A new surgical path planning framework for neurosurgery
Background Despite using a variety of path‐finding algorithms that use tracts, the most significant advancement in this study is considering the values of all brain areas by doing atlas‐based segmentation for a more precise search. Our motivation comes from the literature’s shortcomings in designing...
Saved in:
Published in | The international journal of medical robotics + computer assisted surgery Vol. 20; no. 1; pp. e2576 - n/a |
---|---|
Main Authors | , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Hamilton
Wiley Subscription Services, Inc
01.02.2024
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Abstract | Background
Despite using a variety of path‐finding algorithms that use tracts, the most significant advancement in this study is considering the values of all brain areas by doing atlas‐based segmentation for a more precise search. Our motivation comes from the literature’s shortcomings in designing and implementing path‐planning methods. Since planning paths with curvatures is a complex problem that requires considering many surgical and physiological constraints, most path‐planning strategies focus on straight paths. There is also a lack of studies that focus on the complete structure of the brain with the tracks, veins, and segmented areas. Instrument dependence is another inadequacy of the methods proposed in the literature.
Aims
The aim of this study is to design a new surgical path planning framework that helps to plan the surgical path independently of the instrument, considers the entire structure of the brain, and allows curvilinear surgical paths. Thus, neurosurgeons can generate patient‐specific possible optimal surgical pathways before the neurosurgical procedure.
Materials & Methods
The proposed framework includes different path‐finding algorithms (Dijkstra, A*, and their aggressive variants) that find optimal paths by taking the risk scores (surgeons assessed all the segmented regions, considering the extent of damage. In this evaluation, scores ranged from “0 to 10,” with the most critical areas receiving a score of “10,” while the least possible affected areas were assigned a score of "0") for sensitive brain areas into consideration. For the tract image processing the framework includes fractional anisotropy (FA), relative anisotropy (RA), spherical measure (SM), and linear measure (LM) methods.
Results
This is the first paper to handle tracts and atlas‐based segmentation of the human brain altogether under a framework for surgical path planning. The framework has a dynamic structure that gives the flexibility to add different path‐finding algorithms and generate different widths of surgical pathways. Moreover, surgeons can update the score table to guarantee minimally invasive surgery. The output file format of all the extracted surgical paths is NRRD, so it can be easily visualised, analysed, or processed over the third part software tools.
Discussion
In this study, we generated many possible surgical pathways then these pathways were evaluated by the surgeons the results were impressive because the framework could identify surgical pathways used in real‐world surgery that correspond to the standard pathways such as anterior transsylvian, trans sulcal, transgyral, and sub‐temporal.
Conclusion
This study proposes a new surgical path planning framework for neurosurgery. Moreover, in the future by adding/adopting different parameters (such as operation time, and short and long‐term complications after surgery) to the proposed framework, it would be possible to find new surgical pathways for difficult surgical conditions. |
---|---|
