Augmented reality in laparoscopic liver resection evaluated on an ex-vivo animal model with pseudo-tumours
Background The aim of this study was to assess the performance of our augmented reality (AR) software (Hepataug) during laparoscopic resection of liver tumours and compare it to standard ultrasonography (US). Materials and methods Ninety pseudo-tumours ranging from 10 to 20 mm were created in sheep...
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description | Background
The aim of this study was to assess the performance of our augmented reality (AR) software (Hepataug) during laparoscopic resection of liver tumours and compare it to standard ultrasonography (US).
Materials and methods
Ninety pseudo-tumours ranging from 10 to 20 mm were created in sheep cadaveric livers by injection of alginate. CT-scans were then performed and 3D models reconstructed using a medical image segmentation software (MITK). The livers were placed in a pelvi-trainer on an inclined plane, approximately perpendicular to the laparoscope. The aim was to obtain free resection margins, as close as possible to 1 cm. Laparoscopic resection was performed using US alone (
n
= 30, US group), AR alone (
n
= 30, AR group) and both US and AR (
n
= 30, ARUS group). R0 resection, maximal margins, minimal margins and mean margins were assessed after histopathologic examination, adjusted to the tumour depth and to a liver zone-wise difficulty level.
Results
The minimal margins were not different between the three groups (8.8, 8.0 and 6.9 mm in the US, AR and ARUS groups, respectively). The maximal margins were larger in the US group compared to the AR and ARUS groups after adjustment on depth and zone difficulty (21 vs. 18 mm,
p
= 0.001 and 21 vs. 19.5 mm,
p
= 0.037, respectively). The mean margins, which reflect the variability of the measurements, were larger in the US group than in the ARUS group after adjustment on depth and zone difficulty (15.2 vs. 12.8 mm,
p
|
doi_str_mv | 10.1007/s00464-021-08798-z |
format | Article |
fullrecord | <record><control><sourceid>proquest_hal_p</sourceid><recordid>TN_cdi_hal_primary_oai_HAL_hal_03681251v1</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2617592461</sourcerecordid><originalsourceid>FETCH-LOGICAL-c409t-781224e9fac3b98cce73bf50178250c81e9f0cff5dc4db7a09cd0a5264bda2f63</originalsourceid><addsrcrecordid>eNp9kU1v1DAQhi0EokvhD3BAkbjAwTD-iuPjqqIUaSUucLYcx2m9cuJgJ4H219dLSpE4cLLH88w7M34Rek3gAwGQHzMArzkGSjA0UjX47gnaEc4oppQ0T9EOFANMpeJn6EXORyi8IuI5OmNcMs5A7NBxv1wPbpxdVyVngp9vKz9WwUwmxWzj5G0V_OpSyWZnZx_Hyq0mLOZUUQJT4l949WssVz-YUA2xc6H66eebaspu6SKelyEuKb9Ez3oTsnv1cJ6j75efvl1c4cPXz18u9gdsOagZy4ZQyp3qjWWtaqx1krW9ACIbKsA2pKTA9r3oLO9aaUDZDoygNW87Q_uanaP3m-6NCXpKZah0q6Px-mp_0Kc3YHXpIchKCvtuY6cUfywuz3rw2boQzOjikjUVitXAKIOCvv0HPZatxrKJpjWRQlFenwTpRtnyfzm5_nECAvrkmt5c08U1_ds1fVeK3jxIL-3guseSPzYVgG1ALqnx2qW_vf8jew_OQKO5</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2617592461</pqid></control><display><type>article</type><title>Augmented reality in laparoscopic liver resection evaluated on an ex-vivo animal model with pseudo-tumours</title><source>MEDLINE</source><source>SpringerLink Journals - AutoHoldings</source><creator>Adballah, Mourad ; Espinel, Yamid ; Calvet, Lilian ; Pereira, Bruno ; Le Roy, Bertrand ; Bartoli, Adrien ; Buc, Emmanuel</creator><creatorcontrib>Adballah, Mourad ; Espinel, Yamid ; Calvet, Lilian ; Pereira, Bruno ; Le Roy, Bertrand ; Bartoli, Adrien ; Buc, Emmanuel</creatorcontrib><description>Background
The aim of this study was to assess the performance of our augmented reality (AR) software (Hepataug) during laparoscopic resection of liver tumours and compare it to standard ultrasonography (US).
