Abstract: Embodiments include exemplary systems. methods. and computer-accessible medium for analysis of anatomical images and identification of anatomical components and/or structures. In some embodiments. systems. devices. and methods described herein relate to identification of levels of a spine and other anatomical components associated with those levels.

Abstract: Systems, devices, and methods for segmentation of patient anatomy are described herein. A method can include receiving a three-dimensional (3D) scan volume including a set of images of a 3D region of patient anatomy. The 3D region of patient anatomy can include a set of anatomical structures. The method can also include generating a set of two-dimensional (2D) radiographs using the 3D scan volume. Each 2D radiograph from the set of 2D radiographs can include 3D image data extracted from the 3D scan volume. The method can also include training a segmentation model to segment 2D radiographs using the set of 2D radiographs to identify one or more anatomical parts of interest.

Abstract: A surgical navigation system can include a three-dimensional (3D) display system with a see-through screen; a tracking system configured to track the position and orientation of a surgeon's head, the see-through screen, a patient anatomy and a medical device to provide current position and orientation data; data associated with an operative plan, patient anatomy, and/or the medical device; a surgical navigation image generator configured to generate a surgical navigation image with a three-dimensional image including a first virtual image indicating the planned position and orientation of the medical device according to the operative plan data and a second virtual image indicating a current position and orientation of the medical device.

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 surgical navigation system includes a source of a patient anatomy data, wherein the patient anatomy data comprises a three-dimensional reconstruction of a segmented model comprising at least two sections representing parts of the anatomy. A surgical navigation image generator is configured to generate a surgical navigation image comprising the patient anatomy. A 3D display system is configured to show the surgical navigation image wherein the display of the patient anatomy is selectively configurable such that at least one section of the anatomy is di splayed and at least one other section of the anatomy is not displayed.

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 surgical navigation system includes a source of a patient anatomy data, wherein the patient anatomy data comprises a three-dimensional reconstruction of a segmented model comprising at least two sections representing parts of the anatomy. A surgical navigation image generator is configured to generate a surgical navigation image comprising the patient anatomy. A 3D display system is configured to show the surgical navigation image wherein the display of the patient anatomy is selectively configurable such that at least one section of the anatomy is displayed and at least one other section of the anatomy is not displayed.

Abstract: A surgical navigation system includes: a tracker (125) for real-time tracking of a position and orientation of a robot arm (191); a source of a patient anatomical data (163) and a robot arm virtual image (166); a surgical navigation image generator (131) generating a surgical navigation image (142A) including the patient anatomy (163) and the robot arm virtual image (166) in accordance to the current position and/or orientation data provided by the tracker (125); a 3D display system (140) showing the surgical navigation image (142A).

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 surgical navigation system includes: a 3D display system with a see-through visor; a tracking system comprising means for real-time tracking of: a surgeon's head, the see-through visor, a patient anatomy and a surgical instrument to provide current position and orientation data; a source of an operative plan, a patient anatomy data and a virtual surgical instrument model; a surgical navigation image generator configured to generate a surgical navigation image with a three-dimensional image representing simultaneously a virtual image of the surgical instrument corresponding to the current position and orientation of the surgical instrument and a virtual image of the surgical instrument indicating the suggested positions and orientation of the surgical instrument according to the operative plan data based on the current relative position and orientation of the surgeon's head, the see-through visor, the patient anatomy and the surgical instrument; wherein the 3D display system is configured to show the surgica

Abstract: An image-guided surgical system includes a processor, a display communicatively coupled to the processor, and an imaging system communicatively coupled to the processor. A memory device, communicatively coupled to the processor, stores instructions, executable by the processor, to cause the processor to receive, from the imaging system, real-time image data of an ophthalmological surgical field during an ophthalmological surgical procedure, and analyze the image data in real-time to identify an ocular tissue boundary present in the image data of the ophthalmological surgical field. The instructions cause the processor to provide real-time visual, auditory, and/or haptic feedback in response to the identified ocular tissue boundary.

Abstract: A surgical navigation system includes: a tracker (125) for real-time tracking of a position and orientation of a robot arm (191); a source of a patient anatomical data (163) and a robot arm virtual image (166); a surgical navigation image generator (131) generating a surgical navigation image (142A) including the patient anatomy (163) and the robot arm virtual image (166) in accordance to the current position and/or orientation data provided by the tracker (125); a 3D display system (140) showing the surgical navigation image (142A).

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: at least one processor communicably coupled to at least one nontransitory processor-readable storage medium storing processor-executable instructions or data receives segmentation learning data comprising a plurality of batches of labeled anatomical image sets, each image set comprising image data representative of a series of slices of a three-dimensional bony structure, and each image set including at least one label which identifies the region of a particular part of the bony structure depicted in each image of the image set, wherein the label indicates one of a plurality of classes indicating parts of the bone anatomy; trains a segmentation CNN, that is a fully convolutional neural network model with layer skip connections, to segment semantically at least one part of the bony structure utilizing the received segmentation learning data; and stores the trained segmentation CNN in at least one nontransitory processor-readable storage medium of the machine learning system.

Abstract: A surgical navigation system includes: a 3D display system with a see-through visor; a tracking system comprising means for real-time tracking of: a surgeon's head, the see-through visor, a patient anatomy and a surgical instrument to provide current position and orientation data; a source of an operative plan, a patient anatomy data and a virtual surgical instrument model; a surgical navigation image generator configured to generate a surgical navigation image with a three-dimensional image representing simultaneously a virtual image of the surgical instrument corresponding to the current position and orientation of the surgical instrument and a virtual image of the surgical instrument indicating the suggested positions and orientation of the surgical instrument according to the operative plan data based on the current relative position and orientation of the surgeon's head, the see-through visor, the patient anatomy and the surgical instrument; wherein the 3D display system is configured to show the surgica

Abstract: A computer-implemented system: at least one processor communicably coupled to at least one nontransitory processor-readable storage medium storing processor-executable instructions or data receives segmentation learning data comprising a plurality of batches of labeled anatomical image sets, each image set comprising image data representative of a series of slices of a three-dimensional bony structure, and each image set including at least one label which identifies the region of a particular part of the bony structure depicted in each image of the image set, wherein the label indicates one of a plurality of classes indicating parts of the bone anatomy; trains a segmentation CNN, that is a fully convolutional neural network model with layer skip connections, to segment semantically at least one part of the bony structure utilizing the received segmentation learning data; and stores the trained segmentation CNN in at least one nontransitory processor-readable storage medium of the machine learning system.

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