Evaluación automática de la vista crítica de seguridad utilizando deep learning en la base de datos Cholec80-CVS
La colecistectomía laparoscópica es un procedimiento quirúrgico mínimamente invasivo utilizado para la extirpación de la vesícula biliar que puede provocar lesiones en el conducto biliar. Para prevenir estas lesiones, Strasberg y sus colegas propusieron el método Critical View of Safety para identif...
- Autores:
-
Mugnier Zuluaga, Andrés
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2023
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/68213
- Acceso en línea:
- http://hdl.handle.net/1992/68213
- Palabra clave:
- Deep learning
Colecistectomía laparoscópica
Machine learning
Inteligencia artificial
Computer vision
Vision transformer
Ingeniería
- Rights
- openAccess
- License
- Atribución 4.0 Internacional
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Evaluación automática de la vista crítica de seguridad utilizando deep learning en la base de datos Cholec80-CVS |
| title |
Evaluación automática de la vista crítica de seguridad utilizando deep learning en la base de datos Cholec80-CVS |
| spellingShingle |
Evaluación automática de la vista crítica de seguridad utilizando deep learning en la base de datos Cholec80-CVS Deep learning Colecistectomía laparoscópica Machine learning Inteligencia artificial Computer vision Vision transformer Ingeniería |
| title_short |
Evaluación automática de la vista crítica de seguridad utilizando deep learning en la base de datos Cholec80-CVS |
| title_full |
Evaluación automática de la vista crítica de seguridad utilizando deep learning en la base de datos Cholec80-CVS |
| title_fullStr |
Evaluación automática de la vista crítica de seguridad utilizando deep learning en la base de datos Cholec80-CVS |
| title_full_unstemmed |
Evaluación automática de la vista crítica de seguridad utilizando deep learning en la base de datos Cholec80-CVS |
| title_sort |
Evaluación automática de la vista crítica de seguridad utilizando deep learning en la base de datos Cholec80-CVS |
| dc.creator.fl_str_mv |
Mugnier Zuluaga, Andrés |
| dc.contributor.advisor.none.fl_str_mv |
Giraldo Trujillo, Luis Felipe |
| dc.contributor.author.none.fl_str_mv |
Mugnier Zuluaga, Andrés |
| dc.contributor.jury.none.fl_str_mv |
Segura Quijano, Fredy Enrique |
| dc.subject.keyword.none.fl_str_mv |
Deep learning Colecistectomía laparoscópica Machine learning Inteligencia artificial Computer vision Vision transformer |
| topic |
Deep learning Colecistectomía laparoscópica Machine learning Inteligencia artificial Computer vision Vision transformer Ingeniería |
| dc.subject.themes.es_CO.fl_str_mv |
Ingeniería |
| description |
La colecistectomía laparoscópica es un procedimiento quirúrgico mínimamente invasivo utilizado para la extirpación de la vesícula biliar que puede provocar lesiones en el conducto biliar. Para prevenir estas lesiones, Strasberg y sus colegas propusieron el método Critical View of Safety para identificar el conducto cístico y la arteria durante estos procedimientos. En este trabajo entrenamos modelos de aprendizaje profundo con el primer conjunto de datos de código abierto de vídeos de colistectomía laparoscópica que contienen anotaciones de los criterios de Strasberg, llamado Cholec80-CVS. Este estudio representa el primer intento de investigar el desempeño de los modelos de aprendizaje profundo para ayudar a identificar la vista crítica de seguridad durante los procedimientos de colecistectomía laparoscópica utilizando el conjunto de datos Cholec80-CVS, y proporciona información sobre las limitaciones y los enfoques potenciales para futuras investigaciones en esta área |
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2023-07-07T19:36:31Z |
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2023-07-07T19:36:31Z |
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2023-06-27 |
