Deep learning-based monocular placental pose estimation: towards collaborative robotics in fetoscopy
Purpose Twin-to-twin transfusion syndrome (TTTS) is a placental defect occurring in monochorionic twin pregnancies. It is associated with high risks of fetal loss and perinatal death. Fetoscopic elective laser ablation (ELA) of placental anastomoses has been established as the most effective therapy...
Ausführliche Beschreibung
Autor*in: |
Ahmad, Mirza Awais [verfasserIn] Ourak, Mouloud [verfasserIn] Gruijthuijsen, Caspar [verfasserIn] Deprest, Jan [verfasserIn] Vercauteren, Tom [verfasserIn] Vander Poorten, Emmanuel [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2020 |
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Übergeordnetes Werk: |
Enthalten in: International journal of computer assisted radiology and surgery - Berlin : Springer, 2006, 15(2020), 9 vom: 30. Apr., Seite 1561-1571 |
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Übergeordnetes Werk: |
volume:15 ; year:2020 ; number:9 ; day:30 ; month:04 ; pages:1561-1571 |
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DOI / URN: |
10.1007/s11548-020-02166-3 |
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Katalog-ID: |
SPR040621677 |
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520 | |a Purpose Twin-to-twin transfusion syndrome (TTTS) is a placental defect occurring in monochorionic twin pregnancies. It is associated with high risks of fetal loss and perinatal death. Fetoscopic elective laser ablation (ELA) of placental anastomoses has been established as the most effective therapy for TTTS. Current tools and techniques face limitations in case of more complex ELA cases. Visualization of the entire placental surface and vascular equator; maintaining an adequate distance and a close to perpendicular angle between laser fiber and placental surface are central for the effectiveness of laser ablation and procedural success. Robot-assisted technology could address these challenges, offer enhanced dexterity and ultimately improve the safety and effectiveness of the therapeutic procedures. Methods This work proposes a ‘minimal’ robotic TTTS approach whereby rather than deploying a massive and expensive robotic system, a compact instrument is ‘robotised’ and endowed with ‘robotic’ skills so that operators can quickly and efficiently use it. The work reports on automatic placental pose estimation in fetoscopic images. This estimator forms a key building block of a proposed shared-control approach for semi-autonomous fetoscopy. A convolutional neural network (CNN) is trained to predict the relative orientation of the placental surface from a single monocular fetoscope camera image. To overcome the absence of real-life ground-truth placenta pose data, similar to other works in literature (Handa et al. in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016; Gaidon et al. in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016; Vercauteren et al. in: Proceedings of the IEEE, 2019) the network is trained with data generated in a simulated environment and an in-silico phantom model. A limited set of coarsely manually labeled samples from real interventions are added to the training dataset to improve domain adaptation. Results The trained network shows promising results on unseen samples from synthetic, phantom and in vivo patient data. The performance of the network for collaborative control purposes was evaluated in a virtual reality simulator in which the virtual flexible distal tip was autonomously controlled by the neural network. Conclusion Improved alignment was established compared to manual operation for this setting, demonstrating the feasibility to incorporate a CNN-based estimator in a real-time shared control scheme for fetoscopic applications. | ||
650 | 4 | |a Deep learning |7 (dpeaa)DE-He213 | |
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650 | 4 | |a Robot assisted surgery |7 (dpeaa)DE-He213 | |
650 | 4 | |a Shared control |7 (dpeaa)DE-He213 | |
650 | 4 | |a Orientation estimation |7 (dpeaa)DE-He213 | |
650 | 4 | |a Fetoscopy |7 (dpeaa)DE-He213 | |
700 | 1 | |a Ourak, Mouloud |e verfasserin |4 aut | |
700 | 1 | |a Gruijthuijsen, Caspar |e verfasserin |4 aut | |
700 | 1 | |a Deprest, Jan |e verfasserin |4 aut | |
700 | 1 | |a Vercauteren, Tom |e verfasserin |4 aut | |
700 | 1 | |a Vander Poorten, Emmanuel |e verfasserin |4 aut | |
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2020 |