AbstractList | BACKGROUNDDespite using a variety of path-finding algorithms that use tracts, the most significant advancement in this study is considering the values of all brain areas by doing atlas-based segmentation for a more precise search. Our motivation comes from the literature's shortcomings in designing and implementing path-planning methods. Since planning paths with curvatures is a complex problem that requires considering many surgical and physiological constraints, most path-planning strategies focus on straight paths. There is also a lack of studies that focus on the complete structure of the brain with the tracks, veins, and segmented areas. Instrument dependence is another inadequacy of the methods proposed in the literature. AIMSThe aim of this study is to design a new surgical path planning framework that helps to plan the surgical path independently of the instrument, considers the entire structure of the brain, and allows curvilinear surgical paths. Thus, neurosurgeons can generate patient-specific possible optimal surgical pathways before the neurosurgical procedure. MATERIALS & METHODSThe proposed framework includes different path-finding algorithms (Dijkstra, A*, and their aggressive variants) that find optimal paths by taking the risk scores (surgeons assessed all the segmented regions, considering the extent of damage. In this evaluation, scores ranged from "0 to 10," with the most critical areas receiving a score of "10," while the least possible affected areas were assigned a score of "0") for sensitive brain areas into consideration. For the tract image processing the framework includes fractional anisotropy (FA), relative anisotropy (RA), spherical measure (SM), and linear measure (LM) methods. RESULTSThis is the first paper to handle tracts and atlas-based segmentation of the human brain altogether under a framework for surgical path planning. The framework has a dynamic structure that gives the flexibility to add different path-finding algorithms and generate different widths of surgical pathways. Moreover, surgeons can update the score table to guarantee minimally invasive surgery. The output file format of all the extracted surgical paths is NRRD, so it can be easily visualised, analysed, or processed over the third part software tools. DISCUSSIONIn this study, we generated many possible surgical pathways then these pathways were evaluated by the surgeons the results were impressive because the framework could identify surgical pathways used in real-world surgery that correspond to the standard pathways such as anterior transsylvian, trans sulcal, transgyral, and sub-temporal. CONCLUSIONThis study proposes a new surgical path planning framework for neurosurgery. Moreover, in the future by adding/adopting different parameters (such as operation time, and short and long-term complications after surgery) to the proposed framework, it would be possible to find new surgical pathways for difficult surgical conditions. Background Despite using a variety of path‐finding algorithms that use tracts, the most significant advancement in this study is considering the values of all brain areas by doing atlas‐based segmentation for a more precise search. Our motivation comes from the literature’s shortcomings in designing and implementing path‐planning methods. Since planning paths with curvatures is a complex problem that requires considering many surgical and physiological constraints, most path‐planning strategies focus on straight paths. There is also a lack of studies that focus on the complete structure of the brain with the tracks, veins, and segmented areas. Instrument dependence is another inadequacy of the methods proposed in the literature. Aims The aim of this study is to