and image similarity to improve registration accuracy and generalization capability. In this work, 101 CT images of patients were collected for model construction (57), evaluation (14), and testing (30). HSCN-Net was compared with the classical Demons and VoxelMorph models. Qualitative analysis through the visual evaluation of critical brain tissues and quantitative analysis by determining the endpoint error (EPE) between the predicted sparse deformation vectors and gold-standard sparse deformation vectors, image normalized mutual information (NMI), and the Dice coefficient of the middle cerebral artery (MCA) blood supply area were carried out to assess model performance comprehensively.
HSCN-Net and Demons had a better visual spatial matching performance than VoxelMorph, and HSCN-Net was more competent for smooth and large intersubject deformations than Demons. The mean EPE of HSCN-Net (3.29 mm) was less than that of Demons (3.47 mm) and VoxelMorph (5.12 mm); the mean Dice of HSCN-Net was 0.96, which was higher than that of Demons (0.90) and VoxelMorph (0.87); and the mean NMI of HSCN-Net (0.83) was slightly lower than that of Demons (0.84), but higher than that of VoxelMorph (0.81). Moreover, the mean registration time of HSCN-Net (17.86 s) was shorter than that of VoxelMorph (18.53 s) and Demons (147.21 s).
The proposed HSCN-Net could achieve accurate and rapid intersubject brain CT registration. |
|---|---|
| AbstractList | Image registration is an essential step in the automated interpretation of the brain computed tomography (CT) images of patients with acute cerebrovascular disease (ACVD). However, performing brain CT registration accurately and rapidly remains greatly challenging due to the large intersubject anatomical variations, low resolution of soft tissues, and heavy computation costs. To this end, the HSCN-Net, a hybrid supervised convolutional neural network, was developed for precise and fast brain CT registration.
HSCN-Net generated synthetic deformation fields using a simulator as one supervision for one reference-moving image pair to address the problem of lack of gold standards. Furthermore, the simulator was designed to generate multiscale affine and elastic deformation fields to overcome the registration challenge posed by large intersubject anatomical deformation. Finally, HSCN-Net adopted a hybrid loss function constituted by deformation field and image similarity to improve registration accuracy and generalization capability. In this work, 101 CT images of patients were collected for model construction (57), evaluation (14), and testing (30). HSCN-Net was compared with the classical Demons and VoxelMorph models. Qualitative analysis through the visual evaluation of critical brain tissues and quantitative analysis by determining the endpoint error (EPE) between the predicted sparse deformation vectors and gold-standard sparse deformation vectors, image normalized mutual information (NMI), and the Dice coefficient of the middle cerebral artery (MCA) blood supply area were carried out to assess model performance comprehensively.
HSCN-Net and Demons had a better visual spatial matching performance than VoxelMorph, and HSCN-Net was more competent for smooth and large intersubject deformations than Demons. The mean EPE of HSCN-Net (3.29 mm) was less than that of Demons (3.47 mm) and VoxelMorph (5.12 mm); the mean Dice of HSCN-Net was 0.96, which was higher than that of Demons (0.90) and VoxelMorph (0.87); and the mean NMI of HSCN-Net (0.83) was slightly lower than that of Demons (0.84), but higher than that of VoxelMorph (0.81). Moreover, the mean registration time of HSCN-Net (17.86 s) was shorter than that of VoxelMorph (18.53 s) and Demons (147.21 s).
The proposed HSCN-Net could achieve accurate and rapid intersubject brain CT registration. BACKGROUNDImage registration is an essential step in the automated interpretation of the brain computed tomography (CT) images of patients with acute cerebrovascular disease (ACVD). However, performing brain CT registration accurately and rapidly remains greatly challenging due to the large intersubject anatomical variations, low resolution of soft tissues, and heavy computation costs. To this end, the HSCN-Net, a hybrid supervised convolutional neural network, was developed for precise and fast brain CT registration.METHODHSCN-Net generated synthetic deformation fields using a simulator as one supervision for one reference-moving image pair to address the problem of lack of gold standards. Furthermore, the simulator was designed to generate multiscale affine and elastic deformation fields to overcome the registration challenge posed by large intersubject anatomical deformation. Finally, HSCN-Net adopted a hybrid loss function constituted by deformation field and image similarity to improve registration accuracy and