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10.1007/s11548-020-02166-3 doi (DE-627)SPR040621677 (SPR)s11548-020-02166-3-e DE-627 ger DE-627 rakwb eng 610 ASE 44.09 bkl 44.64 bkl 44.65 bkl Ahmad, Mirza Awais verfasserin aut Deep learning-based monocular placental pose estimation: towards collaborative robotics in fetoscopy 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose Twin-to-twin transfusion syndrome (TTTS) is a placental defect occurring in monochorionic twin pregnancies. It is associated with high risks of fetal loss and perinatal death. Fetoscopic elective laser ablation (ELA) of placental anastomoses has been established as the most effective therapy for TTTS. Current tools and techniques face limitations in case of more complex ELA cases. Visualization of the entire placental surface and vascular equator; maintaining an adequate distance and a close to perpendicular angle between laser fiber and placental surface are central for the effectiveness of laser ablation and procedural success. Robot-assisted technology could address these challenges, offer enhanced dexterity and ultimately improve the safety and effectiveness of the therapeutic procedures. Methods This work proposes a ‘minimal’ robotic TTTS approach whereby rather than deploying a massive and expensive robotic system, a compact instrument is ‘robotised’ and endowed with ‘robotic’ skills so that operators can quickly and efficiently use it. The work reports on automatic placental pose estimation in fetoscopic images. This estimator forms a key building block of a proposed shared-control approach for semi-autonomous fetoscopy. A convolutional neural network (CNN) is trained to predict the relative orientation of the placental surface from a single monocular fetoscope camera image. To overcome the absence of real-life ground-truth placenta pose data, similar to other works in literature (Handa et al. in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016; Gaidon et al. in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016; Vercauteren et al. in: Proceedings of the IEEE, 2019) the network is trained with data generated in a simulated environment and an in-silico phantom model. A limited set of coarsely manually labeled samples from real interventions are added to the training dataset to improve domain adaptation. Results The trained network shows promising results on unseen samples from synthetic, phantom and in vivo patient data. The performance of the network for collaborative control purposes was evaluated in a virtual reality simulator in which the virtual flexible distal tip was autonomously controlled by the neural network. Conclusion Improved alignment was established compared to manual operation for this setting, demonstrating the feasibility to incorporate a CNN-based estimator in a real-time shared control scheme for fetoscopic applications. Deep learning (dpeaa)DE-He213 Convolutional neural networks (dpeaa)DE-He213 Robot assisted surgery (dpeaa)DE-He213 Shared control (dpeaa)DE-He213 Orientation estimation (dpeaa)DE-He213 Fetoscopy (dpeaa)DE-He213 Ourak, Mouloud verfasserin aut Gruijthuijsen, Caspar verfasserin aut Deprest, Jan verfasserin aut Vercauteren, Tom verfasserin aut Vander Poorten, Emmanuel verfasserin aut Enthalten in International journal of computer assisted radiology and surgery Berlin : Springer, 2006 15(2020), 9 vom: 30. Apr., Seite 1561-1571 (DE-627)512299250 (DE-600)2235881-X 1861-6429 nnns volume:15 year:2020 number:9 day:30 month:04 pages:1561-1571 https://dx.doi.org/10.1007/s11548-020-02166-3 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.09 ASE 44.64 ASE 44.65 ASE AR 15 2020 9 30 04 1561-1571 |
spelling |
10.1007/s11548-020-02166-3 doi (DE-627)SPR040621677 (SPR)s11548-020-02166-3-e DE-627 ger DE-627 rakwb eng 610 ASE 44.09 bkl 44.64 bkl 44.65 bkl Ahmad, Mirza Awais verfasserin aut Deep learning-based monocular placental pose estimation: towards collaborative robotics in fetoscopy 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose Twin-to-twin transfusion syndrome (TTTS) is a placental defect occurring in monochorionic twin pregnancies. It is associated with high risks of fetal loss and perinatal death. Fetoscopic elective laser ablation (ELA) of placental anastomoses has been established as the most effective therapy for TTTS. Current tools and techniques face limitations in case of more complex ELA cases. Visualization of the entire placental surface and vascular equator; maintaining an adequate distance and a close to perpendicular angle between laser fiber and placental surface are central for the effectiveness of laser ablation and procedural success. Robot-assisted technology could address these challenges, offer enhanced dexterity