Abstract: The present disclosure relates to systems and methods for real and virtual object interactions in augmented reality environments are disclosed. The system comprises areal object detection module to receive multiple image pixels and the corresponding depths of at least one initiative object, a real object recognition module to determine a shape, a position, and a movement of the initiative object; a virtual object display module to display a virtual target object, a collision module to determine whether the at least one initiative object collides into a virtual target object and, an interaction module for determining an action responding to an event based on at least one of an object recognition determination from the real object recognition module, a collision determination from the collision module, and a type of the virtual target object.

Abstract: A system of deep learning neural network in prostate cancer bone metastasis identification based on whole body bone scan images includes a pre-processing module for receiving input whole body bone scan images, and a neural network module for detecting whether there is a prostate cancer bone metastasis. The neural network module includes: a chest portion network module for establishing first stage faster R-CNN and segmenting training images of chest portion according to the input whole body bone scan images, and using the training images to train second stage faster R-CNN and categorizing the lesions of cancerous bone metastasis; and a pelvis portion network module for establishing first stage faster R-CNN and segmenting training images of pelvis portion according to the input whole body bone scan images, and using the training images to train the convolutional neural network to categorize whether it is a bone metastasis image.

Abstract: A system of deep learning neural network in prostate cancer bone metastasis identification based on whole body bone scan images includes a pre-processing module for receiving input whole body bone scan images, and a neural network module for detecting whether there is a prostate cancer bone metastasis. The neural network module includes: a chest portion network module for establishing first stage faster R-CNN and segmenting training images of chest portion according to the input whole body bone scan images, and using the training images to train second stage faster R-CNN and categorizing the lesions of cancerous bone metastasis; and a pelvis portion network module for establishing first stage faster R-CNN and segmenting training images of pelvis portion according to the input whole body bone scan images, and using the training images to train the convolutional neural network to categorize whether it is a bone metastasis image.