Abstract: Systems and methods for implementing an adaptive therapy workflow that minimizes time needed to create a session patient model, select an appropriate plan for the treatment session, and treat the patient.

Abstract: Systems and method for automatically generating structures, such as target volumes, in a treatment image using structure-guided deformation to propagate the structures from a planning image onto the subsequently acquired treatment image.

Abstract: To overcome the difficulties inherent in conventional treatment planning approaches, new techniques are described herein for providing an intuitive user interface for automatic structure derivation in patient modeling. In an embodiment, a graphical user interface is provided that provides a list of structures of a specified region. The interface uses medical terminology instead of mathematical one. In one or more embodiments, the list of structures may be a pre-defined list of structures that correspond to that region for the purposes of treatment planning. A user is able to actuate a toggle to include and/or exclude each of the structures separately. In one or more embodiments, the user is also able to actuate a toggle to define a perimeter around each included structure, and further define a margin around the perimeter. The user is also able to specify whether the desired output should include a union or the intersection of all included structures.

Abstract: An apparatus and method for determining an image similarity based on image features. In one aspect, the image similarity determination is based on an image comparison tool. The image comparison tool may be trained, by a machine-learning system, to estimate a similarity between images based on a subset of image data comprised by image features. The estimate may be an estimate of how similar structures found in the images would be following a geometric transformation of some of the structures. In one aspect, an atlas image for performing automatic segmentation of an image is determined according to a comparison made using the image comparison tool.

Abstract: A shape similarity metric can be provided that indicates how similar two or more shapes are. A difference between a union of the shapes and an intersection of the shapes can be used to determine the similarity metric. The shape similarity metric can provide an average distance between the shapes. Different processes for determining shapes can be evaluated for accuracy based on the shape similarity metric. New or alternative shape-determining processes can be compared for accuracy against other shape-determining processes including reference shape-determining processes. Shape similarity metrics can be determined for two-dimensional shapes and three-dimensional shapes.

Abstract: An apparatus and method for determining an image similarity based on image features. In one aspect, the image similarity determination is based on an image comparison tool. The image comparison tool may be trained, by a machine-learning system, to estimate a similarity between images based on a subset of image data comprised by image features. The estimate may be an estimate of how similar structures found in the images would be following a geometric transformation of some of the structures. In one aspect, an atlas image for performing automatic segmentation of an image is determined according to a comparison made using the image comparison tool.

Abstract: To overcome the difficulties inherent in conventional treatment planning approaches, new techniques are described herein for providing an intuitive user interface for automatic structure derivation in patient modeling. In an embodiment, a graphical user interface is provided that provides a list of structures of a specified region. The interface uses medical terminology instead of mathematical one. In one or more embodiments, the list of structures may be a pre-defined list of structures that correspond to that region for the purposes of treatment planning. A user is able to actuate a toggle to include and/or exclude each of the structures separately. In one or more embodiments, the user is also able to actuate a toggle to define a perimeter around each included structure, and further define a margin around the perimeter. The user is also able to specify whether the desired output should include a union or the intersection of all included structures.

Abstract: An apparatus and method for determining an image similarity based on image features. In one aspect, the image similarity determination is based on an image comparison tool. The image comparison tool may be trained, by a machine-learning system, to estimate a similarity between images based on a subset of image data comprised by image features. The estimate may be an estimate of how similar structures found in the images would be following a geometric transformation of some of the structures. In one aspect, an atlas image for performing automatic segmentation of an image is determined according to a comparison made using the image comparison tool.

Abstract: A shape similarity metric can be provided that indicates how similar two or more shapes are. A difference between a union of the shapes and an intersection of the shapes can be used to determine the similarity metric. The shape similarity metric can provide an average distance between the shapes. Different processes for determining shapes can be evaluated for accuracy based on the shape similarity metric. New or alternative shape-determining processes can be compared for accuracy against other shape-determining processes including reference shape-determining processes. Shape similarity metrics can be determined for two-dimensional shapes and three-dimensional shapes.

Abstract: A shape similarity metric can be provided that indicates how similar two or more shapes are. A difference between a union of the shapes and an intersection of the shapes can be used to determine the similarity metric. The shape similarity metric can provide an average distance between the shapes. Different processes for determining shapes can be evaluated for accuracy based on the shape similarity metric. New or alternative shape-determining processes can be compared for accuracy against other shape-determining processes including reference shape-determining processes. Shape similarity metrics can be determined for two-dimensional shapes and three-dimensional shapes.