generalization capability. In this work, 101 CT images of patients were collected for model construction (57), evaluation (14), and testing (30). HSCN-Net was compared with the classical Demons and VoxelMorph models. Qualitative analysis through the visual evaluation of critical brain tissues and quantitative analysis by determining the endpoint error (EPE) between the predicted sparse deformation vectors and gold-standard sparse deformation vectors, image normalized mutual information (NMI), and the Dice coefficient of the middle cerebral artery (MCA) blood supply area were carried out to assess model performance comprehensively.RESULTSHSCN-Net and Demons had a better visual spatial matching performance than VoxelMorph, and HSCN-Net was more competent for smooth and large intersubject deformations than Demons. The mean EPE of HSCN-Net (3.29 mm) was less than that of Demons (3.47 mm) and VoxelMorph (5.12 mm); the mean Dice of HSCN-Net was 0.96, which was higher than that of Demons (0.90) and VoxelMorph (0.87); and the mean NMI of HSCN-Net (0.83) was slightly lower than that of Demons (0.84), but higher than that of VoxelMorph (0.81). Moreover, the mean registration time of HSCN-Net (17.86 s) was shorter than that of VoxelMorph (18.53 s) and Demons (147.21 s).CONCLUSIONThe proposed HSCN-Net could achieve accurate and rapid intersubject brain CT registration. Abstract Background Image registration is an essential step in the automated interpretation of the brain computed tomography (CT) images of patients with acute cerebrovascular disease (ACVD). However, performing brain CT registration accurately and rapidly remains greatly challenging due to the large intersubject anatomical variations, low resolution of soft tissues, and heavy computation costs. To this end, the HSCN-Net, a hybrid supervised convolutional neural network, was developed for precise and fast brain CT registration. Method HSCN-Net generated synthetic deformation fields using a simulator as one supervision for one reference–moving image pair to address the problem of lack of gold standards. Furthermore, the simulator was designed to generate multiscale affine and elastic deformation fields to overcome the registration challenge posed by large intersubject anatomical deformation. Finally, HSCN-Net adopted a hybrid loss function constituted by deformation field and image similarity to improve registration accuracy and generalization capability. In this work, 101 CT images of patients were collected for model construction (57), evaluation (14), and testing (30). HSCN-Net was compared with the classical Demons and VoxelMorph models. Qualitative analysis through the visual evaluation of critical brain tissues and quantitative analysis by determining the endpoint error (EPE) between the predicted sparse deformation vectors and gold-standard sparse deformation vectors, image normalized mutual information (NMI), and the Dice coefficient of the middle cerebral artery (MCA) blood supply area were carried out to assess model performance comprehensively. Results HSCN-Net and Demons had a better visual spatial matching performance than VoxelMorph, and HSCN-Net was more competent for smooth and large intersubject deformations than Demons. The mean EPE of HSCN-Net (3.29 mm) was less than that of Demons (3.47 mm) and VoxelMorph (5.12 mm); the mean Dice of HSCN-Net was 0.96, which was higher than that of Demons (0.90) and VoxelMorph (0.87); and the mean NMI of HSCN-Net (0.83) was slightly lower than that of Demons (0.84), but higher than that of VoxelMorph (0.81). Moreover, the mean registration time of HSCN-Net (17.86 s) was shorter than that of VoxelMorph (18.53 s) and Demons (147.21 s). Conclusion The proposed HSCN-Net could achieve accurate and rapid intersubject brain CT registration. Background Image registration is an essential step in the automated interpretation of the brain computed tomography (CT) images of patients with acute cerebrovascular disease (ACVD). However, performing brain CT registration accurately and rapidly remains greatly challenging due to the large intersubject anatomical variations, low resolution of soft tissues, and heavy computation costs. To this end, the HSCN-Net, a hybrid supervised convolutional neural network, was developed for precise and fast brain CT registration. Method HSCN-Net generated synthetic deformation fields using a simulator as one supervision for one reference–moving image pair to address the problem of lack of gold standards. Furthermore, the simulator was designed to generate multiscale affine and elastic deformation fields to overcome the registration challenge posed by large intersubject anatomical deformation. Finally, HSCN-Net adopted a hybrid loss function constituted by deformation field and image similarity to improve registration accuracy and generalization