The State University of New York

Viatronix Incorporated

Arie E Kaufman

Mark R Wax

Zhengrong Liang

Ming Wan

Kevin Kreeger

Dongqing Chen

Ingmar Bitter

Bin Li

Frank Dachille

Dongquing Chen

Baoquan Chen

Joseph Marino

Xianfeng David Gu

Hanspeter Pfister

Roni Yagel

Clemente T Roque

Konstantin Dmitriev

Eric E Smouha

Li-Yi Wei

Jacob Sharony

Qi Sun

Daniel Cohen-Or

Jerome Zhengrong Liang

Satya Sharma

Zhenrong Liang

Saeed Boorboor

Lichan Hong

Ajay Viswambharan

Stephen D Shapiro

Esther D Shilcrat

Philip M Lewis

Prateek Mishra

Donald P Harrington

Wei Zeng

Michael Poon

Daniel Cohen

Solomon E Shimony

Reuven Bakalash

Rong Zhao

Suzanne Yoakum-Stover

Wei Hong

Feng Qiu

Sarang Lakare

Lichon Hong

The exemplary system, method, and computer-accessible medium for generating a multiclass image segmentation model(s) can include receiving multiple single-class image datasets, receiving a target mask for each of the single-class image datasets, receiving a condition of an object associated with each of the single-class image datasets, and generating the multiclass image segmentation model(s) based on the single-class image datasets, the target masks, and the identification of the target objects. The single-class image datasets can include computer tomography images of abdominal organs. The single-class image datasets can be non-overlapping single-class image datasets. The single-class image datasets can include medical imaging datasets or cityscape datasets. The condition can include (i) an identification of a target object associated with each image in each single-class image dataset, (ii) a classification of each image associated with each single-class image dataset or (iii) an identifiable detail regarding each image in each single-class image datasets.

System, method, and computer-accessible medium for generating multi-class models from single-class datasets

An exemplary system, method, and computer-accessible medium for generating an image(s) of an anatomical structure(s) in a biological sample(s) can include receiving first wide field microscopy imaging information for the biological sample, generating second imaging information by applying a gradient-based distance transform to the first imaging information, and generating the image(s) based on the second imaging information. The second imaging information can be generated by applying an anisotropic diffusion procedure to the first imaging information. The second imaging information can be generated by applying a curvilinear filter and a Hessian-based enhancement filter after the application of the gradient-based distance transform. The second information can be generated by applying (i) a tube enhancement procedure or (ii) a plate enhancement procedure after the application of the gradient-based distance transform.

System, method, and computer-accessible medium for processing brain images and extracting neuronal structures

A system, method, and computer-accessible medium for using medical imaging data to screen for a cystic lesion(s) can include, for example, receiving first imaging information for an organ(s) of a one patient(s), generating second imaging information by performing a segmentation operation on the first imaging information to identify a plurality of tissue types, including a tissue type(s) indicative of the cystic lesion(s), identifying the cystic lesion(s) in the second imaging information, and applying a first classifier and a second classifier to the cystic lesion(s) to classify the cystic lesion(s) into one or more of a plurality of cystic lesion types. The first classifier can be a Random Forest classifier and the second classifier can be a convolutional neural network classifier. The convolutional neural network can include at least 6 convolutional layers, where the at least 6 convolutional layers can include a max-pooling layer(s), a dropout layer(s), and fully-connected layer(s).

System, method, and computer-accessible medium for virtual pancreatography

A system of generating a progressive representation associated with virtual and physical reality image data is disclosed. The system receives virtual image data associated with a virtual scene map of a virtual scene, and receives physical image data from the image capturing device, associated with a physical environment of a real scene. Perimeter information of the physical environment is determined. Boundary information of an obstacle associated with the physical environment is determined. A real scene map associated with the physical environment including an obstacle and one or more free space areas is generated. A corresponding virtual barrier is generated in the real scene map, the virtual barrier associated with the boundary information of the obstacle in the real scene map. A folding of the virtual scene map into the one or more free space areas of the real scene map is implemented, the free space areas surround or are adjacent the generated virtual barrier. A progressive representation to the wearable communication device is generated that is associated with virtual scene pixels of the virtual map corresponding to real scene points of the real scene map in a time interval. A corresponding method and computer-readable device are also disclosed.

System and method for generating a progressive representation associated with surjectively mapped virtual and physical reality image data

A computer-based method for generating a context preserving mapping of tubular structures represented by a 3D dataset having the steps of projecting a skeleton of a 3D tubular structure on to a 2D plane, and adjusting the projected skeleton to correct projection imbued distortion in skeleton length. The 2D projected skeleton is processed to remove intersections, and a surface boundary around the 2D skeleton is determined for the map. The 3D surface of the skeleton is mapped to match the 3D boundary to create a 3D map of the tubular structure.