design a new surgical path planning framework that helps to plan the surgical path independently of the instrument, considers the entire structure of the brain, and allows curvilinear surgical paths. Thus, neurosurgeons can generate patient‐specific possible optimal surgical pathways before the neurosurgical procedure. Materials & Methods The proposed framework includes different path‐finding algorithms (Dijkstra, A*, and their aggressive variants) that find optimal paths by taking the risk scores (surgeons assessed all the segmented regions, considering the extent of damage. In this evaluation, scores ranged from “0 to 10,” with the most critical areas receiving a score of “10,” while the least possible affected areas were assigned a score of "0") for sensitive brain areas into consideration. For the tract image processing the framework includes fractional anisotropy (FA), relative anisotropy (RA), spherical measure (SM), and linear measure (LM) methods. Results This is the first paper to handle tracts and atlas‐based segmentation of the human brain altogether under a framework for surgical path planning. The framework has a dynamic structure that gives the flexibility to add different path‐finding algorithms and generate different widths of surgical pathways. Moreover, surgeons can update the score table to guarantee minimally invasive surgery. The output file format of all the extracted surgical paths is NRRD, so it can be easily visualised, analysed, or processed over the third part software tools. Discussion In this study, we generated many possible surgical pathways then these pathways were evaluated by the surgeons the results were impressive because the framework could identify surgical pathways used in real‐world surgery that correspond to the standard pathways such as anterior transsylvian, trans sulcal, transgyral, and sub‐temporal. Conclusion This study proposes a new surgical path planning framework for neurosurgery. Moreover, in the future by adding/adopting different parameters (such as operation time, and short and long‐term complications after surgery) to the proposed framework, it would be possible to find new surgical pathways for difficult surgical conditions. |
Author | Ay, Eren Cem Albayrak, Nur Banu Duru, Nevcihan Eker, Ayşe Gül Dündar, Tolga Turan Doğan, İhsan Kurt Pehlivanoğlu, Meltem Mutluer, Ahmet Serdar |
Author_xml | – sequence: 1 givenname: Meltem orcidid: 0000-0002-7581-9390 surname: Kurt Pehlivanoğlu fullname: Kurt Pehlivanoğlu, Meltem email: meltem.kurt@kocaeli.edu.tr organization: Kocaeli University – sequence: 2 givenname: Eren Cem surname: Ay fullname: Ay, Eren Cem organization: Kocaeli University – sequence: 3 givenname: Ayşe Gül surname: Eker fullname: Eker, Ayşe Gül organization: Kocaeli University – sequence: 4 givenname: Nur Banu surname: Albayrak fullname: Albayrak, Nur Banu organization: Kocaeli Health and Technology University – sequence: 5 givenname: Nevcihan surname: Duru fullname: Duru, Nevcihan organization: Kocaeli Health and Technology University – sequence: 6 givenname: Ahmet Serdar orcidid: 0000-0002-4332-2531 surname: Mutluer fullname: Mutluer, Ahmet Serdar organization: Bezmialem Vakıf University – sequence: 7 givenname: Tolga Turan surname: Dündar fullname: Dündar, Tolga Turan organization: Bezmialem Vakıf University – sequence: 8 givenname: İhsan surname: Doğan fullname: Doğan, İhsan organization: Ankara University Faculty of Medicine |
BookMark | eNp10MtKAzEUBuAgFWyr4CMMuHEzNfdMlqV4g4LgBdyFNJPUqdNkTDqUvr2pFQXB1TmLj59z_hEY-OAtAOcIThCE-CqaNMFM8CMwRFRUJZP8dfCzM3QCRimtIKSMcjoEYlp4uy1SH5eN0W3R6c1b0bXa-8YvCxf12m5DfC9ciBn2MeyljbtTcOx0m-zZ9xyDl5vr59ldOX-4vZ9N56UhHPJSi4WtOIWMCoxlzUwNhbbOEEykxRUlTDgpeM0001pQIghHqKqJNgtjF9SRMbg85HYxfPQ2bdS6Sca2-UIb-qRwJaCUDFKU6cUfugp99Pk6hSVBjCAoyG-gyb-kaJ3qYrPWcacQVPsGVW5Q7RvMtDzQbdPa3b9OPc6evvwnDDRybQ |