Materials and methods
Ninety pseudo-tumours ranging from 10 to 20 mm were created in sheep cadaveric livers by injection of alginate. CT-scans were then performed and 3D models reconstructed using a medical image segmentation software (MITK). The livers were placed in a pelvi-trainer on an inclined plane, approximately perpendicular to the laparoscope. The aim was to obtain free resection margins, as close as possible to 1 cm. Laparoscopic resection was performed using US alone (
n
= 30, US group), AR alone (
n
= 30, AR group) and both US and AR (
n
= 30, ARUS group). R0 resection, maximal margins, minimal margins and mean margins were assessed after histopathologic examination, adjusted to the tumour depth and to a liver zone-wise difficulty level.
Results
The minimal margins were not different between the three groups (8.8, 8.0 and 6.9 mm in the US, AR and ARUS groups, respectively). The maximal margins were larger in the US group compared to the AR and ARUS groups after adjustment on depth and zone difficulty (21 vs. 18 mm,
p
= 0.001 and 21 vs. 19.5 mm,
p
= 0.037, respectively). The mean margins, which reflect the variability of the measurements, were larger in the US group than in the ARUS group after adjustment on depth and zone difficulty (15.2 vs. 12.8 mm,
p
< 0.001). When considering only the most difficult zone (difficulty 3), there were more R1/R2 resections in the US group than in the AR + ARUS group (50% vs. 21%,
p
= 0.019).
Conclusion
Laparoscopic liver resection using AR seems to provide more accurate resection margins with less variability than the gold standard US navigation, particularly in difficult to access liver zones with deep tumours.</description><identifier>ISSN: 0930-2794</identifier><identifier>EISSN: 1432-2218</identifier><identifier>DOI: 10.1007/s00464-021-08798-z</identifier><identifier>PMID: 34734305</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Abdominal Surgery ; Animals ; Augmented Reality ; Computer Science ; Computer Vision and Pattern Recognition ; Disease Models, Animal ; Dynamic Manuscript ; Gastroenterology ; Gynecology ; Hepatology ; Imaging, Three-Dimensional ; Laparoscopy ; Laparoscopy - methods ; Liver ; Liver cancer ; Liver Neoplasms - diagnostic imaging ; Liver Neoplasms - surgery ; Medicine ; Medicine & Public Health ; Proctology ; Sheep ; Surgery ; Tumors</subject><ispartof>Surgical endoscopy, 2022, Vol.36 (1), p.833-843</ispartof><rights>The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021</rights><rights>2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.</rights><rights>The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021.</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c409t-781224e9fac3b98cce73bf50178250c81e9f0cff5dc4db7a09cd0a5264bda2f63</citedby><cites>FETCH-LOGICAL-c409t-781224e9fac3b98cce73bf50178250c81e9f0cff5dc4db7a09cd0a5264bda2f63</cites><orcidid>0000-0002-3559-166X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00464-021-08798-z$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00464-021-08798-z$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>230,314,776,780,881,4010,27902,27903,27904,41467,42536,51297</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34734305$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://uca.hal.science/hal-03681251$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Adballah, Mourad</creatorcontrib><creatorcontrib>Espinel, Yamid</creatorcontrib><creatorcontrib>Calvet, Lilian</creatorcontrib><creatorcontrib>Pereira, Bruno</creatorcontrib><creatorcontrib>Le Roy, Bertrand</creatorcontrib><creatorcontrib>Bartoli, Adrien</creatorcontrib><creatorcontrib>Buc, Emmanuel</creatorcontrib><title>Augmented reality in laparoscopic liver resection evaluated on an ex-vivo animal model with pseudo-tumours</title><title>Surgical endoscopy</title><addtitle>Surg Endosc</addtitle><addtitle>Surg Endosc</addtitle><description>Background
The aim of this study was to assess the performance of our augmented reality (AR) software (Hepataug) during laparoscopic resection of liver tumours and compare it to standard ultrasonography (US).
Materials and methods
Ninety pseudo-tumours ranging from 10 to 20 mm were created in sheep cadaveric livers by injection of alginate. CT-scans were then performed and 3D models reconstructed using a medical image segmentation software (MITK). The livers were placed in a pelvi-trainer on an inclined plane, approximately perpendicular to the laparoscope. The aim was to obtain free resection margins, as close as possible to 1 cm. Laparoscopic resection was performed using US alone (
n
= 30, US group), AR alone (
n
= 30, AR group) and both US and AR (
n
= 30, ARUS group). R0 resection, maximal margins, minimal margins and mean margins were assessed after histopathologic examination, adjusted to the tumour depth and to a liver zone-wise difficulty level.