capability. In this work, 101 CT images of patients were collected for model construction (57), evaluation (14), and testing (30). HSCN-Net was compared with the classical Demons and VoxelMorph models. Qualitative analysis through the visual evaluation of critical brain tissues and quantitative analysis by determining the endpoint error (EPE) between the predicted sparse deformation vectors and gold-standard sparse deformation vectors, image normalized mutual information (NMI), and the Dice coefficient of the middle cerebral artery (MCA) blood supply area were carried out to assess model performance comprehensively. Results HSCN-Net and Demons had a better visual spatial matching performance than VoxelMorph, and HSCN-Net was more competent for smooth and large intersubject deformations than Demons. The mean EPE of HSCN-Net (3.29 mm) was less than that of Demons (3.47 mm) and VoxelMorph (5.12 mm); the mean Dice of HSCN-Net was 0.96, which was higher than that of Demons (0.90) and VoxelMorph (0.87); and the mean NMI of HSCN-Net (0.83) was slightly lower than that of Demons (0.84), but higher than that of VoxelMorph (0.81). Moreover, the mean registration time of HSCN-Net (17.86 s) was shorter than that of VoxelMorph (18.53 s) and Demons (147.21 s). Conclusion The proposed HSCN-Net could achieve accurate and rapid intersubject brain CT registration. Image registration is an essential step in the automated interpretation of the brain computed tomography (CT) images of patients with acute cerebrovascular disease (ACVD). However, performing brain CT registration accurately and rapidly remains greatly challenging due to the large intersubject anatomical variations, low resolution of soft tissues, and heavy computation costs. To this end, the HSCN-Net, a hybrid supervised convolutional neural network, was developed for precise and fast brain CT registration. HSCN-Net generated synthetic deformation fields using a simulator as one supervision for one reference-moving image pair to address the problem of lack of gold standards. Furthermore, the simulator was designed to generate multiscale affine and elastic deformation fields to overcome the registration challenge posed by large intersubject anatomical deformation. Finally, HSCN-Net adopted a hybrid loss function constituted by deformation field and image similarity to improve registration accuracy and generalization capability. In this work, 101 CT images of patients were collected for model construction (57), evaluation (14), and testing (30). HSCN-Net was compared with the classical Demons and VoxelMorph models. Qualitative analysis through the visual evaluation of critical brain tissues and quantitative analysis by determining the endpoint error (EPE) between the predicted sparse deformation vectors and gold-standard sparse deformation vectors, image normalized mutual information (NMI), and the Dice coefficient of the middle cerebral artery (MCA) blood supply area were carried out to assess model performance comprehensively. HSCN-Net and Demons had a better visual spatial matching performance than VoxelMorph, and HSCN-Net was more competent for smooth and large intersubject deformations than Demons. The mean EPE of HSCN-Net (3.29 mm) was less than that of Demons (3.47 mm) and VoxelMorph (5.12 mm); the mean Dice of HSCN-Net was 0.96, which was higher than that of Demons (0.90) and VoxelMorph (0.87); and the mean NMI of HSCN-Net (0.83) was slightly lower than that of Demons (0.84), but higher than that of VoxelMorph (0.81). Moreover, the mean registration time of HSCN-Net (17.86 s) was shorter than that of VoxelMorph (18.53 s) and Demons (147.21 s). The proposed HSCN-Net could achieve accurate and rapid intersubject brain CT registration. Abstract Background Image registration is an essential step in the automated interpretation of the brain computed tomography (CT) images of patients with acute cerebrovascular disease (ACVD). However, performing brain CT registration accurately and rapidly remains greatly challenging due to the large intersubject anatomical variations, low resolution of soft tissues, and heavy computation costs. To this end, the HSCN-Net, a hybrid supervised convolutional neural network, was developed for precise and fast brain CT registration. Method HSCN-Net generated synthetic deformation fields using a simulator as one supervision for one reference–moving image pair to address the problem of lack of gold standards. Furthermore, the simulator was designed to generate multiscale affine and elastic deformation fields to overcome the registration challenge posed by large intersubject anatomical deformation. Finally, HSCN-Net adopted a hybrid loss function constituted by deformation field and image similarity to improve registration accuracy and generalization capability. In this work, 101 CT images of patients were collected