CitedBy_id | crossref_primary_10_3390_app14093687 |
Cites_doi | 10.1109/ROBOT.2005.1570348 10.1016/j.jneumeth.2022.109566 10.1109/ICRA40945.2020.9196954 10.1016/S0090‐3019(03)00382‐3 10.1109/IROS.2009.5354787 10.1109/tro.2014.2307633 10.1007/s10278‐017‐0037‐8 10.1109/ICRA46639.2022.9811679 10.1145/37402.37422 10.1016/j.nurt.2007.05.011 10.1007/s11042‐021‐11476‐w 10.1136/bmj.291.6488.130 10.1002/rcs.1998 10.1097/00006123‐200003000‐00046 10.1007/s10514‐022‐10042‐z 10.1007/978-981-19-1968-8_8 10.18637/jss.v086.i08 10.1016/j.neucom.2022.05.044 10.1017/s0263574713001161 10.1109/IROS40897.2019.8968153 10.1016/j.bspc.2022.103867 10.3389/fsurg.2022.863633 10.1177/0278364908097661 10.1109/IROS.2014.6942795 10.1088/1361‐6560/ac8fdd 10.1016/j.mri.2012.05.001 10.1007/s11548‐010‐0529‐1 10.1109/CCISP55629.2022.9974252 10.1007/978-981-19-5001-8_7 10.1109/tra.2003.817061 10.1007/s10143‐021‐01511‐7 10.1002/mrm.10682 10.1016/j.ifacol.2022.09.161 10.1080/10673220216231 10.3389/fninf.2013.00045 |
ContentType | Journal Article |
Copyright | 2023 The Authors. The International Journal of Medical Robotics and Computer Assisted Surgery published by John Wiley & Sons Ltd. 2023. This article is published 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. |
Copyright_xml | – notice: 2023 The Authors. The International Journal of Medical Robotics and Computer Assisted Surgery published by John Wiley & Sons Ltd. – notice: 2023. This article is published 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. |
DBID | 24P WIN AAYXX CITATION 7SC 7SP 7TB 8FD F28 FR3 JQ2 K9. L7M L~C L~D 7X8 |
DOI | 10.1002/rcs.2576 |
DatabaseName | Wiley Open Access Journals Wiley Online Library Free Content CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Mechanical & Transportation Engineering Abstracts Technology Research Database ANTE: Abstracts in New Technology & Engineering Engineering Research Database ProQuest Computer Science Collection ProQuest Health & Medical Complete (Alumni) Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional MEDLINE - Academic |
DatabaseTitle | CrossRef Technology Research Database Computer and Information Systems Abstracts – Academic Mechanical & Transportation Engineering Abstracts Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts ProQuest Health & Medical Complete (Alumni) Engineering Research Database Advanced Technologies Database with Aerospace ANTE: Abstracts in New Technology & Engineering Computer and Information Systems Abstracts Professional MEDLINE - Academic |
DatabaseTitleList | MEDLINE - Academic Technology Research Database |
Database_xml | – sequence: 1 dbid: 24P name: Wiley Open Access url: https://authorservices.wiley.com/open-science/open-access/browse-journals.html sourceTypes: Publisher |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Engineering |
EISSN | 1478-596X |
EndPage | n/a |
ExternalDocumentID | 10_1002_rcs_2576 RCS2576 |
Genre | article |
GroupedDBID | --- .3N .GA .Y3 05W 0R~ 10A 1L6 1OC 24P 31~ 33P 3SF 3WU 4.4 50Y 50Z 51W 51X 52M 52N 52O 52P 52R 52S 52T 52U 52V 52W 52X 53G 5GY 5VS 66C 702 7PT 8-0 8-1 8-3 8-4 8-5 8UM 930 A01 A03 AAESR AAEVG AAHHS AANLZ AAONW AASGY AAXRX AAZKR ABCQN ABCUV ABEML ABIJN ABQWH ABXGK ACAHQ ACBWZ ACCFJ ACCZN ACGFS ACGOF ACIWK ACMXC ACPOU ACSCC ACXBN ACXQS ADBBV ADBTR ADEOM ADIZJ ADKYN ADMGS ADOZA ADXAS ADZMN AEEZP AEIGN AEIMD AENEX AEQDE AEUQT AEUYR AFBPY AFFPM AFGKR AFPWT AFZJQ AHBTC AHMBA AIACR AITYG AIURR AIWBW AJBDE ALAGY ALMA_UNASSIGNED_HOLDINGS ALUQN AMBMR AMYDB ASPBG ATUGU AVWKF AZBYB AZFZN AZVAB BAFTC BDRZF BFHJK BHBCM BMXJE BROTX BRXPI BY8 C45 CS3 D-6 D-7 D-E D-F DCZOG DPXWK DR2 DRFUL DRMAN DRSTM DU5 EBD EBS EJD EMOBN F00 F01 F04 F5P FEDTE FUBAC G-S G.N GNP GODZA H.X HBH HF~ HGLYW HVGLF HZ~ IX1 J0M JPC KBYEO LATKE LAW LC2 LC3 LEEKS LH4 LITHE LOXES LP6 LP7 LUTES LW6 LYRES MEWTI MK4 MRFUL MRMAN MRSTM MSFUL MSMAN MSSTM MXFUL MXMAN MXSTM N04 N05 NF~ O66 O9- OIG OVD P2P P2W P2X P2Z P4B P4D PQQKQ Q.N Q11 QB0 QRW R.K ROL RWI RX1 RYL SJN SUPJJ SV3 TEORI UB1 W8V W99 WBKPD WHWMO WIH WIJ WIK WIN WOHZO WQJ WRC WVDHM WXI WXSBR XG1 XV2 ZZTAW ~IA ~WT AAMNL AAYXX ACRPL CITATION 7SC 7SP 7TB 8FD F28 FR3 JQ2 K9. L7M L~C L~D 7X8 |