and ultimately improve the safety and effectiveness of the therapeutic procedures. Methods This work proposes a ‘minimal’ robotic TTTS approach whereby rather than deploying a massive and expensive robotic system, a compact instrument is ‘robotised’ and endowed with ‘robotic’ skills so that operators can quickly and efficiently use it. The work reports on automatic placental pose estimation in fetoscopic images. This estimator forms a key building block of a proposed shared-control approach for semi-autonomous fetoscopy. A convolutional neural network (CNN) is trained to predict the relative orientation of the placental surface from a single monocular fetoscope camera image. To overcome the absence of real-life ground-truth placenta pose data, similar to other works in literature (Handa et al. in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016; Gaidon et al. in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016; Vercauteren et al. in: Proceedings of the IEEE, 2019) the network is trained with data generated in a simulated environment and an in-silico phantom model. A limited set of coarsely manually labeled samples from real interventions are added to the training dataset to improve domain adaptation. Results The trained network shows promising results on unseen samples from synthetic, phantom and in vivo patient data. The performance of the network for collaborative control purposes was evaluated in a virtual reality simulator in which the virtual flexible distal tip was autonomously controlled by the neural network. Conclusion Improved alignment was established compared to manual operation for this setting, demonstrating the feasibility to incorporate a CNN-based estimator in a real-time shared control scheme for fetoscopic applications. Deep learning (dpeaa)DE-He213 Convolutional neural networks (dpeaa)DE-He213 Robot assisted surgery (dpeaa)DE-He213 Shared control (dpeaa)DE-He213 Orientation estimation (dpeaa)DE-He213 Fetoscopy (dpeaa)DE-He213 Ourak, Mouloud verfasserin aut Gruijthuijsen, Caspar verfasserin aut Deprest, Jan verfasserin aut Vercauteren, Tom verfasserin aut Vander Poorten, Emmanuel verfasserin aut Enthalten in International journal of computer assisted radiology and surgery Berlin : Springer, 2006 15(2020), 9 vom: 30. Apr., Seite 1561-1571 (DE-627)512299250 (DE-600)2235881-X 1861-6429 nnns volume:15 year:2020 number:9 day:30 month:04 pages:1561-1571 https://dx.doi.org/10.1007/s11548-020-02166-3 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.09 ASE 44.64 ASE 44.65 ASE AR 15 2020 9 30 04 1561-1571 |
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10.1007/s11548-020-02166-3 doi (DE-627)SPR040621677 (SPR)s11548-020-02166-3-e DE-627 ger DE-627 rakwb eng 610 ASE 44.09 bkl 44.64 bkl 44.65 bkl Ahmad, Mirza Awais verfasserin aut Deep learning-based monocular placental pose estimation: towards collaborative robotics in fetoscopy 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose Twin-to-twin transfusion syndrome (TTTS) is a placental defect occurring in monochorionic twin pregnancies. It is associated with high risks of fetal loss and perinatal death. Fetoscopic elective laser ablation (ELA) of placental anastomoses has been established as the most effective therapy for TTTS. Current tools and techniques face limitations in case of more complex ELA cases. Visualization of the entire placental surface and vascular equator; maintaining an adequate distance and a close to perpendicular angle between laser fiber and placental surface are central for the effectiveness of laser ablation and procedural success. Robot-assisted technology could address these challenges, offer enhanced dexterity and ultimately improve the safety and effectiveness of the therapeutic procedures. Methods This work proposes a ‘minimal’ robotic TTTS approach whereby rather than deploying a massive and expensive robotic system, a compact instrument is ‘robotised’ and endowed with ‘robotic’ skills so that operators can quickly and efficiently use it. The work reports on automatic placental pose estimation in fetoscopic images. This estimator forms a key building block of a proposed shared-control approach for semi-autonomous fetoscopy. A convolutional neural network (CNN) is trained to predict the relative orientation of the placental surface from a single monocular fetoscope camera image. To overcome the absence of real-life ground-truth placenta pose data, similar to other works in literature (Handa et al. in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016; Gaidon et al. in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016; Vercauteren