for model construction (57), evaluation (14), and testing (30). HSCN-Net was compared with the classical Demons and VoxelMorph models. Qualitative analysis through the visual evaluation of critical brain tissues and quantitative analysis by determining the endpoint error (EPE) between the predicted sparse deformation vectors and gold-standard sparse deformation vectors, image normalized mutual information (NMI), and the Dice coefficient of the middle cerebral artery (MCA) blood supply area were carried out to assess model performance comprehensively. Results HSCN-Net and Demons had a better visual spatial matching performance than VoxelMorph, and HSCN-Net was more competent for smooth and large intersubject deformations than Demons. The mean EPE of HSCN-Net (3.29 mm) was less than that of Demons (3.47 mm) and VoxelMorph (5.12 mm); the mean Dice of HSCN-Net was 0.96, which was higher than that of Demons (0.90) and VoxelMorph (0.87); and the mean NMI of HSCN-Net (0.83) was slightly lower than that of Demons (0.84), but higher than that of VoxelMorph (0.81). Moreover, the mean registration time of HSCN-Net (17.86 s) was shorter than that of VoxelMorph (18.53 s) and Demons (147.21 s). Conclusion The proposed HSCN-Net could achieve accurate and rapid intersubject brain CT registration. Background Image registration is an essential step in the automated interpretation of the brain computed tomography (CT) images of patients with acute cerebrovascular disease (ACVD). However, performing brain CT registration accurately and rapidly remains greatly challenging due to the large intersubject anatomical variations, low resolution of soft tissues, and heavy computation costs. To this end, the HSCN-Net, a hybrid supervised convolutional neural network, was developed for precise and fast brain CT registration. Method HSCN-Net generated synthetic deformation fields using a simulator as one supervision for one reference-moving image pair to address the problem of lack of gold standards. Furthermore, the simulator was designed to generate multiscale affine and elastic deformation fields to overcome the registration challenge posed by large intersubject anatomical deformation. Finally, HSCN-Net adopted a hybrid loss function constituted by deformation field and image similarity to improve registration accuracy and generalization capability. In this work, 101 CT images of patients were collected for model construction (57), evaluation (14), and testing (30). HSCN-Net was compared with the classical Demons and VoxelMorph models. Qualitative analysis through the visual evaluation of critical brain tissues and quantitative analysis by determining the endpoint error (EPE) between the predicted sparse deformation vectors and gold-standard sparse deformation vectors, image normalized mutual information (NMI), and the Dice coefficient of the middle cerebral artery (MCA) blood supply area were carried out to assess model performance comprehensively. Results HSCN-Net and Demons had a better visual spatial matching performance than VoxelMorph, and HSCN-Net was more competent for smooth and large intersubject deformations than Demons. The mean EPE of HSCN-Net (3.29 mm) was less than that of Demons (3.47 mm) and VoxelMorph (5.12 mm); the mean Dice of HSCN-Net was 0.96, which was higher than that of Demons (0.90) and VoxelMorph (0.87); and the mean NMI of HSCN-Net (0.83) was slightly lower than that of Demons (0.84), but higher than that of VoxelMorph (0.81). Moreover, the mean registration time of HSCN-Net (17.86 s) was shorter than that of VoxelMorph (18.53 s) and Demons (147.21 s). Conclusion The proposed HSCN-Net could achieve accurate and rapid intersubject brain CT registration. Keywords: Image registration, Brain CT, Intersubject, Deep learning, Hybrid supervision |
| ArticleNumber | 131 |
| Audience | Academic |
| Author | Yuan, Hongmei Yang, Minglei Huang, Feng Qian, Shan Wang, Wenxin Jia, Xiaotian |
| Author_xml | – sequence: 1 givenname: Hongmei surname: Yuan fullname: Yuan, Hongmei organization: Neusoft Medical System, Co. Ltd, Shenyang, 110167, China – sequence: 2 givenname: Minglei surname: Yang fullname: Yang, Minglei email: yangminglei@neusoft.com, yangminglei@neusoft.com organization: Neusoft Medical System, Co. Ltd, Shenyang, 110167, China. yangminglei@neusoft.com – sequence: 3 givenname: Shan surname: Qian fullname: Qian, Shan organization: Neusoft Medical System, Co. Ltd, Shenyang, 110167, China – sequence: 4 givenname: Wenxin surname: Wang fullname: Wang, Wenxin organization: Neusoft Medical System, Co. Ltd, Shenyang, 110167, China – sequence: 5 givenname: Xiaotian surname: Jia fullname: Jia, Xiaotian organization: Shenyang Advanced Medical Equipment Technology Incubation Center, Co. Ltd, Shenyang, 110167, China – sequence: 6 givenname: Feng surname: Huang fullname: Huang, Feng organization: Neusoft Medical System, Co. Ltd, Shenyang, 110167, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34965854$$D View this record in MEDLINE/PubMed |
