Hluboké neuronové sítě pro analýzu medicínských dat

The goal of this bachelor's thesis is to use the 2D convolutional neural network on the 3D model dataset by multi-view methods. The view is 2D picture of 3D model. The result are Pyqt applications, where is possible to load the 3D model of teeth and predict the location of landmarks and teeth by object segmentation and object detection. During this thesis, an annotation's script was created for the annotation of 3D models for landmarks of teeth and teeth themself. This thesis solves the problem of the small availability of annotated 3D datasets in the medical industry by automating generating binary masks from different views on 3D models.

Medical data, Teeth segmentation, Teeth detection, Landmark, Orthodontics , Multi-view method, 2D convolutional neural network, 3D object classification, U-Net, Yolo v3, MaskRCNN, Blender, STL model, Anotation script.