Abstract: A computer-implemented system with at least one processor that reads a set of 2D slices of an intraoperative 3D volume, each of the 2D slices comprising an image of an anatomical structure and of a registration grid containing an array of markers; detects the markers of the registration grid on each of the 2D slices by using a marker detection convolutional neural network (CNN); filters the marker detection results for the 2D slices to remove false positives by processing the whole set of the 2D slices of the intraoperative 3D volume; and determines the 3D location and 3D orientation of the registration grid with respect to the intraoperative 3D volume, by finding a homogeneous transformation between the filtered marker detection results for the intraoperative 3D volume and a reference 3D volume of the registration grid.

Abstract: A method for autonomous multidimensional segmentation of anatomical structures from 3D scan volumes including receiving the 3D scan volume including a set of medical scan images comprising the anatomical structures; automatically defining succeeding multidimensional regions of input data used for further processing; autonomously processing), by means of a pre-trained segmentation convolutional neural network, the defined multidimensional regions to determine weak segmentation results that define a probable 3D shape, location, and size of the anatomical structures; automatically combining multiple weak segmentation results by determining segmented voxels that overlap on the weak segmentation results, to obtain raw strong segmentation results with improved accuracy of the segmentation; autonomously filtering the raw strong segmentation results with a predefined set of filters and parameters for enhancing shape, location, size and continuity of the anatomical structures to obtain filtered strong segmentation res

Abstract: A computer-implemented method for fully-autonomous level identification of anatomical structures within a three-dimensional medical imagery, includes: receiving a set of medical scan images of the anatomical structures; processing the set to perform an autonomous semantic segmentation of anatomical components and to store segmentation results; processing segmentation results by removing the false positives, and smoothing 3D surfaces of the generated anatomical components; determining morphological and spatial relationships of the anatomical components; grouping the anatomical components to form separate levels based on the morphological and spatial relationships of the anatomical components; processing the set using a convolutional neural network to autonomously assign an initial level type; assigning the determined level type to each group of anatomical components by combining the determined morphological and spatial relationships with the determined initial level type; assigning an ordinal identifier to eac

Abstract: A method for computer assisted identification of appropriate anatomical structure for placement of a medical device, comprising: receiving a 3D scan volume comprising set of medical scan images of a region of an anatomical structure where the medical device is to be placed; automatically processing the set of medical scan images to perform automatic segmentation of the anatomical structure; automatically determining a subsection of the 3D scan volume as a 3D ROI by combining the raw medical scan images and the obtained segmentation data; automatically processing the ROI to determine the preferred 3D position and orientation of the medical device to be placed with respect to the anatomical structure by identifying landmarks within the anatomical structure with a pre-trained prediction neural network; automatically determining the preferred 3D position and orientation of the medical device to be placed with respect to the 3D scan volume of the anatomical structure.

Abstract: A method for autonomous segmentation of three-dimensional nervous system structures from raw medical images, the method including: receiving a 3D scan volume with a set of medical scan images of a region of the anatomy; autonomously processing the set of medical scan images to perform segmentation of a bony structure of the anatomy to obtain bony structure segmentation data; autonomously processing a subsection of the 3D scan volume as a 3D region of interest by combining the raw medical scan images and the bony structure segmentation data, wherein the 3D ROI contains a subvolume of the bony structure with a portion of surrounding tissues, including the nervous system structure; autonomously processing the ROI to determine the 3D shape, location, and size of the nervous system structures by means of a pre-trained convolutional neural network (CNN).

Abstract: A method for computer assisted identification of appropriate anatomical structure for placement of a medical device, comprising: receiving a 3D scan volume comprising set of medical scan images of a region of an anatomical structure where the medical device is to be placed; automatically processing the set of medical scan images to perform automatic segmentation of the anatomical structure; automatically determining a subsection of the 3D scan volume as a 3D ROI by combining the raw medical scan images and the obtained segmentation data; automatically processing the ROI to determine the preferred 3D position and orientation of the medical device to be placed with respect to the anatomical structure by identifying landmarks within the anatomical structure with a pre-trained prediction neural network; automatically determining the preferred 3D position and orientation of the medical device to be placed with respect to the 3D scan volume of the anatomical structure.

Abstract: A method for autonomous multidimensional segmentation of anatomical structures from 3D scan volumes including receiving the 3D scan volume including a set of medical scan images comprising the anatomical structures; automatically defining succeeding multidimensional regions of input data used for further processing; autonomously processing), by means of a pre-trained segmentation convolutional neural network, the defined multidimensional regions to determine weak segmentation results that define a probable 3D shape, location, and size of the anatomical structures; automatically combining multiple weak segmentation results by determining segmented voxels that overlap on the weak segmentation results, to obtain raw strong segmentation results with improved accuracy of the segmentation; autonomously filtering the raw strong segmentation results with a predefined set of filters and parameters for enhancing shape, location, size and continuity of the anatomical structures to obtain filtered strong segmentation res

Abstract: A computer-implemented method for fully-autonomous level identification of anatomical structures within a three-dimensional medical imagery, includes: receiving a set of medical scan images of the anatomical structures; processing the set to perform an autonomous semantic segmentation of anatomical components and to store segmentation results; processing segmentation results by removing the false positives, and smoothing 3D surfaces of the generated anatomical components; determining morphological and spatial relationships of the anatomical components; grouping the anatomical components to form separate levels based on the morphological and spatial relationships of the anatomical components; processing the set using a convolutional neural network to autonomously assign an initial level type; assigning the determined level type to each group of anatomical components by combining the determined morphological and spatial relationships with the determined initial level type; assigning an ordinal identifier to eac

Abstract: A method for autonomous segmentation of three-dimensional nervous system structures from raw medical images, the method including: receiving a 3D scan volume with a set of medical scan images of a region of the anatomy; autonomously processing the set of medical scan images to perform segmentation of a bony structure of the anatomy to obtain bony structure segmentation data; autonomously processing a subsection of the 3D scan volume as a 3D region of interest by combining the raw medical scan images and the bony structure segmentation data, wherein the 3D ROI contains a subvolume of the bony structure with a portion of surrounding tissues, including the nervous system structure; autonomously processing the ROI to determine the 3D shape, location, and size of the nervous system structures by means of a pre-trained convolutional neural network (CNN).

Abstract: A method for computer assisted identification of appropriate anatomical structure for placement of a medical device, comprising: receiving a 3D scan volume comprising set of medical scan images of a region of an anatomical structure where the medical device is to be placed; automatically processing the set of medical scan images to perform automatic segmentation of the anatomical structure; automatically determining a subsection of the 3D scan volume as a 3D ROI by combining the raw medical scan images and the obtained segmentation data; automatically processing the ROI to determine the preferred 3D position and orientation of the medical device to be placed with respect to the anatomical structure by identifying landmarks within the anatomical structure with a pre-trained prediction neural network; automatically determining the preferred 3D position and orientation of the medical device to be placed with respect to the 3D scan volume of the anatomical structure.

Abstract: A computer-implemented system with at least one processor that reads a set of 2D slices of an intraoperative 3D volume, each of the 2D slices comprising an image of an anatomical structure and of a registration grid containing an array of markers; detects the markers of the registration grid on each of the 2D slices by using a marker detection convolutional neural network (CNN); filters the marker detection results for the 2D slices to remove false positives by processing the whole set of the 2D slices of the intraoperative 3D volume; and determines the 3D location and 3D orientation of the registration grid with respect to the intraoperative 3D volume, by finding a homogeneous transformation between the filtered marker detection results for the intraoperative 3D volume and a reference 3D volume of the registration grid.