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--- activities:theme1:projects:kbmrspm [2015/12/21 16:31]
janin
+++ activities:theme1:projects:kbmrspm [2021/01/29 14:51] (current)
gibaud
@@ Line 4: / Line 4: @@
 ===== Members =====
   * [[members:pierre.jannin:index|Pierre Jannin]]
-  * [[members:olga.dergachyova:index|Olga Dergachyova]] - PhD student Funded by ANR within the Investissement d'Avenir program (Labex CAMI)
+  * [[members:duygu.sarikaya:index|Duygu Sarikaya]] - Post Doc Funded by CONDOR project
   * [[members:xavier.morandi:index|Xavier Morandi]] - Neurosurgeon
   * [[members:pierre-louis.henaux|Pierre-Louis Henaux]] - Neurosurgeon
+  * [[members:olga.dergachyova:index|Olga Dergachyova]] - PhD student Funded by ANR within the Investissement d'Avenir program (Labex CAMI)
@@ Line 14: / Line 16: @@
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+===== Funding =====
+  * [[http://cami-labex.fr/|LabEx CAMI]]
+  * [[https://condor-project.eu/|CONDOR project]]
 ===== Description =====
 \\
-The system for surgical workflow detection designed within the project aims for covering four aspects: 1) knowledge integration, 2) recognition of surgical processes in multiple levels, 3) use of multi-modal input data, 4) real-time performance. Formalized knowledge allows to give a symantic meaning to the input data comming from sensors and better understand the performed actions. This knowledge can be represented in form of Ontology standardizing all the notions of the domain. Also Surgical Process Models can be used to guide the recognition process of surgical procedures. Workflow detection performed in multiple granularity levels is necessary for a wide range of clinical applications from estimation of remaining time to robotic assitance. The top-down approach has been chosen for hierarchical modelling of surgical process. The multi-modal sensors getting a data of different nature (e.g. signal or capturing inverenement) can provide additional information and thus improve the recognition capacity. The input data for this project includes GRB and RGBD videos of the surgical feild and the surgical team in order to analyse the actions of the surgeon and his/her interactions with other team members. The algorithms used in the method allow a real-time computation for on-line use. Additionally, the project comprises new metrics and methods for an informative analysis and rigoureus validation addapted expressly for surgical workflow detection.
+The system for surgical workflow detection designed within the project aims for covering four aspects: 1) knowledge integration, 2) recognition of surgical processes in multiple levels, 3) use of multi-modal input data, 4) real-time performance. Formalized knowledge allows to give a symantic meaning to the input data comming from sensors and better understand the performed actions. This knowledge can be represented in form of Ontology standardizing all the notions of the domain. In the context of the CONDOR project, an ontology was developed (called [[ontocondor|OntoCONDOR]]) to address the needs of the project, extending the OntoSPM ontology. Also Surgical Process Models can be used to guide the recognition process of surgical procedures. Workflow detection performed in multiple granularity levels is necessary for a wide range of clinical applications from estimation of remaining time to robotic assitance. The top-down approach has been chosen for hierarchical modelling of surgical process. The multi-modal sensors getting a data of different nature (e.g. signal or capturing inverenement) can provide additional information and thus improve the recognition capacity. The input data for this project includes GRB and RGBD videos of the surgical field and the surgical team in order to analyse the actions of the surgeon and his/her interactions with other team members. The algorithms used in the method allow a real-time computation for on-line use. Additionally, the project comprises new metrics and methods for an informative analysis and rigoureus validation addapted expressly for surgical workflow detection.
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