ID | FETCH-LOGICAL-c3606-a7be8640547229d5cd07aefc3239e284357f976d5a5aa743736118d3acbceb4f3 |
IEDL.DBID | 24P |
ISSN | 1478-5951 |
IngestDate | Wed Dec 04 09:22:15 EST 2024 Mon Dec 16 17:33:27 EST 2024 Fri Dec 06 08:51:24 EST 2024 Sat Aug 24 00:53:45 EDT 2024 |
IsDoiOpenAccess | true |
IsOpenAccess | true |
IsPeerReviewed | true |
IsScholarly | true |
Issue | 1 |
Language | English |
License | Attribution |
LinkModel | DirectLink |
MergedId | FETCHMERGED-LOGICAL-c3606-a7be8640547229d5cd07aefc3239e284357f976d5a5aa743736118d3acbceb4f3 |
Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ORCID | 0000-0002-4332-2531 0000-0002-7581-9390 |
OpenAccessLink | https://onlinelibrary.wiley.com/doi/abs/10.1002%2Frcs.2576 |
PQID | 2931531073 |
PQPubID | 1026349 |
PageCount | 15 |
ParticipantIDs | proquest_miscellaneous_2870995041 proquest_journals_2931531073 crossref_primary_10_1002_rcs_2576 wiley_primary_10_1002_rcs_2576_RCS2576 |
PublicationCentury | 2000 |
PublicationDate | February 2024 |
PublicationDateYYYYMMDD | 2024-02-01 |
PublicationDate_xml | – month: 02 year: 2024 text: February 2024 |
PublicationDecade | 2020 |
PublicationPlace | Hamilton |
PublicationPlace_xml | – name: Hamilton |
PublicationTitle | The international journal of medical robotics + computer assisted surgery |
PublicationYear | 2024 |
Publisher | Wiley Subscription Services, Inc |
Publisher_xml | – sequence: 0 name: Wiley Subscription Services, Inc |
References | 2022; 374 2004; 61 2021; 44 2000; 46 2019; 15 2002; 10 2009 2022; 67 2022; 46 2005 2003; 19 2008; 50 2013; 7 2011; 6 2018; 86 2012; 30 2004; 51 1987; 21 2022; 81 2022 2020 2019; 10951 2008; 27 2022; 9 2022; 78 2019 2007; 4 2015 2014 2022; 55 1985; 291 2014; 30 2018; 31 2022; 500 2014; 32 e_1_2_9_30_1 e_1_2_9_31_1 e_1_2_9_11_1 e_1_2_9_10_1 e_1_2_9_35_1 e_1_2_9_13_1 e_1_2_9_32_1 e_1_2_9_12_1 e_1_2_9_33_1 Tadimety PR (e_1_2_9_38_1) 2015 Enchev YP (e_1_2_9_5_1) 2008; 50 Kotian RP (e_1_2_9_34_1) 2022 Wankhede A (e_1_2_9_23_1) 2019 e_1_2_9_15_1 e_1_2_9_14_1 e_1_2_9_39_1 e_1_2_9_17_1 e_1_2_9_36_1 e_1_2_9_16_1 e_1_2_9_37_1 e_1_2_9_19_1 e_1_2_9_18_1 e_1_2_9_41_1 e_1_2_9_20_1 e_1_2_9_40_1 e_1_2_9_22_1 e_1_2_9_21_1 e_1_2_9_24_1 e_1_2_9_8_1 e_1_2_9_7_1 e_1_2_9_6_1 e_1_2_9_4_1 e_1_2_9_3_1 e_1_2_9_2_1 e_1_2_9_9_1 e_1_2_9_26_1 e_1_2_9_25_1 e_1_2_9_28_1 e_1_2_9_27_1 e_1_2_9_29_1 |
References_xml | – start-page: 1775 year: 2014 end-page: 1781 – start-page: 248 year: 2022 end-page: 253 – volume: 10 start-page: 324 issue: 6 year: 2002 end-page: 336 article-title: Diffusion tensor imaging and its application to neuropsychiatric disorders publication-title: Harv Rev Psychiatry – start-page: 7731 year: 2022 end-page: 7737 – volume: 51 start-page: 413 issue: 2 year: 2004 end-page: 417 article-title: Does fractional anisotropy have better noise immunity characteristics than relative anisotropy in diffusion tensor mri? an analytical approach publication-title: Magn Reson Med – volume: 27 start-page: 1361 issue: 11‐12 year: 2008 end-page: 1374 article-title: Motion planning under uncertainty for image‐guided medical needle steering publication-title: Int J Robot Res – volume: 86 issue: 8 year: 2018 article-title: Image