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Törnqvist Björn, Strömberg Cecilia, Persson Gunnar, and Nilsson Magnus. Effect of intended intraoperative cholangiography and early detection of bile duct injury on survival after cholecystectomy: Population based cohort study. BMJ (Clinical research ed.), 345:e6457, 10 2012. Pucher PH, Brunt LM, Davies N, Linsk A, Munshi A, Rodriguez HA, Fingerhut A, Fanelli RD, Asbun H, and Aggarwal R. Outcome trends and safety measures after 30 years of laparoscopic cholecystectomy: a systematic review and pooled data analysis. Surgical Endoscopy, 32:2175-2183, 5 2018. Alkhaffaf Bilal and Bart Decadt. 15 years of litigation following laparoscopic cholecystectomy in england. Annals of surgery, 251:682-685, 4 2010. Berci G, Hunter J, Morgenstern L, Arregui M, Brunt M, Carroll B, Edye M, Fermelia D, Ferzli G, Greene F, Petelin J, Phillips E, Ponsky J, Sax H, Schwaitzberg S, Soper N, Swanstrom L, , and Traverso W. Laparoscopic cholecystectomy: first, do no harm; second, take care of bile duct stones. Surgical endoscopy, 27:1051-1054, 4 2013. Dominic Sanford and Steven Strasberg. A simple effective method for generation of a permanent record of the critical view of safety during laparoscopic cholecystectomy by intraoperative "doublet" photography. Journal of the American College of Surgeons, 218:170-8, 02 2014. Cristina González, Laura Bravo-Sánchez, and Pablo Arbelaez. Isinet: An instance-based approach for surgical instrument segmentation. arXiv preprint arXiv:2007.05533, 2020. Max Allan, Alex Shvets, Thomas Kurmann, Zichen Zhang, Rahul Duggal, Yun-Hsuan Su, Nicola Rieke, Iro Laina, Niveditha Kalavakonda, Sebastian Bodenstedt, Luis Herrera, Wenqi Li, Vladimir Iglovikov, Huoling Luo, Jian Yang, Danail Stoyanov, Lena Maier-Hein, Stefanie Speidel, and Mahdi Azizian. 2017 robotic instrument segmentation challenge, 2019. Chinedu Innocent Nwoye, Didier Mutter, Jacques Marescaux, and Nicolas Padoy. Weakly supervised convolutional lstm approach for tool tracking in laparoscopic videos, 2019. Daniel A Hashimoto, Guy Rosman, Elan R Witkowski, Caitlin Stafford, Allison J Navarette-Welton, David W Rattner, Keith D Lillemoe, Daniela L Rus, and Ozanan R Meireles. Computer vision analysis of intraoperative video: Automated recognition of operative steps in laparoscopic sleeve gastrectomy. Ann Surg, 2019. P. Mascagni, A. Vardazaryan, D. Alapatt, T. Urade, T. Emre, C. Fiorillo, P. Pessaux, D. Mutter, J. Marescaux, G. Costamagna, B. Dallemagne, and N. Padoy. Artificial Intelligence for Surgical Safety: Automatic Assessment of the Critical View of Safety in Laparoscopic Cholecystectomy Using Deep Learning. Ann Surg, Nov 2020. Manuel Sebastián Ríos, María Alejandra Molina, Daniella Londoño, Camilo Andrés Guillén, Sebastian Siera, Felipe Zapata, and Luis Felipe Giraldo. Cholec80-cvs: An open dataset with an evaluation of strasberg's critical view of safety for ai. Scientific Data, 194, 4 2023. Yegiyants Sara and J Craig Collins. Operative strategy can reduce the incidence of major bile duct injury in laparoscopic cholecystectomy. The American surgeon, 74:985-987, 10 2008. Avgerinos C, Kelgiorgi D, Touloumis Z, Baltatzi L, and Dervenis C. One thousand laparoscopic cholecystectomies in a single surgical unit using the çritical view of safety"technique. Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract, 13:498-503, 3 2009. Weixiang Chen, Jianjiang Feng, Jiwen Lu, and Jie Zhou. Endo3d: Online workflow analysis for endoscopic surgeries based on 3d cnn and lstm. In Danail Stoyanov, Zeike Taylor, Duygu Sarikaya, Jonathan McLeod, Miguel Angel González Ballester, Noel C.F. Codella, Anne Martel, Lena Maier-Hein, Anand Malpani, Marco A. Zenati, Sandrine De Ribaupierre, Luo Xiongbiao, Toby Collins, Tobias Reichl, Klaus Drechsler, Marius Erdt, Marius George Linguraru, Cristina Oyarzun Laura, Raj Shekhar, Stefan Wesarg, M. Emre Celebi, Kristin Dana, and Allan Halpern, editors, OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis, pages 97-107, Cham, 2018. Springer International Publishing. W.