Abstract: Some systems include a memory, and a processor coupled to the memory, wherein the processor is configured to: identify one or more spatial markers in a medical data-based image of a patient, identify one or more spatial markers in a real-time perceived image of the patient, wherein the one or more spatial markers in the medical data-based image correspond to an anatomical feature of the patient and the one or more spatial markers in the real-time perceived image correspond to the anatomical feature of the patient, superimpose the medical data-based image of the patient with the real-time perceived image of the patient, and align the one or more spatial markers in the medical data-based image with the respective one or more spatial markers in the real-time perceived image.

Abstract: Systems and methods for visualizing garment fit are provided. In one embodiment, the method can include obtaining garment data descriptive of a garment and body data descriptive of a body. The method can further include simulating a garment deformation of the garment due to contact from the body, and determining a simulating a body deformation of the body due to contact from the garment. The method can further include providing a visualization of the garment on the body for display to a user, the visualization visualizing the garment deformation and the body deformation.

Abstract: A profile of porosities and permeabilities calculated from several sample volumes in a system can speed up computational fluid dynamics (CFDs). Heterogeneous fluid flow paths can be calculation intense, limiting the accuracy of fluid-path models. Further, allowing a user to define a number of sample volumes in a model system allows pre-calculation of porosities and permeabilities for use in Navier-Stokes formulas for modeling fluid flow and gives the user control over calculation time and accuracy. This is helpful, for example, in modeling endovascular interventions where fluid dynamics are determinative in the efficacy or method of treatment for various vascular disorders, such as aneurysms, and heart disease. This is also beneficial in other healthcare contexts, like blood filters, embolic gels, endografts, web devices, and atrial appendage occluders, among others.

Abstract: Systems, methods, and apparatus of providing a visual writing aid are provided. In one example embodiment, a method includes obtaining data descriptive of a first set of information, wherein the first set of information is presented in a first language. The method includes determining a translation of the first set of information to a second language. The method includes presenting a visual representation of the translation of the first set of information in the second language via a display device. The method includes obtaining data descriptive of a second set of information. The second set of information includes a transcription of at least a portion of the first set of information in the second language generated via a mobile writing device. The method includes determining whether the second set of information corresponds to the visual representation of the translation of the first set of information in the second language.

Abstract: Some systems include a memory, and a processor coupled to the memory, wherein the processor is configured to: identify one or more spatial markers in a medical data-based image of a patient, identify one or more spatial markers in a real-time perceived image of the patient, wherein the one or more spatial markers in the medical data-based image correspond to an anatomical feature of the patient and the one or more spatial markers in the real-time perceived image correspond to the anatomical feature of the patient, superimpose the medical data-based image of the patient with the real-time perceived image of the patient, and align the one or more spatial markers in the medical data-based image with the respective one or more spatial markers in the real-time perceived image.

Abstract: Systems and methods for providing instructional guidance relating to an instructive writing instrument are provided. For instance, a first visual contextual signal instructing a user to actuate an instructive writing instrument in a first direction can be provided based at least in part on a model object. The model object can correspond to an object to be rendered on a writing surface by a user using the instructive writing instrument. A first image depicting the writing surface can be obtained. First position data associated with the instructive writing instrument can be determined based at least in part on the first image. A second visual contextual signal instructing the user to actuate the instructive writing instrument in a second direction can be provided based at least in part on the model object and the first position data associated with the instructive writing instrument.

Abstract: Measuring the number of glomeruli in the entire, intact kidney using non-destructive techniques is of immense importance in studying several renal and systemic diseases. In particular, a recent Magnetic Resonance Imaging (MRI) technique, based on injection of a contrast agent, cationic ferritin, has been effective in identifying glomerular regions in the kidney. In various embodiments, a low-complexity, high accuracy method for obtaining the glomerular count from such kidney MRI images is described. This method employs a patch-based approach for identifying a low-dimensional embedding that enables the separation of glomeruli regions from the rest. By using only a few images marked by the expert for learning the model, the method provides an accurate estimate of the glomerular number for any kidney image obtained with the contrast agent. In addition, the implementation of our method shows that this method is near real-time, and can process about 5 images per second.

