University of Iowa Research Foundation

Marval Biosciences, Inc.

Eric A Hoffman

Geoffrey McLennan

Milan Sonka

Joseph M Reinhardt

Ge Wang

Ananth V Annapragada

Ravi Bellamkonda

Juerg Tschirren

Theophano Mitsa

Chandra Vijayalakshmi

Renuka Uppaluri

Punam Kumar Saha

Syed Ahmed Nadeem

Soumik Ukil

Martin Donnelley

Deepa Gopalakrishnan

Melissa Suter

Novel systems and methods for airway segmentation are disclosed using confident airway volume, multi-scale topological and geometric leakage detection, exact leakage localization and correction, forbidden volume insertion and freezing, iterative shift in growing techniques starting with a conservative parameter/strategy and progressing toward generous ones, and a final pruning using machine learning, neural network, deep learning or artificial intelligence methods. The systems and methods are fully automated requiring no manual inputs or post-editing steps. The systems and methods use region growing-based segmentation to iteratively grow an airway tree starting with an initial seed, possibly inside the trachea, and a conservative segmentation parameter, e.g., a conservative threshold value for intensity-based region growing. The method then gradually progresses toward more generous values of the segmentation parameters until there is convergence.

Systems and methods for airway tree segmentation

Devices, such as computer readable media, and methods, such as automated methods, for labeling and/or matching. Some of the devices and methods are particularly useful for anatomical labeling of human airway trees. Some of the devices and methods are particularly useful for matching branch-points of human airway trees from represented in two or more graphs.

Methods and devices for labeling and/or matching

An image of an object is synergistically reconstructed using two or multiple imaging modalities. A first reconstructed image, showing structural information of the object is produced using a first imaging modality. The first reconstructed image is segmented, and known optical properties of the object are then mapped to the first reconstructed image. Optical signal emissions from the object are detected and registered with the first reconstructed image. A second reconstructed image volume is then produced using a second imaging modality, based on the mapped optical properties after registration between the first image and the data from the second modality. The second reconstructed image depicts some optical property, such as a bioluminescent source distribution, or optical properties, such as, attenuation and scattering properties, of the object.

Systems and methods for bioluminescent computed tomographic reconstruction

Methods and devices for airway tree labeling and/or matching

Automated methods for segmenting and smoothing volumes of interest, such as the mediastinal boundary of a lung. Devices and systems configured to perform the automated methods.

Methods of smoothing segmented regions and related devices

A method for automated analysis of textural differences present on an image volume. The image volume includes a plurality of volume elements, and each volume element has a gray level. The method includes defining a volume of interest (VOI); performing texture measures within the VOI; and classifying the VOI as belonging to a tissue pathology class based upon the texture measures. Computer readable media encoded with computer readable instructions for carrying out these functions. An apparatus that includes an image input adapted to receive a diagnostic medical image. The image includes a plurality of pixels, and each pixel has a particular gray level. The apparatus also includes a display for displaying a graphical user interface and the received image; and a processor adapted to perform texture measures on one or more groups of pixels within the image and classify each group of pixels to a tissue pathology class based upon the textures measures. The processor is further adapted to (1) associate a color to each group of pixels indicative of the group's tissue pathology class, (2) cause the display to display one or more of the colors on the image at the location of the associated group or groups of pixels, (3) permit a user to manually associate a tissue pathology class to a group of pixels, and (4) cause the display to display the manually-associated tissue pathology class.

Methods and apparatuses for analyzing images

Example compositions of liposomes with hydrophilic polymers on their surface, and containing relatively high concentrations of contrast-enhancing agents for computed tomography are provided. Example pharmaceutical compositions of such liposomes, when administered to a subject, provide for increased contrast of extended duration, as measured by computed tomography, in the bloodstream and other tissues of the subject. Also provided are example methods for making the liposomes containing high concentrations of contrast-enhancing agents, and example methods for using the compositions.

Compositions and methods for enhancing contrast in imaging

In one embodiment, a method that includes comparing a subject color medical image to normal color medical image data; and identifying abnormal pixels from the subject color medical image. Another embodiment includes a computer readable medium comprising machine readable instructions for implementing one or more steps of that method. Another embodiment includes a device that has a field programmable gate array configured to perform one or more of the steps of that method. Another embodiment includes a device that has an application specific integrated circuit configured to perform one or more of the steps of that method.

Methods and devices useful for analyzing color medical images

A method and apparatus for analyzing CT images to determine the presence of pulmonary tissue pathology, such as in emphysema, IPF, sarcoid, etc. In accordance with one embodiment, a CT slice is selected to perform an automated, objective, and quantitative analysis of the slice. Initially, an image processing stage is performed, which includes segmentation and edgementation of the selected CT slice for preparation of a series of objective, quantitative measures to be performed on the slice. A region of interest (ROI) is selected on the CT slice in which these objective, quatitative measures are to be taken. The first set of objective, quantitative measures are first order texture measures that describe a frequency of occurrence of all gray levels assigned to pixels within the ROI of the image slice. The second set of objective, quantitative measures are second order texture measures that characterize the spatial interdependencies between particular pixels of the ROI. Fractal analysis could also be performed to provide additional objective, quantitative measures of the ROI. The ROI is classified to a particular tissue pathology class based upon an optimal subset of first or second order texture measures and fractal measures obtained. A color-coded output is displayed for visual presentation to a user indicating the different tissue pathology classes assigned to different regions of the CT slice.

Growing community of inventors

Iowa City, IA, United States of America

Eric A Hoffman