Results
The minimal margins were not different between the three groups (8.8, 8.0 and 6.9 mm in the US, AR and ARUS groups, respectively). The maximal margins were larger in the US group compared to the AR and ARUS groups after adjustment on depth and zone difficulty (21 vs. 18 mm,
p
= 0.001 and 21 vs. 19.5 mm,
p
= 0.037, respectively). The mean margins, which reflect the variability of the measurements, were larger in the US group than in the ARUS group after adjustment on depth and zone difficulty (15.2 vs. 12.8 mm,
p
< 0.001). When considering only the most difficult zone (difficulty 3), there were more R1/R2 resections in the US group than in the AR + ARUS group (50% vs. 21%,
p
= 0.019).
Conclusion
Laparoscopic liver resection using AR seems to provide more accurate resection margins with less variability than the gold standard US navigation, particularly in difficult to access liver zones with deep tumours.</description><subject>Abdominal Surgery</subject><subject>Animals</subject><subject>Augmented Reality</subject><subject>Computer Science</subject><subject>Computer Vision and Pattern Recognition</subject><subject>Disease Models, Animal</subject><subject>Dynamic Manuscript</subject><subject>Gastroenterology</subject><subject>Gynecology</subject><subject>Hepatology</subject><subject>Imaging, Three-Dimensional</subject><subject>Laparoscopy</subject><subject>Laparoscopy - methods</subject><subject>Liver</subject><subject>Liver cancer</subject><subject>Liver Neoplasms - diagnostic imaging</subject><subject>Liver Neoplasms - surgery</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Proctology</subject><subject>Sheep</subject><subject>Surgery</subject><subject>Tumors</subject><issn>0930-2794</issn><issn>1432-2218</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNp9kU1v1DAQhi0EokvhD3BAkbjAwTD-iuPjqqIUaSUucLYcx2m9cuJgJ4H219dLSpE4cLLH88w7M34Rek3gAwGQHzMArzkGSjA0UjX47gnaEc4oppQ0T9EOFANMpeJn6EXORyi8IuI5OmNcMs5A7NBxv1wPbpxdVyVngp9vKz9WwUwmxWzj5G0V_OpSyWZnZx_Hyq0mLOZUUQJT4l949WssVz-YUA2xc6H66eebaspu6SKelyEuKb9Ez3oTsnv1cJ6j75efvl1c4cPXz18u9gdsOagZy4ZQyp3qjWWtaqx1krW9ACIbKsA2pKTA9r3oLO9aaUDZDoygNW87Q_uanaP3m-6NCXpKZah0q6Px-mp_0Kc3YHXpIchKCvtuY6cUfywuz3rw2boQzOjikjUVitXAKIOCvv0HPZatxrKJpjWRQlFenwTpRtnyfzm5_nECAvrkmt5c08U1_ds1fVeK3jxIL-3guseSPzYVgG1ALqnx2qW_vf8jew_OQKO5</recordid><startdate>2022</startdate><enddate>2022</enddate><creator>Adballah, Mourad</creator><creator>Espinel, Yamid</creator><creator>Calvet, Lilian</creator><creator>Pereira, Bruno</creator><creator>Le Roy, Bertrand</creator><creator>Bartoli, Adrien</creator><creator>Buc, Emmanuel</creator><general>Springer US</general><general>Springer Nature B.V</general><general>Springer Verlag (Germany)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>KB0</scope><scope>M0S</scope><scope>M1P</scope><scope>NAPCQ</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>1XC</scope><orcidid>https://orcid.org/0000-0002-3559-166X</orcidid></search><sort><creationdate>2022</creationdate><title>Augmented reality in laparoscopic liver resection evaluated on an ex-vivo animal model with pseudo-tumours</title><author>Adballah, Mourad ; Espinel, Yamid ; Calvet, Lilian ; Pereira, Bruno ; Le Roy, Bertrand ; Bartoli, Adrien ; Buc, Emmanuel</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c409t-781224e9fac3b98cce73bf50178250c81e9f0cff5dc4db7a09cd0a5264bda2f63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Abdominal Surgery</topic><topic>Animals</topic><topic>Augmented Reality</topic><topic>Computer Science</topic><topic>Computer Vision and Pattern Recognition</topic><topic>Disease Models, Animal</topic><topic>Dynamic Manuscript</topic><topic>Gastroenterology</topic><topic>Gynecology</topic><topic>Hepatology</topic><topic>Imaging, Three-Dimensional</topic><topic>Laparoscopy</topic><topic>Laparoscopy - methods</topic><topic>Liver</topic><topic>Liver cancer</topic><topic>Liver Neoplasms - diagnostic imaging</topic><topic>Liver Neoplasms - surgery</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Proctology</topic><topic>Sheep</topic><topic>Surgery</topic><topic>Tumors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Adballah, Mourad</creatorcontrib><creatorcontrib>Espinel, Yamid</creatorcontrib><creatorcontrib>Calvet, Lilian</creatorcontrib><creatorcontrib>Pereira, Bruno</creatorcontrib><creatorcontrib>Le Roy, Bertrand</creatorcontrib><creatorcontrib>Bartoli, Adrien</creatorcontrib><creatorcontrib>Buc, Emmanuel</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Nursing & Allied Health Database</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Nursing & Allied Health Premium</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>Hyper Article en Ligne (HAL)</collection><jtitle>Surgical endoscopy</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Adballah, Mourad</au><au>Espinel, Yamid</au><au>Calvet, Lilian</au><au>Pereira, Bruno</au><au>Le Roy, Bertrand</au><au>Bartoli, Adrien</au><au>Buc, Emmanuel</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Augmented reality in laparoscopic liver resection evaluated on an ex-vivo animal model with pseudo-tumours</atitle><jtitle>Surgical endoscopy</jtitle><stitle>Surg Endosc</stitle><addtitle>Surg Endosc</addtitle><date>2022</date><risdate>2022</risdate><volume>36</volume><issue>1</issue><spage>833</spage><epage>843</epage><pages>833-843</pages><issn>0930-2794</issn><eissn>1432-2218</eissn><abstract>Background
The aim of this study was to assess the performance of our augmented reality (AR) software (Hepataug) during laparoscopic resection of liver tumours and compare it to standard ultrasonography (US).
Materials and methods
Ninety pseudo-tumours ranging from 10 to 20 mm were created in sheep cadaveric livers by injection of alginate. CT-scans were then performed and 3D models reconstructed using a medical image segmentation software (MITK). The livers were placed in a pelvi-trainer on an inclined plane, approximately perpendicular to the laparoscope. The aim was to obtain free resection margins, as close as possible to 1 cm. Laparoscopic resection was performed using US alone (
n
= 30, US group), AR alone (
n
= 30, AR group) and both US and AR (
n
= 30, ARUS group). R0 resection, maximal margins, minimal margins and mean margins were assessed after histopathologic examination, adjusted to the tumour depth and to a liver zone-wise difficulty level.
Results
The minimal margins were not different between the three groups (8.8, 8.0 and 6.9 mm in the US, AR and ARUS groups, respectively). The maximal margins were larger in the US group compared to the AR and ARUS groups after adjustment on depth and zone difficulty (21 vs. 18 mm,
p
= 0.001 and 21 vs. 19.5 mm,
p
= 0.037, respectively). The mean margins, which reflect the variability of the measurements, were larger in the US group than in the ARUS group after adjustment on depth and zone difficulty (15.2 vs. 12.8 mm,
p
< 0.001). When considering only the most difficult zone (difficulty 3), there were more R1/R2 resections in the US group than in the AR + ARUS group (50% vs. 21%,
p
= 0.019).
Conclusion
Laparoscopic liver resection using AR seems to provide more accurate resection margins with less variability than the gold standard US navigation, particularly in difficult to access liver zones with deep tumours.</abstract><cop>New York</cop><pub>Springer US</pub><pmid>34734305</pmid><doi>10.1007/s00464-021-08798-z</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0002-3559-166X</orcidid></addata></record> |
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source | MEDLINE; SpringerLink Journals - AutoHoldings |
subjects | Abdominal Surgery Animals Augmented Reality Computer Science Computer Vision and Pattern Recognition Disease Models, Animal Dynamic Manuscript Gastroenterology Gynecology Hepatology Imaging, Three-Dimensional Laparoscopy Laparoscopy - methods Liver Liver cancer Liver Neoplasms - diagnostic imaging Liver Neoplasms - surgery Medicine Medicine & Public Health Proctology Sheep Surgery Tumors |
title | Augmented reality in laparoscopic liver resection evaluated on an ex-vivo animal model with pseudo-tumours |
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