-Y. Hong, C.-L. Kao, Y.-H. Kuo, J.-R. Wang, W.-L. Chang, and C.-S. Shih. Cholecseg8k: A semantic segmentation dataset for laparoscopic cholecystectomy based on cholec80. CoRR, abs/2012.12453, 2020. Pan Shi, Zijian Zhao, Sanyuan Hu, and Faliang Chang. Real-time surgical tool detection in minimally invasive surgery based on attention-guided convolutional neural network. IEEE Access, 8:228853-228862, 2020. Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv 1409.1556, 09 2014. Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations, 2021. Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, 2016. http://www. deeplearningbook.org. Niemann AC, Matusko N, Sandhu G, and Varban OA. Assessing the effect of the critical view of safety criteria on simulated operative decision-making: a pilot study. Surgical endoscopy, 33:911-916, 2019. |
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Atribución 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Giraldo Trujillo, Luis Felipevirtual::1698-1Mugnier Zuluaga, Andrés223726e7-cff5-4cbd-8a0d-09b253a311e6600Segura Quijano, Fredy Enrique2023-07-07T19:36:31Z2023-07-07T19:36:31Z2023-06-27http://hdl.handle.net/1992/68213instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/La colecistectomía laparoscópica es un procedimiento quirúrgico mínimamente invasivo utilizado para la extirpación de la vesícula biliar que puede provocar lesiones en el conducto biliar. Para prevenir estas lesiones, Strasberg y sus colegas propusieron el método Critical View of Safety para identificar el conducto cístico y la arteria durante estos procedimientos. En este trabajo entrenamos modelos de aprendizaje profundo con el primer conjunto de datos de código abierto de vídeos de colistectomía laparoscópica que contienen anotaciones de los criterios de Strasberg, llamado Cholec80-CVS. Este estudio representa el primer intento de investigar el desempeño de los modelos de aprendizaje profundo para ayudar a identificar la vista crítica de seguridad durante los procedimientos de colecistectomía laparoscópica utilizando el conjunto de datos Cholec80-CVS, y proporciona información sobre las limitaciones y los enfoques potenciales para futuras investigaciones en esta áreaIngeniero ElectrónicoPregradoDeep learningMachine learningInteligencia artificialComputer vision15 páginasapplication/pdfspaUniversidad de los AndesIngeniería ElectrónicaFacultad de IngenieríaDepartamento de Ingeniería Eléctrica y ElectrónicaEvaluación automática de la vista crítica de seguridad utilizando deep learning en la base de datos Cholec80-CVSTrabajo de grado - Pregradoinfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_7a1fTexthttp://purl.org/redcol/resource_type/TPDeep learningColecistectomía laparoscópicaMachine learningInteligencia artificialComputer visionVision transformerIngenieríaTörnqvist Björn, Strömberg Cecilia, Persson Gunnar, and Nilsson Magnus. Effect of intended intraoperative cholangiography and early detection of bile duct injury on survival after cholecystectomy: Population based cohort study. BMJ (Clinical research ed.), 345:e6457, 10 2012.Pucher PH, Brunt LM, Davies N, Linsk A, Munshi A, Rodriguez HA, Fingerhut A, Fanelli RD, Asbun H, and Aggarwal R. Outcome trends and safety measures after 30 years of laparoscopic cholecystectomy: a systematic review and pooled data analysis. Surgical Endoscopy, 32:2175-2183, 5 2018.Alkhaffaf Bilal and Bart Decadt. 