System and method for context preserving maps of tubular structures

A system and method is provided which allows for the transfer of an image data file such as a medical image data file. Device scan slices of an object are acquired per time point. A portion of the device scan slices of the object, e.g., live body organ, per time point which illustrate a change in the object from a previous time point is selected. At least one optical temporal image in spiral fashion per time interval is formed from the selected portion. The at least one optical temporal image is transmittable as an image data file to a location.

Method and system for transfer of cardiac medical image data files

Provided is a method for obtaining a conformal mapping of first image data corresponding to a source mesh to a target mesh, by generating a template of the source mesh and a template of the target mesh, processing the generated source mesh template and the generated target mesh template to obtain a conformal mapping from the source mesh to the target mesh, mapping the first image data corresponding to the source mesh to second image data corresponding to the target mesh according to the obtained conformal mapping, and outputting a mapped texture corresponding to the mapped second image data to the display corresponding to the target mesh.

Method for conformal visualization

A method, system, and computer-readable medium for detecting a disease of a prostate. Exemplary embodiments of the present disclosure can include receiving an image dataset acquired with at least one acquisition mode; segmenting a region of interest including the prostate from the dataset; applying conformal mapping to map the region of interest to a canonical shape; generating a 3D visualization of the prostate using the canonically mapped dataset; and applying computer aided detection (CAD) to the canonically mapped volume to detect a region of disease of the organ.

System and method for prostate visualization and cancer detection

In the present methods, the automatic detection of polyps is converted into a 2D pattern recognition problem using conformal mapping and direct volume rendering. The colon surface is first segmented and extracted from the CT data set of the abdomen, which is then mapped to a 2D plane using conformal mapping. Ray casting is used to determine sub-surface density values and the flattened image is rendered using a volume rendering technique with a translucent electronic biopsy transfer function. Polyp candidates are detected by a clustering method which identifies regions of elevated sub-surface density. The potential for false positives is reduced by analyzing the volumetric shape and texture features of the polyp candidate regions.

System and method for computer aided polyp detection

Virtual navigation () and examination of virtual objects are enhanced using methods of insuring that an entire surface to be examined has been properly viewed. A user interface (FIG.) identifies regions which have not been subject to examination and provides a mechanism () to route the user to these regions in the 3D display. Virtual examination is further improved by the use of measuring disks () to enhance quantitative measurements such as diameter, distance, volume and angle. Yet another enhancement to the virtual examination of objects is a method of electronic segmentation, or cleaning, which corrects for partial volume effects occurring in an object.

Enhanced virtual navigation and examination

A method of computer aided treatment planning is performed by generating and manipulating a three dimensional (3D) image of a region which includes at least one anatomical structure for which treatment, such as surgery, biopsy, tissue component analysis, prosthesis implantation, radiation, chemotherapy and the like, is contemplated. A virtual intervention, which simulates at least a portion of the contemplated treatment, is performed in the 3D image. The user can then determine the effect of the intervention and interactively modify the intervention for improved treatment results. Preferably, a warning is automatically provided if the intervention poses a risk of detrimental effect. The user can navigate through the contemplated region in the 3D image and assess the results. The treatment plans can be saved for comparison and post treatment evaluation.

Computer aided treatment planning

In accordance with the present invention, a method for determining a centerline through a region of interest in a 3D image dataset is provided. The method includes identifying the boundaries of the region of interest and identifying the endpoints of the region of interest. For those points within the boundaries, a penalty value which is a function of the proximity of the point to a boundary is determined. A centerline is then identified by the path connecting the endpoints which has the minimum penalized distance wherein the penalized distance reflects the actual accumulated pathlength and the penalties associated with the points along the path. From the centerline, branches of a complete skeleton can be established by determining branch endpoints and then finding the minimum penalized distance from each endpoint the centerline or another intersecting branch.

Centerline and tree branch skeleton determination for virtual objects

Methods for generating a three-dimensional visualization image of an object, such as an internal organ, using volume visualization techniques are provided. The techniques include a multi-scan imaging method; a multi-resolution imaging method; and a method for generating a skeleton of a complex three dimension object. The applications include virtual cystoscopy, virtual laryngoscopy, virtual angiography, among others.

System and method for performing a three-dimensional virtual examination of objects, such as internal organs

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Plainview, NY, United States of America

Arie E Kaufman