et al. in: Proceedings of the IEEE, 2019) the network is trained with data generated in a simulated environment and an in-silico phantom model. A limited set of coarsely manually labeled samples from real interventions are added to the training dataset to improve domain adaptation. Results The trained network shows promising results on unseen samples from synthetic, phantom and in vivo patient data. The performance of the network for collaborative control purposes was evaluated in a virtual reality simulator in which the virtual flexible distal tip was autonomously controlled by the neural network. Conclusion Improved alignment was established compared to manual operation for this setting, demonstrating the feasibility to incorporate a CNN-based estimator in a real-time shared control scheme for fetoscopic applications. Deep learning (dpeaa)DE-He213 Convolutional neural networks (dpeaa)DE-He213 Robot assisted surgery (dpeaa)DE-He213 Shared control (dpeaa)DE-He213 Orientation estimation (dpeaa)DE-He213 Fetoscopy (dpeaa)DE-He213 Ourak, Mouloud verfasserin aut Gruijthuijsen, Caspar verfasserin aut Deprest, Jan verfasserin aut Vercauteren, Tom verfasserin aut Vander Poorten, Emmanuel verfasserin aut Enthalten in International journal of computer assisted radiology and surgery Berlin : Springer, 2006 15(2020), 9 vom: 30. Apr., Seite 1561-1571 (DE-627)512299250 (DE-600)2235881-X 1861-6429 nnns volume:15 year:2020 number:9 day:30 month:04 pages:1561-1571 https://dx.doi.org/10.1007/s11548-020-02166-3 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.09 ASE 44.64 ASE 44.65 ASE AR 15 2020 9 30 04 1561-1571 |
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10.1007/s11548-020-02166-3 doi (DE-627)SPR040621677 (SPR)s11548-020-02166-3-e DE-627 ger DE-627 rakwb eng 610 ASE 44.09 bkl 44.64 bkl 44.65 bkl Ahmad, Mirza Awais verfasserin aut Deep learning-based monocular placental pose estimation: towards collaborative robotics in fetoscopy 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose Twin-to-twin transfusion syndrome (TTTS) is a placental defect occurring in monochorionic twin pregnancies. It is associated with high risks of fetal loss and perinatal death. Fetoscopic elective laser ablation (ELA) of placental anastomoses has been established as the most effective therapy for TTTS. Current tools and techniques face limitations in case of more complex ELA cases. Visualization of the entire placental surface and vascular equator; maintaining an adequate distance and a close to perpendicular angle between laser fiber and placental surface are central for the effectiveness of laser ablation and procedural success. Robot-assisted technology could address these challenges, offer enhanced dexterity and ultimately improve the safety and effectiveness of the therapeutic procedures. Methods This work proposes a ‘minimal’ robotic TTTS approach whereby rather than deploying a massive and expensive robotic system, a compact instrument is ‘robotised’ and endowed with ‘robotic’ skills so that operators can quickly and efficiently use it. The work reports on automatic placental pose estimation in fetoscopic images. This estimator forms a key building block of a proposed shared-control approach for semi-autonomous fetoscopy. A convolutional neural network (CNN) is trained to predict the relative orientation of the placental surface from a single monocular fetoscope camera image. To overcome the absence of real-life ground-truth placenta pose data, similar to other works in literature (Handa et al. in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016; Gaidon et al. in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016; Vercauteren et al. in: Proceedings of the IEEE, 2019) the network is trained with data generated in a simulated environment and an in-silico phantom model. A limited set of coarsely manually labeled samples from real interventions are added to the training dataset to improve domain adaptation. Results The trained network shows promising results on unseen samples from synthetic, phantom and in vivo patient data. The performance of the network for collaborative control purposes was evaluated in a virtual reality simulator in which the virtual flexible distal tip was autonomously controlled by the neural network. Conclusion Improved alignment was established compared to manual operation for this setting, demonstrating the feasibility to incorporate a CNN-based estimator in a real-time shared control scheme for fetoscopic applications. Deep learning (dpeaa)DE-He213 Convolutional neural networks (dpeaa)DE-He213 Robot assisted surgery (dpeaa)DE-He213 Shared control (dpeaa)DE-He213 Orientation estimation (dpeaa)DE-He213 Fetoscopy (dpeaa)DE-He213 Ourak, Mouloud verfasserin aut Gruijthuijsen, Caspar verfasserin aut Deprest, Jan verfasserin aut Vercauteren, Tom verfasserin aut Vander Poorten, Emmanuel verfasserin aut Enthalten in International journal of computer assisted radiology and surgery Berlin : Springer, 2006 15(2020), 9 vom: 30. Apr., Seite 1561-1571 (DE-627)512299250 (DE-600)2235881-X 1861-6429 nnns volume:15 year:2020 number:9 day:30 month:04 pages:1561-1571 https://dx.doi.org/10.1007/s11548-020-02166-3 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.09 ASE 44.64 ASE 44.65 ASE AR 15 2020 9 30 04 1561-1571 |
allfieldsSound |
10.1007/s11548-020-02166-3 doi (DE-627)SPR040621677 (SPR)s11548-020-02166-3-e DE-627 ger DE-627 rakwb eng 610 ASE 44.09 bkl 44.64 bkl 44.65 bkl Ahmad, Mirza Awais verfasserin aut Deep learning-based monocular placental pose estimation: towards collaborative robotics in fetoscopy 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose Twin-to-twin transfusion syndrome (TTTS) is a placental defect occurring in monochorionic twin pregnancies. It is associated with high risks of fetal loss and perinatal death. Fetoscopic elective laser ablation (ELA) of placental anastomoses has been established as the most effective therapy for TTTS. Current tools and techniques face limitations in case of more complex ELA cases. Visualization of the entire placental surface and vascular equator; maintaining an adequate distance and a close to perpendicular angle between laser fiber and placental surface are central for the effectiveness of laser ablation and procedural success. Robot-assisted technology could address these challenges, offer enhanced dexterity and ultimately improve the safety and effectiveness of the therapeutic procedures. Methods This work proposes a ‘minimal’ robotic TTTS approach whereby rather than deploying a massive and expensive robotic system, a compact instrument is ‘robotised’ and endowed with ‘robotic’ skills so that operators can quickly and efficiently use it. The work reports on automatic placental pose estimation in fetoscopic images. This estimator forms a key building block of a proposed shared-control approach for semi-autonomous fetoscopy. A convolutional neural network (CNN) is trained to predict the relative orientation of the placental surface from a single monocular fetoscope camera image. To overcome the absence of real-life ground-truth placenta pose data, similar to other works in literature (Handa et al. in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016; Gaidon et al. in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016; Vercauteren et al. in: Proceedings of the IEEE, 2019) the network is trained with data generated in a simulated environment and an in-silico phantom model. A limited set of coarsely manually labeled samples from real interventions are added to the training dataset to improve domain adaptation. Results The trained network shows promising results on unseen samples from synthetic, phantom and in vivo patient data. The performance of the network for collaborative control purposes was evaluated in a virtual reality simulator in which the virtual flexible distal tip was autonomously controlled by the neural network. Conclusion Improved alignment was established compared to manual operation for this setting, demonstrating the feasibility to incorporate a CNN-based estimator in a real-time shared control scheme for fetoscopic applications. Deep learning (dpeaa)DE-He213 Convolutional neural networks (dpeaa)DE-He213 Robot assisted surgery (dpeaa)DE-He213 Shared control (dpeaa)DE-He213 Orientation estimation (dpeaa)DE-He213 Fetoscopy (dpeaa)DE-He213 Ourak, Mouloud verfasserin aut Gruijthuijsen, Caspar verfasserin aut Deprest, Jan verfasserin aut Vercauteren, Tom verfasserin aut Vander Poorten, Emmanuel verfasserin aut Enthalten in International journal of computer assisted radiology and surgery Berlin : Springer, 2006 15(2020), 9 vom: 30. Apr., Seite 1561-1571 (DE-627)512299250 (DE-600)2235881-X 1861-6429 nnns volume:15 year:2020 number:9 day:30 month:04 pages:1561-1571 https://dx.doi.org/10.1007/s11548-020-02166-3 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.09 ASE 44.64 ASE 44.65 ASE AR 15 2020 9 30 04 1561-1571 |
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Deep learning Convolutional neural networks Robot assisted surgery Shared control Orientation estimation Fetoscopy |
