Registration of EMT Positions and X-Ray Images in an Aortic Phantom
In this work a real-time object detection model based on the state-of-the-art architecture You Only Look Once (YOLOv3) was trained, in order to detect an Electromagnetic Tracking (EMT) sensor inside an aortic phantom. An EMT system consists of a tracking system, a field generator and an EMT sensor . A problem with EMT is that magnetic fields interfere with ferromagnetic objects in the surrounding area, which corrupts the EMT positional data . The object detection model can be used to detect and recalibrate the EMT sensor, in order to counteract the electromagnetic interference. This can result in a robust model to support surgeons in minimally invasive surgeries, which have advantages over open surgeries like less pain for the patients, faster recovery after operations or better cosmetic results . In this work different datasets containing webcam images were collected, in order to train and test two models. One model was trained on raw webcam images, while the other one was additionally trained on augmented images. Both models were evaluated quantitatively and qualitatively for different hyperparameter configurations. They show good results on the test dataset with achieved mean Average Precision (mAP) scores of 99.89% (model without augmentations) and 99.67% (model with augmentations) and they run very fast with an interference of 26 ms and 35 Frames Per Second (FPS) on a video file. However, the model trained with augmentations generalizes better on images with different backgrounds. Furthermore, a study with some sample x-ray images showed, that the model trained with augmentations is capable of transferring from webcam images to x-ray images. Finally, this work presented a real-time object detection model for detecting an EMT sensor inside an aortic phantom with very precise detections and the capability of transferring to different domains like x-ray images.
Darmstadt, TU, Bachelor Thesis, 2021