| BookMark | eNptkktvEzEUhS1URNvAH2CBRmIDiyn2-DH2BqlEPIIqIUEW7CyP587UYWKn9kyg_x4nKaVByAtb1989ls895-jEBw8IPSf4ghAp3iRSKSpLXJESY1WTUj5CZ4TVvFQV_37y4HyKzlNaYVxhLNQTdEqZElxydoY-v4vG-WK-LCL0Lo3RjC74YkrO98X1bRNdW6RpA3HrErSFDX4bhmnHmKHwMMX9Nv4M8cdT9LgzQ4Jnd_sMLT-8X84_lVdfPi7ml1el5aoeS0sV5lQJLGoCqoGOSEYrqISizACtQRFGWqkUwyBAMWq6htVdDdaYSlA6Q4uDbBvMSm-iW5t4q4Nxel8Isdcmjs4OoC22yhAhARRnsmubrmvafGoYplRgmbXeHrQ2U7OG1oLPBgxHosc33l3rPmy1rAnnimeBV3cCMdxMkEa9dsnCMBgPYUq6EoTnIdT5czP08h90FaaYbdxRFVZc1pj8pXqTP-B8F_K7dieqL7NInppgLFMX_6HyamHt8oygc7l-1PD6qCEzI_waezOlpBffvh6z1YG1MaQUobv3g2C9C54-BE_n4Ol98PTOyRcPnbxv-ZM0-huYNdPS |
| CitedBy_id | crossref_primary_10_3389_fneur_2023_1170955 crossref_primary_10_2139_ssrn_3999122 crossref_primary_10_1016_j_cmpb_2022_106932 |
| Cites_doi | 10.1109/WACV45572.2020.9093506 10.1016/S0031-3203(98)00091-0 10.1016/j.media.2019.03.006 10.3390/pr9071155 10.1109/TMI.2016.2576360 10.1007/s11548-016-1380-9 10.1007/978-3-030-72084-1_8 10.1007/978-3-030-00928-1_82 10.1007/978-3-319-75238-9_32 10.1016/j.cma.2020.113609 10.1109/CVPR42600.2020.00470 10.1016/j.patcog.2016.09.036 10.1177/1533034617691408 10.1016/j.cie.2021.107250 10.14257/astl.2018.150.77 10.1109/TMI.2020.2974844 10.1007/978-3-030-32226-7_19 10.1109/TMI.2018.2878316 10.23919/SPA.2017.8166900 10.1007/s00138-020-01060-x 10.1016/j.neuroimage.2017.07.008 10.1117/12.2581567 10.1007/978-981-10-1424-6 10.1007/978-3-319-67558-9_28 |
| ContentType | Journal Article |
| Copyright | 2021. The Author(s). COPYRIGHT 2021 BioMed Central Ltd. 2021. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. The Author(s) 2021 |
| Copyright_xml | – notice: 2021. The Author(s). – notice: COPYRIGHT 2021 BioMed Central Ltd. – notice: 2021. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: The Author(s) 2021 |
| DBID | CGR CUY CVF ECM EIF NPM AAYXX CITATION ISR 3V. 7QO 7X7 7XB 88E 8FD 8FE 8FG 8FH 8FI 8FJ 8FK ABJCF ABUWG AFKRA AZQEC BBNVY BENPR BGLVJ BHPHI CCPQU COVID DWQXO FR3 FYUFA GHDGH GNUQQ HCIFZ K9. L6V LK8 M0S M1P M7P M7S P64 PIMPY PQEST PQQKQ PQUKI PRINS PTHSS 7X8 5PM DOA |
| DOI | 10.1186/s12938-021-00971-8 |
| DatabaseName | Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed CrossRef Gale In Context: Science ProQuest Central (Corporate) Biotechnology Research Abstracts ProQuest - Health & Medical Complete保健、医学与药学数据库 ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) Materials Science & Engineering Database (Proquest) ProQuest Central (Alumni) ProQuest Central ProQuest Central Essentials Biological Science Collection AUTh Library subscriptions: ProQuest Central Technology Collection ProQuest Natural Science Collection ProQuest One Community College Coronavirus Research Database ProQuest Central Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection (Proquest) (PQ_SDU_P3) ProQuest Health & Medical Complete (Alumni) ProQuest Engineering Collection Biological Sciences Health & Medical Collection (Alumni Edition) PML(ProQuest Medical Library) Biological Science Database Engineering Database Biotechnology and BioEngineering Abstracts Publicly Available Content Database ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China Engineering Collection MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) CrossRef Publicly Available Content Database ProQuest Central Student Technology Collection Technology Research Database ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Natural Science Collection ProQuest Central China ProQuest Central ProQuest Engineering Collection Health Research Premium Collection Biotechnology Research Abstracts Health and Medicine Complete (Alumni Edition) Natural Science Collection ProQuest Central Korea Biological Science Collection ProQuest Medical Library (Alumni) Engineering Collection Engineering Database ProQuest Biological Science Collection ProQuest One Academic Eastern Edition Coronavirus Research Database ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest SciTech Collection ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition Materials Science & Engineering Collection Engineering Research Database ProQuest One Academic ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE MEDLINE - Academic CrossRef Publicly Available Content Database |
| Database_xml | – sequence: 1 dbid: DOA name: Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 4 dbid: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine Engineering |
| EISSN | 1475-925X |
| EndPage | 131 |
| ExternalDocumentID | oai_doaj_org_article_c0c9a168ee9548fdbffbd548b4033608 A693658644 10_1186_s12938_021_00971_8 34965854 |
| Genre | Journal Article |
| GeographicLocations | China |
| GeographicLocations_xml | – name: China |