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Ahmad, Mirza Awais @@aut@@ Ourak, Mouloud @@aut@@ Gruijthuijsen, Caspar @@aut@@ Deprest, Jan @@aut@@ Vercauteren, Tom @@aut@@ Vander Poorten, Emmanuel @@aut@@ |
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It is associated with high risks of fetal loss and perinatal death. Fetoscopic elective laser ablation (ELA) of placental anastomoses has been established as the most effective therapy for TTTS. Current tools and techniques face limitations in case of more complex ELA cases. Visualization of the entire placental surface and vascular equator; maintaining an adequate distance and a close to perpendicular angle between laser fiber and placental surface are central for the effectiveness of laser ablation and procedural success. Robot-assisted technology could address these challenges, offer enhanced dexterity and ultimately improve the safety and effectiveness of the therapeutic procedures. Methods This work proposes a ‘minimal’ robotic TTTS approach whereby rather than deploying a massive and expensive robotic system, a compact instrument is ‘robotised’ and endowed with ‘robotic’ skills so that operators can quickly and efficiently use it. The work reports on automatic placental pose estimation in fetoscopic images. This estimator forms a key building block of a proposed shared-control approach for semi-autonomous fetoscopy. A convolutional neural network (CNN) is trained to predict the relative orientation of the placental surface from a single monocular fetoscope camera image. To overcome the absence of real-life ground-truth placenta pose data, similar to other works in literature (Handa et al. in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016; Gaidon et al. in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016; Vercauteren et al. in: Proceedings of the IEEE, 2019) the network is trained with data generated in a simulated environment and an in-silico phantom model. A limited set of coarsely manually labeled samples from real interventions are added to the training dataset to improve domain adaptation. Results The trained network shows promising results on unseen samples from synthetic, phantom and in vivo patient data. The performance of the network for collaborative control purposes was evaluated in a virtual reality simulator in which the virtual flexible distal tip was autonomously controlled by the neural network. 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|
author |
Ahmad, Mirza Awais |
spellingShingle |
Ahmad, Mirza Awais ddc 610 bkl 44.09 bkl 44.64 bkl 44.65 misc Deep learning misc Convolutional neural networks misc Robot assisted surgery misc Shared control misc Orientation estimation misc Fetoscopy Deep learning-based monocular placental pose estimation: towards collaborative robotics in fetoscopy |
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1861-6429 |
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610 ASE 44.09 bkl 44.64 bkl 44.65 bkl Deep learning-based monocular placental pose estimation: towards collaborative robotics in fetoscopy Deep learning (dpeaa)DE-He213 Convolutional neural networks (dpeaa)DE-He213 Robot assisted surgery (dpeaa)DE-He213 Shared control (dpeaa)DE-He213 Orientation estimation (dpeaa)DE-He213 Fetoscopy (dpeaa)DE-He213 |
topic |
ddc 610 bkl 44.09 bkl 44.64 bkl 44.65 misc Deep learning misc Convolutional neural networks misc Robot assisted surgery misc Shared control misc Orientation estimation misc Fetoscopy |
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ddc 610 bkl 44.09 bkl 44.64 bkl 44.65 misc Deep learning misc Convolutional neural networks misc Robot assisted surgery misc Shared control misc Orientation estimation misc Fetoscopy |
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ddc 610 bkl 44.09 bkl 44.64 bkl 44.65 misc Deep learning misc Convolutional neural networks misc Robot assisted surgery misc Shared control misc Orientation estimation misc Fetoscopy |
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International journal of computer assisted radiology and surgery |
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International journal of computer assisted radiology and surgery |
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Deep learning-based monocular placental pose estimation: towards collaborative robotics in fetoscopy |
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Deep learning-based monocular placental pose estimation: towards collaborative robotics in fetoscopy |
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Ahmad, Mirza Awais |
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International journal of computer assisted radiology and surgery |