Abstract: A robust method to automatically segment and identify tumor regions in medical images is extremely valuable for clinical diagnosis and disease modeling. In various embodiments, an efficient algorithm uses sparse models in feature spaces to identify pixels belonging to tumorous regions. By fusing both intensity and spatial location information of the pixels, this technique can automatically localize tumor regions without user intervention. Using a few expert-segmented training images, a sparse coding-based classifier is learned. For a new test image, the sparse code obtained from every pixel is tested with the classifier to determine if it belongs to a tumor region. Particular embodiments also provide a highly accurate, low-complexity procedure for cases when the user can provide an initial estimate of the tumor in a test image.

Abstract: A profile of porosities and permeabilities calculated from several sample volumes in a system can speed up computational fluid dynamics (CFDs). Heterogeneous fluid flow paths can be calculation intense, limiting the accuracy of fluid-path models. Further, allowing a user to define a number of sample volumes in a model system allows pre-calculation of porosities and permeabilities for use in Navier-Stokes formulas for modeling fluid flow and gives the user control over calculation time and accuracy. This is helpful, for example, in modeling endovascular interventions where fluid dynamics are determinative in the efficacy or method of treatment for various vascular disorders, such as aneurysms, and heart disease. This is also beneficial in other healthcare contexts, like blood filters, embolic gels, endografts, web devices, and atrial appendage occluders, among others.

Abstract: A robust method to automatically segment and identify tumor regions in medical images is extremely valuable for clinical diagnosis and disease modeling. In various embodiments, an efficient algorithm uses sparse models in feature spaces to identify pixels belonging to tumorous regions. By fusing both intensity and spatial location information of the pixels, this technique can automatically localize tumor regions without user intervention. Using a few expert-segmented training images, a sparse coding-based classifier is learned. For a new test image, the sparse code obtained from every pixel is tested with the classifier to determine if it belongs to a tumor region. Particular embodiments also provide a highly accurate, low-complexity procedure for cases when the user can provide an initial estimate of the tumor in a test image.

Abstract: Measuring the number of glomeruli in the entire, intact kidney using non-destructive techniques is of immense importance in studying several renal and systemic diseases. In particular, a recent Magnetic Resonance Imaging (MRI) technique, based on injection of a contrast agent, cationic ferritin, has been effective in identifying glomerular regions in the kidney. In various embodiments, a low-complexity, high accuracy method for obtaining the glomerular count from such kidney MRI images is described. This method employs a patch-based approach for identifying a low-dimensional embedding that enables the separation of glomeruli regions from the rest. By using only a few images marked by the expert for learning the model, the method provides an accurate estimate of the glomerular number for any kidney image obtained with the contrast agent. In addition, the implementation of our method shows that this method is near real-time, and can process about 5 images per second.

Abstract: Approaches to three-dimensional (3D) data reconstruction are presented. The 3D data comprises 2D images. In some embodiments, the 2D images are directionally interpolated to generate directionally-interpolated 3D data. The directionally-interpolated 3D data are then segmented to generate segmented directionally-interpolated 3D data. The segmented directionally-interpolated 3D data is then meshed. In other embodiments, a 3D data set, which includes 2D flow images, is accessed. The accessed 2D flow images are then directionally interpolated to generate 2D intermediate flow images.

Abstract: Approaches to three-dimensional (3D) data reconstruction are presented. The 3D data comprises 2D images. In some embodiments, the 2D images are directionally interpolated to generate directionally-interpolated 3D data. The directionally-interpolated 3D data are then segmented to generate segmented directionally-interpolated 3D data. The segmented directionally-interpolated 3D data is then meshed. In other embodiments, a 3D data set, which includes 2D flow images, is accessed. The accessed 2D flow images are then directionally interpolated to generate 2D intermediate flow images.