| GrantInformation_xml | – fundername: shenyang science and technology plan fund grantid: 20-201-4-10 – fundername: ; grantid: 20-201-4-10 |
| GroupedDBID | --- -A0 0R~ 23N 2WC 3V. 53G 5GY 5VS 6J9 6PF 7X7 88E 8FE 8FG 8FH 8FI 8FJ AAFWJ AAJSJ AAWTL ABDBF ABJCF ABUWG ACGFO ACGFS ACIHN ACIWK ACPRK ACRMQ ADBBV ADINQ ADRAZ ADUKV AEAQA AENEX AFKRA AFPKN AFRAH AHBYD AHMBA AHYZX ALIPV ALMA_UNASSIGNED_HOLDINGS AMKLP AMTXH AOIJS BAPOH BAWUL BBNVY BCNDV BENPR BFQNJ BGLVJ BHPHI BMC BPHCQ BVXVI C24 C6C CCPQU CGR CS3 CUY CVF DIK E3Z EAD EAP EAS EBD EBLON EBS ECM EIF EMB EMK EMOBN ESX F5P FRP FYUFA GROUPED_DOAJ GX1 HCIFZ HMCUK HYE I-F IAO IGS IHR INH INR ISR ITC KQ8 L6V LK8 M1P M48 M7P M7S MK~ ML~ M~E NPM O5R O5S OK1 P2P PGMZT PIMPY PQQKQ PROAC PSQYO PTHSS RBZ RNS ROL RPM RSV SEG SOJ SV3 TR2 TUS UKHRP W2D WOQ WOW XSB AAYXX CITATION AFGXO ABVAZ AFNRJ 7QO 7XB 8FD 8FK AZQEC COVID DWQXO FR3 GNUQQ K9. P64 PQEST PQUKI PRINS 7X8 5PM |
| ID | FETCH-LOGICAL-c597t-c39053960671e9bef18432e26934ae37e9141d89940e6e943afb47f7ecaa2633 |
| IEDL.DBID | RPM |
| ISSN | 1475-925X |
| IngestDate | Fri Oct 04 13:08:27 EDT 2024 Tue Sep 17 21:22:55 EDT 2024 Fri Aug 16 09:53:53 EDT 2024 Fri Aug 30 23:19:05 EDT 2024 Thu Feb 22 23:34:51 EST 2024 Fri Feb 02 04:19:40 EST 2024 Thu Aug 01 19:54:46 EDT 2024 Thu Sep 12 16:32:38 EDT 2024 Sat Sep 28 08:24:41 EDT 2024 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Deep learning Image registration Hybrid supervision Intersubject Brain CT |
| Language | English |
| License | 2021. The Author(s). Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c597t-c39053960671e9bef18432e26934ae37e9141d89940e6e943afb47f7ecaa2633 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| OpenAccessLink | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8715595/ |
| PMID | 34965854 |
| PQID | 2620958701 |
| PQPubID | 42562 |
| PageCount | 1 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_c0c9a168ee9548fdbffbd548b4033608 pubmedcentral_primary_oai_pubmedcentral_nih_gov_8715595 proquest_miscellaneous_2615475769 proquest_journals_2620958701 gale_infotracmisc_A693658644 gale_infotracacademiconefile_A693658644 gale_incontextgauss_ISR_A693658644 crossref_primary_10_1186_s12938_021_00971_8 pubmed_primary_34965854 |
| PublicationCentury | 2000 |
| PublicationDate | 2021-12-29 |
| PublicationDateYYYYMMDD | 2021-12-29 |
| PublicationDate_xml | – month: 12 year: 2021 text: 2021-12-29 day: 29 |
| PublicationDecade | 2020 |
| PublicationPlace | England |
| PublicationPlace_xml | – name: England – name: London |
| PublicationTitle | Biomedical engineering online |
| PublicationTitleAlternate | Biomed Eng Online |
| PublicationYear | 2021 |
| Publisher | BioMed Central Ltd BioMed Central BMC |
| Publisher_xml | – name: BioMed Central Ltd – name: BioMed Central – name: BMC |
| References | 971_CR7 971_CR8 S Broggi (971_CR5) 2017; 16 971_CR9 SM Jung (971_CR2) 2018; 150 G Haskins (971_CR20) 2020; 31 971_CR3 A Uus (971_CR10) 2020; 39 971_CR1 971_CR22 971_CR21 971_CR24 971_CR25 971_CR17 C Liu (971_CR12) 2019; 9 971_CR19 X Yang (971_CR14) 2017; 158 G Haskins (971_CR23) 2020; 31 O Ronneberger (971_CR26) 2015 S Reaungamornrat (971_CR6) 2016; 35 971_CR31 971_CR33 A Mayer (971_CR4) 2016; 11 W Yu (971_CR11) 2017; 63 971_CR13 L Abualigah (971_CR27) 2021; 376 971_CR15 J Fan (971_CR18) 2019; 54 K Eppenhof (971_CR16) 2019; 38 971_CR29 L Abualigah (971_CR30) 2021; 9 C Studholme (971_CR32) 1999; 32 L Abualigah (971_CR28) 2021; 157 |
| References_xml | – ident: 971_CR22 doi: 10.1109/WACV45572.2020.9093506 – volume: 32 start-page: 71 issue: 1 year: 1999 ident: 971_CR32 publication-title: Pattern Recogn doi: 10.1016/S0031-3203(98)00091-0 contributor: fullname: C Studholme – volume: 54 start-page: 193 year: 2019 ident: 971_CR18 publication-title: Med Image Anal doi: 10.1016/j.media.2019.03.006 contributor: fullname: J Fan – volume-title: U-Net: convolutional networks for biomedical image segmentation year: 2015 ident: 971_CR26 contributor: fullname: O Ronneberger – volume: 9 start-page: 1155 issue: 7 year: 2021 ident: 971_CR30 publication-title: Processes doi: 10.3390/pr9071155 contributor: fullname: L Abualigah – volume: 35 start-page: 2413 issue: 11 year: 2016 ident: 971_CR6 publication-title: IEEE Trans Med Imaging doi: 10.1109/TMI.2016.2576360 contributor: fullname: S Reaungamornrat – ident: 971_CR3 – volume: 9 start-page: 4 issue: 1 year: 2019 ident: 971_CR12 publication-title: Intell Comput Appl. contributor: fullname: C Liu – volume: 11 start-page: 1015 issue: 6 year: 2016 ident: 971_CR4 publication-title: Int J Comput Assist Radiol Surg doi: 10.1007/s11548-016-1380-9 contributor: fullname: A Mayer – ident: 971_CR7 – ident: 971_CR9 doi: 10.1007/978-3-030-72084-1_8 – ident: 971_CR17 doi: 10.1007/978-3-030-00928-1_82 – ident: 971_CR8 doi: 10.1007/978-3-319-75238-9_32 – volume: 376 start-page: 113609 year: 2021 ident: 971_CR27 publication-title: Comput Methods Appl Mech Eng doi: 10.1016/j.cma.2020.113609 contributor: fullname: L Abualigah – ident: 971_CR25 – ident: 971_CR24 doi: 10.1109/CVPR42600.2020.00470 – volume: 63 start-page: 689 year: 2017 ident: 971_CR11 publication-title: Pattern Recogn doi: 10.1016/j.patcog.2016.09.036 contributor: fullname: W Yu – volume: 16 start-page: 373 issue: 3 year: 2017 ident: 971_CR5 publication-title: Technol Cancer Res Treat doi: 10.1177/1533034617691408 contributor: fullname: S Broggi – volume: 157 start-page: 107250 year: 2021 ident: 971_CR28 publication-title: Comput Ind Eng doi: 10.1016/j.cie.2021.107250 contributor: fullname: L Abualigah – volume: 150 start-page: 342 year: 2018 ident: 971_CR2 publication-title: Adv Sci Technol doi: 10.14257/astl.2018.150.77 contributor: fullname: SM Jung – volume: 39 start-page: 2750 issue: 9 year: 2020 ident: 971_CR10 publication-title: IEEE Trans Med Imaging doi: 10.1109/TMI.2020.2974844 contributor: fullname: A Uus – ident: 971_CR21 doi: 10.1007/978-3-030-32226-7_19 – ident: 971_CR15 – volume: 38 start-page: 1097 issue: 5 year: 2019 ident: 971_CR16 publication-title: IEEE Trans Med Imaging doi: 10.1109/TMI.2018.2878316 contributor: fullname: K Eppenhof – ident: 971_CR31 – ident: 971_CR13 doi: 10.23919/SPA.2017.8166900 – volume: 31 start-page: 8 year: 2020 ident: 971_CR20 publication-title: Machine Vision and Applications. doi: 10.1007/s00138-020-01060-x contributor: fullname: G Haskins – volume: 158 start-page: 378 year: 2017 ident: 971_CR14 publication-title: Neuroimage doi: 10.1016/j.neuroimage.2017.07.008 contributor: fullname: X Yang – ident: 971_CR19 doi: 10.1117/12.2581567 – ident: 971_CR29 – ident: 971_CR1 doi: 10.1007/978-981-10-1424-6 – volume: 31 start-page: 1 issue: 1–2 year: 2020 ident: 971_CR23 publication-title: Mach Vis Appl contributor: fullname: G Haskins – ident: 971_CR33 doi: 10.1007/978-3-319-67558-9_28 |
| SSID | ssj0020069 |
| Score | 2.3412874 |
| Snippet | Image registration is an essential step in the automated interpretation of the brain computed tomography (CT) images of patients with acute cerebrovascular... Abstract Background Image registration is an essential step in the automated interpretation of the brain computed tomography (CT) images of patients with acute... Background Image registration is an essential step in the automated interpretation of the brain computed tomography (CT) images of patients with acute... BACKGROUNDImage registration is an essential step in the automated interpretation of the brain computed tomography (CT) images of patients with acute... Abstract Background Image registration is an essential step in the automated interpretation of the brain computed tomography (CT) images of patients with acute... |
| SourceID | doaj pubmedcentral proquest gale crossref pubmed |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database |
| StartPage | 131 |
| SubjectTerms | Accuracy Algorithms Artificial neural networks Brain Brain - diagnostic imaging Brain CT Brain mapping Cerebrovascular disease Cerebrovascular diseases Computed tomography CT imaging Deep learning Diagnosis Elastic deformation Error analysis Health aspects Humans Hybrid supervision Image Processing, Computer-Assisted Image registration Intersubject Medical imaging Methods Neural networks Neural Networks, Computer Qualitative analysis Registration Soft tissues Tissue analysis Tomography, X-Ray Computed |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwELZQDwgOqJRXSkEpQuKArG5ix49jW1G1lcoBFqk3y8-WS7Yiuwf-PTOOs9qIAxdOu1pPkvU3Hs-MMv6GkI_B2dAl3lFlA6fcSUkt54ky2CYDGGZnc7u3m6_i8ge_vu1ud1p9YU3YSA88AnfiF17bRqgYkZosBZeSC_DN8QVjohzzbbopmSqpFhLwTkdklDgZ0KspiuUImTSJqpkbymz9f-_JO05pXjC544Eu9smzEjrWp-Nffk4exf6APN0hFDwgj2_Kq_IX5PoMmz_U58saey9M7Lg11rnf1fe_8aBWPWwecKsYYqix-rysQngGslzmj1wj_pIsL74szy9paZxAPeQHa-qZBtvC3EQ2UbuYsKlLG1uhGbeRyagb3gTItPgiiqg5s8lxmWT01raCsVdkr1_18Q2p8T2MhyAQEw-eAlb7Se6E8swHcO28Ip8nGM3DSI9hclqhhBlBNwC6yaAbVZEzRHoridTW-QdQuCkKN_9SeEU-oJ4Mklf0WB1zZzfDYK6-fzOnMEGIqCDEq8inIpRWALC35bABzAr5rmaSRzNJsC4_H56WgynWPRgk8dcd7HRNRY63w3glVqz1cbVBGQhOJWRzuiKvx9WznXcm6Vcd3FzO1tUMmPlI__M-c39Dfguq6A7_B5JvyZMWTaJpaauPyN761ya-gxBr7d5na_oDzmwiDg priority: 102 providerName: Directory of Open Access Journals – databaseName: Coronavirus Research Database dbid: COVID link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9NAEB6VVEJw4FEoGAoyCIkD2ib2rl8n1AaqtFJBgoB6W-3LaYXkhNo5wK9nZm2HGCROnBJlN3Ym89gZ77ffALyyWtmkFAnLlRVM6CxjSoiScQyTFh0zUb7d2_mHdPZFnF0kFzsw68_CEKyyj4k-UNuloWfkYyJOLxK0rmisND0FMM347eo7o_5RtM_aNdO4Abs8xyxgBLvTj19P322KL6Lk7Q_N5Om4pnUuZwRQ8DRKLB8sTJ6__-8ovbVMDSGUW2vSyV246qVpoSjfDteNPjQ__yB6_B_i3oM7XeIaHrWWdh92XLUHt7foDPfg5nm3Uf8Azo6p9UQ4nYfU-aHn5g0JZb8IL3_QMbGwXq8oUNXOhoR973wA70Ecm_7FI9Qfwvzk_Xw6Y13bBmawOmmY4QV6NlVGWeQK7UpqKRO7OC24UI5nrohEZLHOExOXukJwVWqRlZkzSsUp5_swqpaVewwh7QIZTEGp7BGlJaxhJnSaG24sJhYigDe9yuSqJeeQvqjJU9kqWKKCpVewzAM4Jq1uZhKxtv9geb2QnZ9KMzGFitLcOWLCK60uS23xnRYTztMJXuQl2YQk6oyKsDkLta5refr5kzxCATGfwwQzgNfdpHJJqlPdUQeUiti2BjMPBjPRt81wuLcO2cWWWv42jQBebIbpm4SXq9xyTXMwNc6wliwCeNRa6kZu3yIgT_Di2cCGB3_McKS6uvTM41hdoyqSJ__-WU_hVkyOFcUsLg5g1Fyv3TNM3Rr9vPPKX54QRMA