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International journal of computer assisted radiology and surgery |
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Ahmad, Mirza Awais Ourak, Mouloud Gruijthuijsen, Caspar Deprest, Jan Vercauteren, Tom Vander Poorten, Emmanuel |
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Ahmad, Mirza Awais |
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10.1007/s11548-020-02166-3 |
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610 |
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verfasserin |
title_sort |
deep learning-based monocular placental pose estimation: towards collaborative robotics in fetoscopy |
title_auth |
Deep learning-based monocular placental pose estimation: towards collaborative robotics in fetoscopy |
abstract |
Purpose Twin-to-twin transfusion syndrome (TTTS) is a placental defect occurring in monochorionic twin pregnancies. It is associated with high risks of fetal loss and perinatal death. Fetoscopic elective laser ablation (ELA) of placental anastomoses has been established as the most effective therapy for TTTS. Current tools and techniques face limitations in case of more complex ELA cases. Visualization of the entire placental surface and vascular equator; maintaining an adequate distance and a close to perpendicular angle between laser fiber and placental surface are central for the effectiveness of laser ablation and procedural success. Robot-assisted technology could address these challenges, offer enhanced dexterity and ultimately improve the safety and effectiveness of the therapeutic procedures. Methods This work proposes a ‘minimal’ robotic TTTS approach whereby rather than deploying a massive and expensive robotic system, a compact instrument is ‘robotised’ and endowed with ‘robotic’ skills so that operators can quickly and efficiently use it. The work reports on automatic placental pose estimation in fetoscopic images. This estimator forms a key building block of a proposed shared-control approach for semi-autonomous fetoscopy. A convolutional neural network (CNN) is trained to predict the relative orientation of the placental surface from a single monocular fetoscope camera image. To overcome the absence of real-life ground-truth placenta pose data, similar to other works in literature (Handa et al. in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016; Gaidon et al. in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016; Vercauteren et al. in: Proceedings of the IEEE, 2019) the network is trained with data generated in a simulated environment and an in-silico phantom model. A limited set of coarsely manually labeled samples from real interventions are added to the training dataset to improve domain adaptation. Results The trained network shows promising results on unseen samples from synthetic, phantom and in vivo patient data. The performance of the network for collaborative control purposes was evaluated in a virtual reality simulator in which the virtual flexible distal tip was autonomously controlled by the neural network. Conclusion Improved alignment was established compared to manual operation for this setting, demonstrating the feasibility to incorporate a CNN-based estimator in a real-time shared control scheme for fetoscopic applications. |
abstractGer |
Purpose Twin-to-twin transfusion syndrome (TTTS) is a placental defect occurring in monochorionic twin pregnancies. It is associated with high risks of fetal loss and perinatal death. Fetoscopic elective laser ablation (ELA) of placental anastomoses has been established as the most effective therapy for TTTS. Current tools and techniques face limitations in case of more complex ELA cases. Visualization of the entire placental surface and vascular equator; maintaining an adequate distance and a close to perpendicular angle between laser fiber and placental surface are central for the effectiveness of laser ablation and procedural success. Robot-assisted technology could address these challenges, offer enhanced dexterity and ultimately improve the safety and effectiveness of the therapeutic procedures. Methods This work proposes a ‘minimal’ robotic TTTS approach whereby rather than deploying a massive and expensive robotic system, a compact instrument is ‘robotised’ and endowed with ‘robotic’ skills so that operators can quickly and efficiently use it. The work reports on automatic placental pose estimation in fetoscopic images. This estimator forms a key building block of a proposed shared-control approach for