15 years of litigation following laparoscopic cholecystectomy in england. Annals of surgery, 251:682-685, 4 2010.Berci G, Hunter J, Morgenstern L, Arregui M, Brunt M, Carroll B, Edye M, Fermelia D, Ferzli G, Greene F, Petelin J, Phillips E, Ponsky J, Sax H, Schwaitzberg S, Soper N, Swanstrom L, , and Traverso W. Laparoscopic cholecystectomy: first, do no harm; second, take care of bile duct stones. Surgical endoscopy, 27:1051-1054, 4 2013.Dominic Sanford and Steven Strasberg. A simple effective method for generation of a permanent record of the critical view of safety during laparoscopic cholecystectomy by intraoperative "doublet" photography. Journal of the American College of Surgeons, 218:170-8, 02 2014.Cristina González, Laura Bravo-Sánchez, and Pablo Arbelaez. Isinet: An instance-based approach for surgical instrument segmentation. arXiv preprint arXiv:2007.05533, 2020.Max Allan, Alex Shvets, Thomas Kurmann, Zichen Zhang, Rahul Duggal, Yun-Hsuan Su, Nicola Rieke, Iro Laina, Niveditha Kalavakonda, Sebastian Bodenstedt, Luis Herrera, Wenqi Li, Vladimir Iglovikov, Huoling Luo, Jian Yang, Danail Stoyanov, Lena Maier-Hein, Stefanie Speidel, and Mahdi Azizian. 2017 robotic instrument segmentation challenge, 2019.Chinedu Innocent Nwoye, Didier Mutter, Jacques Marescaux, and Nicolas Padoy. Weakly supervised convolutional lstm approach for tool tracking in laparoscopic videos, 2019.Daniel A Hashimoto, Guy Rosman, Elan R Witkowski, Caitlin Stafford, Allison J Navarette-Welton, David W Rattner, Keith D Lillemoe, Daniela L Rus, and Ozanan R Meireles. Computer vision analysis of intraoperative video: Automated recognition of operative steps in laparoscopic sleeve gastrectomy. Ann Surg, 2019.P. Mascagni, A. Vardazaryan, D. Alapatt, T. Urade, T. Emre, C. Fiorillo, P. Pessaux, D. Mutter, J. Marescaux, G. Costamagna, B. Dallemagne, and N. Padoy. Artificial Intelligence for Surgical Safety: Automatic Assessment of the Critical View of Safety in Laparoscopic Cholecystectomy Using Deep Learning. Ann Surg, Nov 2020.Manuel Sebastián Ríos, María Alejandra Molina, Daniella Londoño, Camilo Andrés Guillén, Sebastian Siera, Felipe Zapata, and Luis Felipe Giraldo. Cholec80-cvs: An open dataset with an evaluation of strasberg's critical view of safety for ai. Scientific Data, 194, 4 2023.Yegiyants Sara and J Craig Collins. Operative strategy can reduce the incidence of major bile duct injury in laparoscopic cholecystectomy. The American surgeon, 74:985-987, 10 2008.Avgerinos C, Kelgiorgi D, Touloumis Z, Baltatzi L, and Dervenis C. One thousand laparoscopic cholecystectomies in a single surgical unit using the çritical view of safety"technique. Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract, 13:498-503, 3 2009.Weixiang Chen, Jianjiang Feng, Jiwen Lu, and Jie Zhou. Endo3d: Online workflow analysis for endoscopic surgeries based on 3d cnn and lstm. In Danail Stoyanov, Zeike Taylor, Duygu Sarikaya, Jonathan McLeod, Miguel Angel González Ballester, Noel C.F. Codella, Anne Martel, Lena Maier-Hein, Anand Malpani, Marco A. Zenati, Sandrine De Ribaupierre, Luo Xiongbiao, Toby Collins, Tobias Reichl, Klaus Drechsler, Marius Erdt, Marius George Linguraru, Cristina Oyarzun Laura, Raj Shekhar, Stefan Wesarg, M. Emre Celebi, Kristin Dana, and Allan Halpern, editors, OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis, pages 97-107, Cham, 2018. Springer International Publishing.W.-Y. Hong, C.-L. Kao, Y.-H. Kuo, J.-R. Wang, W.-L. Chang, and C.-S. Shih. Cholecseg8k: A semantic segmentation dataset for laparoscopic cholecystectomy based on cholec80. CoRR, abs/2012.12453, 2020.Pan Shi, Zijian Zhao, Sanyuan Hu, and Faliang Chang. Real-time surgical tool detection in minimally invasive surgery based on attention-guided convolutional neural network. IEEE Access, 8:228853-228862, 2020.Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv 1409.1556, 09 2014.Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations, 2021.Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, 2016. http://www. deeplearningbook.org.Niemann AC, Matusko N, Sandhu G, and Varban OA. Assessing the effect of the critical view of safety criteria on simulated operative decision-making: a pilot study. 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