Abstract: A surgical navigation method utilizing a computer system. The method includes the steps of: (a) fixing a camera relative to a surgical site on a patient such that the camera does not move relative to the surgical site; (b) restraining the body portion of the patient which includes the surgical site relative to an operating table supporting the patient, such that the body portion does not move relative to the operating table; and (c) detecting with the camera the position of a medical instrument having a tracker array.

Abstract: Embodiments disclosed herein generally relate to connected machine learning models with joint training for lesion detection. Particularly, aspects of the present disclosure are directed to accessing a three-dimensional magnetic resonance imaging (MRI) image, wherein the three-dimensional MRI image depicts a region of a brain of a subject, wherein the region of the brain includes at least a first type of lesions and a second type of lesions; inputting the three-dimensional MRI image into a machine-learning model comprising a first convolutional neural network and a second convolutional neural network; generating a first segmentation mask for the first type of lesions using the first convolutional neural network that takes as input the three-dimensional MRI image; generating a second segmentation mask for the second type of lesions using the second convolutional neural network that takes as input the three-dimensional MRI image; and outputting the first segmentation mask and the second segmentation mask.

Abstract: Embodiments disclosed herein generally relate to multi-arm machine learning models for lesion detection. Particularly, aspects of the present disclosure are directed to accessing a three-dimensional magnetic resonance imaging (MRI) images. Each of the three-dimensional MRI images depict a same volume of a brain of a subject. The volume of the brain includes at least part of one or more lesions. Each three-dimensional MRI image of the three-dimensional MRI images is processed using one or more corresponding encoder arms of a machine-learning model to generate an encoding of the three-dimensional MRI image. The encodings of the three-dimensional MRI images are concatenated to generate a concatenated representation. The concatenated representation is processed using a decoder arm of the machine-learning model to generate a prediction that identifies one or more portions of the volume of the brain predicted to depict at least part of a lesion.