semi-autonomous fetoscopy. A convolutional neural network (CNN) is trained to predict the relative orientation of the placental surface from a single monocular fetoscope camera image. To overcome the absence of real-life ground-truth placenta pose data, similar to other works in literature (Handa et al. in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016; Gaidon et al. in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016; Vercauteren et al. in: Proceedings of the IEEE, 2019) the network is trained with data generated in a simulated environment and an in-silico phantom model. A limited set of coarsely manually labeled samples from real interventions are added to the training dataset to improve domain adaptation. Results The trained network shows promising results on unseen samples from synthetic, phantom and in vivo patient data. The performance of the network for collaborative control purposes was evaluated in a virtual reality simulator in which the virtual flexible distal tip was autonomously controlled by the neural network. Conclusion Improved alignment was established compared to manual operation for this setting, demonstrating the feasibility to incorporate a CNN-based estimator in a real-time shared control scheme for fetoscopic applications. |
abstract_unstemmed |
Purpose Twin-to-twin transfusion syndrome (TTTS) is a placental defect occurring in monochorionic twin pregnancies. It is associated with high risks of fetal loss and perinatal death. Fetoscopic elective laser ablation (ELA) of placental anastomoses has been established as the most effective therapy for TTTS. Current tools and techniques face limitations in case of more complex ELA cases. Visualization of the entire placental surface and vascular equator; maintaining an adequate distance and a close to perpendicular angle between laser fiber and placental surface are central for the effectiveness of laser ablation and procedural success. Robot-assisted technology could address these challenges, offer enhanced dexterity and ultimately improve the safety and effectiveness of the therapeutic procedures. Methods This work proposes a ‘minimal’ robotic TTTS approach whereby rather than deploying a massive and expensive robotic system, a compact instrument is ‘robotised’ and endowed with ‘robotic’ skills so that operators can quickly and efficiently use it. The work reports on automatic placental pose estimation in fetoscopic images. This estimator forms a key building block of a proposed shared-control approach for semi-autonomous fetoscopy. A convolutional neural network (CNN) is trained to predict the relative orientation of the placental surface from a single monocular fetoscope camera image. To overcome the absence of real-life ground-truth placenta pose data, similar to other works in literature (Handa et al. in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016; Gaidon et al. in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016; Vercauteren et al. in: Proceedings of the IEEE, 2019) the network is trained with data generated in a simulated environment and an in-silico phantom model. A limited set of coarsely manually labeled samples from real interventions are added to the training dataset to improve domain adaptation. Results The trained network shows promising results on unseen samples from synthetic, phantom and in vivo patient data. The performance of the network for collaborative control purposes was evaluated in a virtual reality simulator in which the virtual flexible distal tip was autonomously controlled by the neural network. Conclusion Improved alignment was established compared to manual operation for this setting, demonstrating the feasibility to incorporate a CNN-based estimator in a real-time shared control scheme for fetoscopic applications. |
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container_issue |
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title_short |
Deep learning-based monocular placental pose estimation: towards collaborative robotics in fetoscopy |
url |
https://dx.doi.org/10.1007/s11548-020-02166-3 |
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author2 |
Ourak, Mouloud Gruijthuijsen, Caspar Deprest, Jan Vercauteren, Tom Vander Poorten, Emmanuel |
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Ourak, Mouloud Gruijthuijsen, Caspar Deprest, Jan Vercauteren, Tom Vander Poorten, Emmanuel |
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doi_str |
10.1007/s11548-020-02166-3 |
up_date |
2024-07-03T17:11:01.002Z |
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|
score |
7.4002895 |