Rutgers, The State University of New Jersey

Other

Dimitris Metaxas

Debabrata Banerjee

Xiaolei Sharon Huang

Atul Kanaujia

Anant Madabhushi

Qiaoying Huang

Junzhou Huang

Zachary A Daniels

Systems and methods for joint reconstruction and segmentation of organs from magnetic resonance imaging (MRI) data are provided. Sparse MRI data is received at a computer system, which jointly processes the MRI data using a plurality of reconstruction and segmentation processes. The MRI data is processed using a joint reconstruction and segmentation process to identify an organ from the MRI data. Additionally, the MRI data is processed using a channel-wise attention network to perform static reconstruction of the organ from the MRI data. Further, the MRI data can is processed using a motion-guided network to perform dynamic reconstruction of the organ from the MRI data. The joint processing allows for rapid static and dynamic reconstruction and segmentation of organs from sparse MRI data, with particular advantage in clinical settings.

Systems and methods for joint reconstruction and segmentation of organs from magnetic resonance imaging data

An image processing neural network system includes a base net of at least one convolutional layer and at least one pooling layer; and a scenario block layer. The scenario block layer performs scene classification and generates a dictionary of scenarios and a vector of scenario encoding coefficients to output a probabilistic scene class assignment and the vector of scenario encoding coefficients. The vector of scenario encoding coefficients corresponds to reasoning for the scene classification.

Image processing neural network systems and methods with scene understanding

A system and methods for generating 3D images from 2D bioluminescent images and visualizing tumor locations are provided. A plurality of 2D bioluminescent images of a subject are acquired using any suitable bioluminescent imaging system. The 2D images are registered to align each image and to compensate for differences between adjacent images. After registration, corresponding features are identified between consecutive sets of 2D images. For each corresponding feature identified in each set of 2D images, an orthographic projection model is applied, such that rays are projected through each point in the feature. The intersection points of the rays are plotted in a 3D image space. All of the 2D images are processed in the same manner, such that a resulting 3D image of a tumor is generated.

System and method for generating three-dimensional images from two-dimensional bioluminescence images and visualizing tumor shapes and locations

A system and method for tracking features is provided which allows for the tracking of features that move in a series of images. A training set of images is processed to produce clustered shape subspaces corresponding to the set of images, such that non-linear shape manifolds in the images are represented as piecewise, overlapping linear surfaces that are clustered according to similarities in perspectives. A landmark-based training algorithm (e.g., ASM) is applied to the clustered shape subspaces to train a model of the clustered shape subspaces and to create training data. A subsequent image is processed using the training data to identify features in the target image by creating an initial shape, superimposing the initial shape on the target image, and then iteratively deforming the shape in accordance with the model until a final shape is produced corresponding to a feature in the target image.

System and method for detecting and tracking features in images

A system and method of reconstructing an image through solution of a data fitting problem, wherein the data fitting problem is not susceptible to efficient solution as a whole, is disclosed, which may comprise gathering, via a computing device, k-space image data, selecting a data fitting problem solution algorithm for the k-space data, decomposing the data fitting problem solution into a plurality of sub-problem solutions each susceptible to efficient solution separately for the k-space data, obtaining, via the computing device, the plurality of sub-problem solutions for the k-space data, and reconstructing, via the computing device, the image based upon a weighted average of the plurality of sub-problem solutions for the k-space data. The image may be a magnetic resonance image. The data fitting problem may comprise a minimization of a plurality of linear terms of a least square data fitting solution. The image may comprise under-sampled k-space image data.

Image reconstruction

A system and methods for generating 3D images () from 2D bioluminescent images () and visualizing tumor locations are provided. A plurality of 2D bioluminescent images of a subject are acquired during a complete revolution of an imaging system about a subject, using any suitable bioluminescent imaging system. After imaging, the 2D images are registered () according to the rotation axis to align each image and to compensate for differences between adjacent images. After registration (), corresponding features are identified between consecutive sets of 2D image (). For each corresponding feature identified in each set of 2D images an orthographic projection model () is applied, such that rays are projected through each point in the feature. The intersection point of the rays are plotted in a 3D image of a tumor is generated. The 3D image can be registered with a reference image of the subject, so that the shape and location of the tumor can be precisely visualized with respect to the subject.

System and methods for generating three-dimensional images from two-dimensional bioluminescence images and visualizing tumor shapes and locations

A system and method for tracking features, e.g., facial features, is provided, which allows for the tracking of features which move in a series of images and whose shape changes nonlinearly due to perspective projection and complex 3D movements. A training set of images is processed to produce clustered shape subspaces corresponding to the set of images, such that non-linear shape manifolds in the images are represented as piecewise, overlapping linear surfaces that are clustered according to similarities in perspectives. A landmark-based training algorithm (e.g., ASM) is applied to the clustered shape subspaces to train a model of the clustered shape subspaces and to create training data. A subsequent image is processed using the training data to identify features in the target image by creating an initial shape, superimposing the initial shape on the target image, and then iteratively deforming the shape in accordance with the model until a final shape is produced corresponding to a feature in the target image.

A method and apparatus for automatically detecting breast tumors and lesions in images, including ultrasound, digital and analog mammograms, and MRI images, is provided. An image of a breast is acquired. The image is filtered and contrast of the image is enhanced. Intensity and texture classifiers are applied to each pixel in the image, the classifiers indicative of the probability of the pixel corresponding to a tumor. A seed point is identified within the image, and a region of interest is grown around the seed point. Directional gradients are calculated for each pixel of the image. Boundary points of the region of interest are identified. The boundary points are passed as inputs to a deformable model. The deformable model processes the boundary points to indicate the presence or absence of a tumor.

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North Brunswick, NJ, United States of America

Dimitris Metaxas