priority: 102 providerName: ProQuest – databaseName: Scholars Portal Journals: Open Access dbid: M48 link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3db9MwED-NISF4QDC-AgMFhMQDMjSxE8cPaNompjGpPEAn9c1y_NFNmtLRtBL777lzk6rR9sBTq_rqyJc73_3k8-8APrrauCKIglXGCSZqKZkRIjCO26RDxyxMbPc2_lmenouzaTHdgb7dUafA9k5oR_2kzhdXX_7-uTlAh_8WHb4qv7YUsypGxQaREolV9-B-Lrggix-LzakCoWcVbxvJgqm8mPaXaO6cYxCoIp__7V17K2wNSyq3YtTJE3jcJZfp4doansKOb_bg0Rbl4B48GHeH6c_g7IjaQ6THk5S6M_T8uSlVws_Sixu6ypW2q2vaTFrvUqpP7-wUn0E8mPEjVpE_h8nJ98nxKetaKzCLCGLJLFfofYReZOZV7QO1fcl9XioujOfSq0xkDrGYGPnSK8FNqIUM0ltj8pLzF7DbzBv_ClI6qbGYJhI0EcFRPaAUdVlZbh0Gf5HA516N-npNoKEj8KhKvVa6RqXrqHRdJXBEmt5IEvl1_GG-mOnOl7QdWWWysvKe2OqCq0OoHX6rxYjzcoSTfKD3pIneoqH6mZlZta3-8fuXPsQFYs6FSWACnzqhMEcFW9NdR8BVESPWQHJ_IIn-Z4fDvTno3nw10fyrAvfCLIH3m2H6J9W0NX6-IhlMXyXiPZXAy7X1bNYdafyrAieXA7saKGY40lxeRHZwRMD4KorX_72-N_AwJ7vPcparfdhdLlb-LWZay_pddJ9_ahIiag priority: 102 providerName: Scholars Portal |
| Title | Brain CT registration using hybrid supervised convolutional neural network |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/34965854 https://www.proquest.com/docview/2620958701/abstract/ https://search.proquest.com/docview/2615475769 https://pubmed.ncbi.nlm.nih.gov/PMC8715595 https://doaj.org/article/c0c9a168ee9548fdbffbd548b4033608 |
| Volume | 20 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3fb9MwED5tQ0LwgGDACIwqICQeUNYmduLkcS0r26SOaRTUN8u_0k1iabW0D_z33LlJ1Yg3XpKovrTx2ee7az5_B_DJamXTkqdRriyPuBYiUpyXEcNl0qJhpsqXe5tcZec_-eUsne1B2u6F8aB9o-9Oqt_3J9XdrcdWLu9Nv8WJ9a8nIwzyMRBO-_uwLxhrU_QmyyLu3XZ3TJ71a3JoeURIBM-XFFGFPs-Snqe844w8Z_-_K_OOa-rCJnf80Pg5PGsCyPB086AvYM9Vh_B0h1bwEB5PmhfmL-FySCUgwtE0pAoMLUduSGj3eXj7h7ZrhfV6SQtG7WxIGPRmLuJvENelP3mk-CuYjs-mo_OoKZ8QGcwSVpFhBVoYZSgidoV2JZV2SVySFYwrx4QrYh5bzLf4wGWu4EyVmotSOKNUkjH2Gg6qReXeQEhvYwyGgpR-8NIS5k9wneWGGYsOngfwpVWjXG5IMqRPLvJMbvQvUf_S61_mAQxJ01tJIrj2Hywe5rIZZmkGplBxljtHjHSl1WWpLV5pPmAsG-CXfKRxkkRhURFGZq7WdS0vftzIU-wgji4GegF8boTKBSrYqGbLAfaKWK86kscdSbQx021up4NsbLyWROVfpLjexQF82DbTnYRbq9xiTTIYogrM6YoAjjazZ9vvdhIGIDrzqqOYbgsahGcAbwzg7X_f-Q6eJGQScRIlxTEcrB7W7j1GVyvdQ5uaCTzm4289eDQ8u7q-wfPo-6-Lrz3_jwUeJzzveav7C2nLKOM |
| link.rule.ids | 230,315,733,786,790,870,891,2115,12083,12792,21416,24346,27957,27958,31754,31755,33408,33409,33779,33780,38551,43345,43635,43840,43930,53827,53829 |
| linkProvider | National Library of Medicine |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV3Nb9MwFH-CIfFxQDAYBAYEhMQBWWtiJ05OaJuYurHuAEXqzXL80XFJytIe-O95z3W7RkicGtUvaf38PuPn3wP4aBttCy8KVmkrmGikZFoIzziaSYuKWejQ7m1yVY5_iotZMYsv3PpYVrmxicFQ287QO_IjAk6vC5Su7MviN6OuUbS7Glto3IV7gnNBJX1ydptwEQzv5qBMVR715NsqRkUJATqJVQNnFDD7_7XMO65pWDa544fOnsDjGECmx-sVfwp3XLsPj3ZgBffh_iRumD-DixNqAZGeTlPqwLDByE2p2n2eXv-h41ppv1qQweidTakGPcoi_gZhXYaPUCn-HKZnX6enYxbbJzCDWcKSGV6jhlGGIjNXN85Ta5fc5WXNhXZcujoTmcV8S4xc6WrBtW-E9NIZrfOS8wPYa7vWvYSUdmMMhoKUfghvqeZPiqasDDcWHbxI4POGjWqxBslQIbmoSrVmukKmq8B0VSVwQpzeUhLAdfiiu5mrqC_KjEyts7JyjhDpvG28byxeNWLEeTnCh3ygdVIEYdFSjcxcr_penf_4ro5xghhXYaCXwKdI5DtksNHxyAHOilCvBpSHA0rUMTMc3oiDijreq1uJTOD9dpjupLq11nUrosEQVWJOVyfwYi0923kHqP6qwIfLgVwNGDMcaX9dBwRwzHJxKYpX__9b7-DBeDq5VJfnV99ew8OcBD_LWV4fwt7yZuXeYDi1bN4GnfkLt9Ab6Q |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Lb9NAEB5BkSo48CiPGgoYhMQBOY7t9evYBqK2kKqCIFW9rPaZVrROVMcH-PXMbOwohltPsbJjW7Mzszsjf_sNwActhU4tS4NCaBYwmeeBYMwGCS6TGgMzFa7d2-QkO_zJjs_Ss41WXw60r-TloLq6HlSXFw5bubhWYYcTC08nI0zyMRFOw4W24V24hzEbl12h3tZaxMDbnZEpsrCmba0ICI_gWJMC6tPnuNKLlPW2JMfc___6vLFB9cGTG7vR-BGcd3qsQCi_Bs1SDtSffygeb6XoY3jY5qj-_krkCdwx1Q482GAu3IHtSftN_ikcH1CXCX809anJQ0fD6xOgfuZf_KYTYX7dLGhNqo32Cebeuju-g-g03Y8Doz-D6fjLdHQYtB0aAoWFyDJQSYlBTEVQHplSGkvdY2ITZ2XChElyU0Ys0ljSsaHJTMkSYSXLbW6UEHGWJM9hq5pXZhd8-uCjMNukCodZTbDCnMmsUInSmEMwDz51NuKLFQ8Hd_VLkfGVcTkalzvj8sKDAzLjWpI4tN0f85sZbyeYq6EqRZQVxhDpndXSWqnxSrJhkmRDfMh7cgJOLBkVwXBmoqlrfvTjO99HBdF1MJf04GMrZOc4wUq0pxpQKyLW6knu9SQxjFV_uPM13i4jNaduAWWKS2rkwbv1MN1J0LjKzBuSwSw4x7Kx9ODFyjXXence7kHec9rexPRH0BUdyXjrei9vfedb2D79PObfjk6-voL7MYVeFAdxuQdby5vGvMZcbinfuKj9C-C8RpU |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Brain+CT+registration+using+hybrid+supervised+convolutional+neural+network&rft.jtitle=Biomedical+engineering+online&rft.au=Yuan%2C+Hongmei&rft.au=Yang%2C+Minglei&rft.au=Qian%2C+Shan&rft.au=Wang%2C+Wenxin&rft.date=2021-12-29&rft.pub=BioMed+Central+Ltd&rft.issn=1475-925X&rft.eissn=1475-925X&rft.volume=20&rft.issue=1&rft_id=info:doi/10.1186%2Fs12938-021-00971-8&rft.externalDocID=A693658644 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1475-925X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1475-925X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1475-925X&client=summon |