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Semantic segmentation of minimally invasive anti-reflux surgery video using U-NET Machine Learning

Abbing, J.R. (2020) Semantic segmentation of minimally invasive anti-reflux surgery video using U-NET Machine Learning.

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Abstract:Introduction: During anti-reflux surgery, there is a potential risk of Nervus Vagus (NV) injury. A solution is to create an AI tool that can detect the NV. and other anatomical structures in surgical videos. Addition of temporal features that might help in segmentation/detecting the actual nerve. Method: Five U-NET algorithms (different data inputs) are used as a for the training of five visible structures in the videoframes. These AI algorithms are trained a small clinical dataset for the actual goal and a larger automotive dataset for testing the functionality of the networks and testing the addition of Dense Optical Flow (temporal features). Results: The U-NETS can segment in all cases at least two structures with an IOU >0.5. Even three structures for the RGB only algorithm. No IOU above the 0.05 for the NV. Adding dense Optical Flow lowers in all cases the IOU. Although Dense Optical Flow only is still able to give spatial information. The confusion matrix shows similar findings. Conclusion: Training U-NETS and visualisation of the segmentation was possible on top of surgical video frames. Detecting the NV was not possible. The addition of temporal features by combining the RGB data does not improve the semantic segmentation.
Item Type:Essay (Master)
Faculty:TNW: Science and Technology
Subject:44 medicine, 50 technical science in general, 54 computer science
Programme:Technical Medicine MSc (60033)
Link to this item:http://purl.utwente.nl/essays/81337
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