segmentation, registration and characterization in with SimpleITK publication-title: J Stat Software – volume: 32 start-page: 985 issue: 6 year: 2014 end-page: 1004 article-title: A new geometry‐based plan for inserting flexible needles to reach multiple targets publication-title: Robotica – volume: 31 start-page: 290 issue: 3 year: 2018 end-page: 303 article-title: SimpleITK image‐analysis notebooks: a collaborative environment for education and reproducible research publication-title: J Digit Imag – start-page: 83 year: 2022 end-page: 90 – volume: 78 year: 2022 article-title: Accurate preoperative path planning with coarse‐to‐refine segmentation for image guided deep brain stimulation publication-title: Biomed Signal Process Control – volume: 46 start-page: 754 issue: 3 year: 2000 end-page: 759 article-title: Cranial base surgical techniques for large sphenocavernous meningiomas publication-title: Neurosurgery – start-page: 1640 year: 2005 end-page: 1645 – volume: 30 start-page: 853 issue: 4 year: 2014 end-page: 864 article-title: Needle steering in 3‐d via rapid replanning publication-title: IEEE Trans Robot – volume: 67 issue: 19 year: 2022 article-title: Flexible needle puncture path planning for liver tumors based on deep reinforcement learning publication-title: Phys Med Biol – volume: 30 start-page: 1323 issue: 9 year: 2012 end-page: 1341 article-title: 3d slicer as an image computing platform for the quantitative imaging network publication-title: Magn Reson Imag – volume: 7 year: 2013 article-title: The design of SimpleITK publication-title: Front Neuroinf – volume: 46 start-page: 645 issue: 5 year: 2022 end-page: 666 article-title: A hybrid inductive learning‐based and deductive reasoning‐based 3‐d path planning method in complex environments publication-title: Aut Robots – volume: 44 start-page: 1 issue: 6 year: 2021 end-page: 18 article-title: Complications of cranioplasty in relationship to traumatic brain injury: a systematic review and meta‐analysis publication-title: Neurosurg Rev – volume: 9 year: 2022 article-title: Machine learning‐based surgical planning for neurosurgery: artificial intelligent approaches to the cranium publication-title: Front Surg – volume: 15 issue: 4 year: 2019 article-title: 3d path planning for flexible needle steering in neurosurgery publication-title: Int J Med Robot – volume: 81 start-page: 1 issue: 25 year: 2022 end-page: 18 article-title: Multi‐objective path planning for lung biopsy surgery publication-title: Multimedia Tools Appl – volume: 500 start-page: 567 year: 2022 end-page: 580 article-title: Surgical gan: towards real‐time path planning for passive flexible tools in endovascular surgeries publication-title: Neurocomputing – start-page: 93 year: 2022 end-page: 101 – volume: 61 start-page: 60 issue: 1 year: 2004 end-page: 67 article-title: Small petrosal approach to the middle portion of the mediobasal temporal region: technical case report publication-title: Surg Neurol – volume: 19 start-page: 854 issue: 5 year: 2003 end-page: 863 article-title: Optimal planning for minimally invasive surgical robots publication-title: IEEE Trans Robot Autom – start-page: 2429 year: 2020 end-page: 2435 – volume: 4 start-page: 316 issue: 3 year: 2007 end-page: 329 article-title: Diffusion tensor imaging of the brain publication-title: Neurotherapeutics – volume: 55 start-page: 600 issue: 20 year: 2022 end-page: 605 article-title: Optimal path planning for stereotactic neurosurgery based on an elastostatic cannula model publication-title: IFAC‐PapersOnLine – start-page: 115 year: 2015 end-page: 121 – year: 2022 – start-page: 4517 year: 2009 end-page: 4522 – volume: 291 start-page: 130 issue: 6488 year: 1985 end-page: 131 