Method for controlling a display of a mixed reality display device, the method comprising at least the following steps: a) providing an image data set comprising a plurality of images of a treatment object, wherein the treatment object is a patient's body or a part thereof and the images depict the treatment object from different perspectives, b) generating a 3D target point cloud from the image data set, wherein the target point cloud comprises a multitude of points defined in a three-dimensional coordinate system and the points represent the treatment object's surface, c) determining a number of semantic segmentation masks (25a, 25b, 25c) in the target point cloud by applying semantic segmentation, d) providing a medical imaging data set that comprises medical imaging data of the treatment object, e) generating a 3D source point cloud from the medical imaging data set, wherein the source point cloud comprises a multitude of points defined in a three-dimensional coordinate system and the points also represent the treatment object's surface, f) determining a number of semantic segmentation masks (29a, 29b, 29c) in the source point cloud by applying semantic segmentation, g) determining a transformation between the source point cloud and the target point cloud using the segmentation masks (29a, 29b, 29c) of the source point cloud and the segmentation masks (25a, 25b, 25c) of the target point cloud, and h) visualizing at least a part of the medical imaging data on the display, wherein the medical imaging data is superimposed on the treatment object and aligned with the treatment object using the transformation between the source point cloud and the target point cloud, wherein step c) comprises ― determining a number of semantic segmentation masks in the images of the image data set by applying semantic segmentation to the images of the image data set, and ― determining the semantic segmentation masks (25a, 25b, 25c) in the target point cloud using the semantic segmentation masks in the images of the image data set. and/or step f) comprises ― determining a number of semantic segmentation masks in the medical imaging data of the medical imaging data set by applying semantic segmentation to the medical imaging data of the medical imaging data set, and ― determining the semantic segmentation masks (29a, 29b, 29c) in the source point cloud using the semantic segmentation masks in the medical imaging data of the medical imaging data set, wherein each semantic segmentation mask refers to a segment of the image that has been determined, using semantic segmentation, as belonging to the same object class. a) providing an image data set comprising a plurality of images of a treatment object, wherein the treatment object is a patient's body or a part thereof and the images depict the treatment object from different perspectives, b) generating a 3D target point cloud from the image data set, wherein the target point cloud comprises a multitude of points defined in a three-dimensional coordinate system and the points represent the treatment object's surface, c) determining a number of semantic segmentation masks (25a, 25b, 25c) in the target point cloud by applying semantic segmentation, d) providing a medical imaging data set that comprises medical imaging data of the treatment object, e) generating a 3D source point cloud from the medical imaging data set, wherein the source point cloud comprises a multitude of points defined in a three-dimensional coordinate system and the points also represent the treatment object's surface, f) determining a number of semantic segmentation masks (29a, 29b, 29c) in the source point cloud by applying semantic segmentation, g) determining a transformation between the source point cloud and the target point cloud using the segmentation masks (29a, 29b, 29c) of the source point cloud and the segmentation masks (25a, 25b, 25c) of the target point cloud, and h) visualizing at least a part of the medical imaging data on the display, wherein the medical imaging data is superimposed on the treatment object and aligned with the treatment object using the transformation between the source point cloud and the target point cloud, ― determining a number of semantic segmentation masks in the images of the image data set by applying semantic segmentation to the images of the image data set, and ― determining the semantic segmentation masks (25a, 25b, 25c) in the target point cloud using the semantic segmentation masks in the images of the image data set. ― determining a number of semantic segmentation masks in the medical imaging data of the medical imaging data set by applying semantic segmentation to the medical imaging data of the medical imaging data set, and ― determining the semantic segmentation masks (29a, 29b, 29c) in the source point cloud using the semantic segmentation masks in the medical imaging data of the medical imaging data set,