Abstract: An apparatus and method for determining an image similarity based on image features. In one aspect, the image similarity determination is based on an image comparison tool. The image comparison tool may be trained, by a machine-learning system, to estimate a similarity between images based on a subset of image data comprised by image features. The estimate may be an estimate of how similar structures found in the images would be following a geometric transformation of some of the structures. In one aspect, an atlas image for performing automatic segmentation of an image is determined according to a comparison made using the image comparison tool.

Abstract: A method involving an image of an anatomy, includes: obtaining an image of an anatomy; obtaining a program instruction from a user for creating an object in the image; and executing the program instruction to create the object in the image, wherein the act of executing the program instruction is performed using a processor. A computer product having a non-transitory medium storing a set of instructions, an execution of which causes a method to be performed, the method includes: obtaining an image of an anatomy; obtaining a program instruction from a user for creating an object in the image; and executing the program instruction to create the object in the image.

Abstract: A shape similarity metric can be provided that indicates how similar two or more shapes are. A difference between a union of the shapes and an intersection of the shapes can be used to determine the similarity metric. The shape similarity metric can provide an average distance between the shapes. Different processes for determining shapes can be evaluated for accuracy based on the shape similarity metric. New or alternative shape-determining processes can be compared for accuracy against other shape-determining processes including reference shape-determining processes. Shape similarity metrics can be determined for two-dimensional shapes and three-dimensional shapes.

Abstract: Embodiments of the present invention are directed to techniques for providing an environment for the efficient execution of recognition tasks. A novel environment is provided which automatically and efficiently executes a recognition program on as many computer processors as available. This program, deconstructed into separate tasks, may be executed by constructing a dependency network from known inputs and outputs of the tasks, applying project planning methods for scheduling these tasks into multiple processing threads, and dynamically assigning tasks within these threads to processors. Therefore, an efficient schedule of tasks to complete a recognition program can be created and executed automatically, for any type of recognition problem.

Abstract: A method involving an image of an anatomy, includes: obtaining an image of an anatomy; obtaining a program instruction from a user for creating an object in the image; and executing the program instruction to create the object in the image, wherein the act of executing the program instruction is performed using a processor. A computer product having a non-transitory medium storing a set of instructions, an execution of which causes a method to be performed, the method includes: obtaining an image of an anatomy; obtaining a program instruction from a user for creating an object in the image; and executing the program instruction to create the object in the image.

Abstract: A medical-imaging digital-computing platform serves to access a plurality of data objects and to execute an iterative process with respect to these data objects. The data objects themselves each at least generally pertain to portions of the human anatomy and may comprise, for example, both a source data object and a target data object. The platform executes the iterative process to determine at least one of a labeling of a portion of one of the data objects and a geometric relationship between at least portions of at least two of the data objects. This can be done, for example, by automatically employing both a segmentation module and a registration module as steps within the iterative process. This can also comprise determining when to automatically generate an intermediate data object to provide as input to at least one of these modules.

Abstract: A medical-imaging digital-computing platform serves to access a plurality of data objects and to execute an iterative process with respect to these data objects. The data objects themselves each at least generally pertain to portions of the human anatomy and may comprise, for example, both a source data object and a target data object. The platform executes the iterative process to determine at least one of a labeling of a portion of one of the data objects and a geometric relationship between at least portions of at least two of the data objects. This can be done, for example, by automatically employing both a segmentation module and a registration module as steps within the iterative process. This can also comprise determining when to automatically generate an intermediate data object to provide as input to at least one of these modules.

Abstract: Embodiments of the present invention are directed to techniques for providing an environment for the efficient execution of recognition tasks. A novel environment is provided which automatically and efficiently executes a recognition program on as many computer processors as available. This program, deconstructed into separate tasks, may be executed by constructing a dependency network from known inputs and outputs of the tasks, applying project planning methods for scheduling these tasks into multiple processing threads, and dynamically assigning tasks within these threads to processors. Therefore, an efficient schedule of tasks to complete a recognition program can be created and executed automatically, for any type of recognition problem.

Abstract: A method of constructing a navigation table relating a set of images representative of a region of interest in a subject to a reference system with reference positions indicating known anatomic landmarks of a reference subject comprising providing reference positions for two or more images identified with two or more anatomic landmarks indicative of the region of interest with reference positions of known anatomic landmarks corresponding to the identified anatomic landmarks, and determining reference positions for the remaining images by interpolation.

Abstract: A method of constructing a navigation table relating a set of images representative of a region of interest in a subject to a reference system with reference positions indicating known anatomic landmarks of a reference subject comprising providing reference positions for two or more images identified with two or more anatomic landmarks indicative of the region of interest with reference positions of known anatomic landmarks corresponding to the identified anatomic landmarks, and determining reference positions for the remaining images by interpolation.