article-title: “primum non nocere” and the principle of non‐maleficence publication-title: Br Med J – volume: 6 start-page: 565 issue: 5 year: 2011 end-page: 572 article-title: Neurosurgical craniotomy localization using a virtual reality planning system versus intraoperative image–guided navigation publication-title: Int J Comput Assist Radiol Surg – volume: 50 start-page: 5 issue: 2 year: 2008 end-page: 10 article-title: Cranial neuronavigation‐a step forward or a step aside in modern neurosurgery publication-title: Folia Med Plovdiv – start-page: 3302 year: 2019 end-page: 3307 – volume: 374 year: 2022 article-title: A hybrid high‐resolution anatomical mri atlas with sub‐parcellation of cortical gyri using resting fmri publication-title: J Neurosci Methods – volume: 10951 start-page: 655 year: 2019 end-page: 663 – volume: 21 start-page: 163 issue: 4 year: 1987 end-page: 169 article-title: Marching cubes: a high resolution 3D surface construction algorithm publication-title: ACM SIGGRAPH Comput Graph – volume: 50 start-page: 5 issue: 2 year: 2008 ident: e_1_2_9_5_1 article-title: Cranial neuronavigation‐a step forward or a step aside in modern neurosurgery publication-title: Folia Med Plovdiv contributor: fullname: Enchev YP – ident: e_1_2_9_8_1 doi: 10.1109/ROBOT.2005.1570348 – ident: e_1_2_9_29_1 doi: 10.1016/j.jneumeth.2022.109566 – ident: e_1_2_9_19_1 doi: 10.1109/ICRA40945.2020.9196954 – ident: e_1_2_9_41_1 doi: 10.1016/S0090‐3019(03)00382‐3 – ident: e_1_2_9_28_1 – ident: e_1_2_9_15_1 doi: 10.1109/IROS.2009.5354787 – ident: e_1_2_9_16_1 doi: 10.1109/tro.2014.2307633 – ident: e_1_2_9_31_1 doi: 10.1007/s10278‐017‐0037‐8 – ident: e_1_2_9_24_1 doi: 10.1109/ICRA46639.2022.9811679 – ident: e_1_2_9_40_1 doi: 10.1145/37402.37422 – ident: e_1_2_9_33_1 doi: 10.1016/j.nurt.2007.05.011 – ident: e_1_2_9_12_1 doi: 10.1007/s11042‐021‐11476‐w – ident: e_1_2_9_3_1 doi: 10.1136/bmj.291.6488.130 – ident: e_1_2_9_21_1 doi: 10.1002/rcs.1998 – ident: e_1_2_9_7_1 doi: 10.1097/00006123‐200003000‐00046 – start-page: 115 volume-title: Dijkstra’s Algorithm — the First Look year: 2015 ident: e_1_2_9_38_1 contributor: fullname: Tadimety PR – ident: e_1_2_9_20_1 doi: 10.1007/s10514‐022‐10042‐z – ident: e_1_2_9_39_1 doi: 10.1007/978-981-19-1968-8_8 – ident: e_1_2_9_30_1 doi: 10.18637/jss.v086.i08 – ident: e_1_2_9_18_1 doi: 10.1016/j.neucom.2022.05.044 – ident: e_1_2_9_11_1 doi: 10.1017/s0263574713001161 – ident: e_1_2_9_14_1 doi: 10.1109/IROS40897.2019.8968153 – ident: e_1_2_9_22_1 doi: 10.1016/j.bspc.2022.103867 – ident: e_1_2_9_26_1 doi: 10.3389/fsurg.2022.863633 – ident: e_1_2_9_9_1 doi: 10.1177/0278364908097661 – ident: e_1_2_9_10_1 doi: 10.1109/IROS.2014.6942795 – ident: e_1_2_9_17_1 doi: 10.1088/1361‐6560/ac8fdd – ident: e_1_2_9_27_1 doi: 10.1016/j.mri.2012.05.001 – ident: e_1_2_9_6_1 doi: 10.1007/s11548‐010‐0529‐1 – ident: e_1_2_9_13_1 doi: 10.1109/CCISP55629.2022.9974252 – start-page: 93 volume-title: Diffusion‐Tensor Imaging and Fractional Anisotropy Protocol at 1.5‐T MRI for Early Parkinson’s Disease year: 2022 ident: e_1_2_9_34_1 doi: 10.1007/978-981-19-5001-8_7 contributor: fullname: Kotian RP – ident: e_1_2_9_2_1 doi: 10.1109/tra.2003.817061 – ident: e_1_2_9_4_1 doi: 10.1007/s10143‐021‐01511‐7 – ident: e_1_2_9_35_1 doi: 10.1002/mrm.10682 – start-page: 655 volume-title: Medical Imaging 2019: Image‐Guided Procedures, Robotic Interventions, and Modeling year: 2019 ident: e_1_2_9_23_1 contributor: fullname: Wankhede A – ident: e_1_2_9_25_1 doi: 10.1016/j.ifacol.2022.09.161 – ident: e_1_2_9_37_1 – ident: e_1_2_9_36_1 doi: 10.1080/10673220216231 – ident: e_1_2_9_32_1 doi: 10.3389/fninf.2013.00045 |
SSID | ssj0045464 |
Score | 2.39326 |
Snippet | Background
