{"id":1833,"date":"2020-01-20T15:57:58","date_gmt":"2020-01-20T14:57:58","guid":{"rendered":"https:\/\/test.re-mic.de\/projects\/szenensegmentierung-chirurgieroboter\/"},"modified":"2022-10-26T13:03:09","modified_gmt":"2022-10-26T11:03:09","slug":"deepmic","status":"publish","type":"page","link":"https:\/\/re-mic.de\/en\/projects\/deepmic\/","title":{"rendered":"DeepMIC – Deep learning based endoscope tracking"},"content":{"rendered":"
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\n\t\t\t\t\t\tDeepMIC - Deep learning based endoscope tracking\t\t\t\t\t\t<\/h3>\n\t\t\t\t\t\t<\/div>\n<\/div><\/div><\/div><\/div>
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Project description<\/h3>\n

The DeepMIC project is creating a new approach for an intelligent, collaborative assistance system for camera guidance during minimally invasive surgical procedures. The new assistance system is to be characterized by an adaptivity in use that has not yet been achieved to any degree, intuitive operability and the ability to cooperate actively (semi-) automatically with the surgeon and thus be capable of the best possible camera guidance virtually on its own.<\/p>\n

The innovative approach consists of a continuous evaluation and classification of the information of the endoscopic camera image by methods of artificial intelligence (here especially Deep Learning) in combination with natural speech recognition. Combined with knowledge from the surgical workflow, the system should allow interaction with the surgeon that is similar to a human assistant and can thus react directly to the current requirements of the procedure.<\/p>\n<\/div>\n<\/div><\/div><\/div><\/div>

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\"\"<\/a>Funding<\/h3>\n

Grant from funds of the Bavarian Research Foundation,
\nFunding code: AZ-1506-21<\/p>\n

Period and volume<\/h3>\n

August 2021 to August 2024<\/p>\n

Overall project: approx. 534 T\u20ac, sub-project of the OTH Regensburg: approx.\u00a0 200 T\u20ac<\/p>\n

Cooperation partners<\/h3>\n