Despite using a variety of path‐finding algorithms that use tracts, the most significant advancement in this study is considering the values of all... BackgroundDespite using a variety of path‐finding algorithms that use tracts, the most significant advancement in this study is considering the values of all... BACKGROUNDDespite using a variety of path-finding algorithms that use tracts, the most significant advancement in this study is considering the values of all... |
SourceID | proquest crossref wiley |
SourceType | Aggregation Database Publisher |
StartPage | e2576 |
SubjectTerms | Algorithms Anisotropy Brain brain atlases Damage assessment Dijkstra Evaluation Image processing Minimally invasive surgery Neurosurgery Path planning Planning Software Surgeons Surgery surgical path planning Tracks (paths) |
Title | A new surgical path planning framework for neurosurgery |
URI | https://onlinelibrary.wiley.com/doi/abs/10.1002%2Frcs.2576 https://www.proquest.com/docview/2931531073 https://search.proquest.com/docview/2870995041 |
Volume | 20 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV07T8MwED5BWWBAPEWhICMhtlA3tuNkrApVhQRChUrdItuxx7Tq4__37CRtGZBYksGOo5zPd9855-8Anqhz3HHOo8SpIuLc0ChlNI3SxApllTYy0C5-fCajCX-fimmdVenPwlT8ENsNN78ygr32C1zpZXdHGrowyxePlg_hCFFN4qsXxPyrscJc8EAd1eMYJQmEEQ3xLI27zZO_XdEOX-6j1OBmhmdwWuND0q8m9BwObHkBJ3usgZcg-wSxMFmuF8FqEV9UmMzr4kPENdlWBOEoqegqq6PPVzAZvv0MRlFd_yAyDOOKSElt0wQRlSd0zAphCiqVdYbFLLPoVpiQDtFEIZRQSnqOogTDhYIpo43V3LFraJWz0t4AkaIwxlCWFjgvDDFFqlSsMhyGegZB3YbHRhT5vKK5yCtC4zhHceVeXG3oNDLKa0XHhoyhzcQYkuEQ22ZUUf_fQZV2tsY-aBOyTFDea8NzkO2f78jHg29_v_1vxzs4xk_gVR51B1qrxdreI0xY6YegD3h9Hccb5ki47g |
link.rule.ids | 314,780,784,1375,11562,27924,27925,46052,46294,46476,46718 |
linkProvider | Wiley-Blackwell |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LTwIxEJ4gHtSDbyOKWhPjbaFs233EE0EJKnBASDiYbLrd7sUECLAXf73TfQCamBhPe2i3u512pt-0028A7mgc85hzbjmxjCzOFbU8Rj3Lc7SQWobKTWkXe32nM-IvYzEuwUNxFybjh1htuBnNSO21UXCzIV1fs4bO1aJm4PIWbKO2N0w81-NgxR3FBU-5oxoc3SSBOKJgnqV2vXjz-1q0BpibMDVdZ9oH8F78YRZe8lFLlmFNff4gb_xnFw5hP8efpJlNmCMo6ckx7G2wEp6A2ySItckimadWkZikxWSWJzcicRHNRRDukowOM7tafQqj9tOw1bHy_AqWYui3WNINtecgYjOEkX4kVERdqWPFbOZrXLaYcGNEK5GQQkrXcCA56I5ETKpQ6ZDH7AzKk-lEnwNxRaSUosyLcNwZYhZPSlv62Aw1DIVhBW4LSQezjEYjyAiT7QCFEBghVKBaDEGQKxIW-AxtMvqoDJtYFaMKmHMNOdHTBOugzfF9QXmjAvepvH_9RjBovZnnxV8r3sBOZ9jrBt3n_usl7GJ3eBazXYXycp7oK4Qky_A6nXpfYYrcCA |
linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnZ1JSwMxFMcfWkH04C5Wq0YQb9Omk2SWY7GWuhWpFgoehkwmuQjT0uXip_dllrYKgniaQzKZyfJe_m8m-QXgmhrDDefc8YxMHM4VdQJGAyfwtJBaxsrPsIvPPa874A9DMSxWVdq9MDkfYvHBzVpG5q-tgY8T01hCQydqWrdqeR02uOeGlpvf7i_QUVzwDB3V5BglCZQRJXiWuo3yzu9T0VJfrqrUbJrp7MJ7-YL56pKP-nwW19XnD3bj_2qwBzuF-iStfLjsw5pOD2B7hUl4CH6LoNIm0_kk84nEHllMxsXRRsSUa7kIil2SwzDzjdVHMOjcvd12neJ0BUcxjFoc6cc68FCvWVxkmAiVUF9qo5jLQo2TFhO-Qa2SCCmk9C0BycNgJGFSxUrH3LBjqKSjVJ8A8UWilKIsSLDXGSqWQEpXhlgMtXzCuApXZUNH4xyiEeW4ZDfCRohsI1ShVvZAVJgRJoQMPTJGqAyLWCSjAdi_GjLVoznmQY8ThoLyZhVusub-9RlR__bVXk__mvESNl_anejpvvd4BltYG54v2K5BZTaZ63PUI7P4Iht4X_Di2rc |
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=A+new+surgical+path+planning+framework+for+neurosurgery&rft.jtitle=The+international+journal+of+medical+robotics+%2B+computer+assisted+surgery&rft.au=Kurt+Pehlivano%C4%9Flu%2C+Meltem&rft.au=Ay%2C+Eren+Cem&rft.au=Eker%2C+Ay%C5%9Fe+G%C3%BCl&rft.au=Albayrak%2C+Nur+Banu&rft.date=2024-02-01&rft.issn=1478-5951&rft.eissn=1478-596X&rft.volume=20&rft.issue=1&rft.epage=n%2Fa&rft_id=info:doi/10.1002%2Frcs.2576&rft.externalDBID=10.1002%252Frcs.2576&rft.externalDocID=RCS2576 |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1478-5951&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1478-5951&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1478-5951&client=summon |