Cognex Corporation

Synapix, Inc.

Medispectra, Incorporated

Iterative Scopes, Inc.

Corindus, Inc.

Jean-Pierre Schott

Paul B Madden

William M Silver

Kevin Schomacker

John Stoddard Robotham

Ross Flewelling

Daniel Wang

Jonathan Ng

Steven Mark Rosenthal

David Askey

Robert Anthony Wolff

Steven J Blacker

Per Bergman

Jeffrey D Kurtze

Nicholas Kottenstette

Robert H Elden

Curt A Rawley

Philip R Moorby

Chunsheng Jiang

Peter J Costa

Charles C Abele

Austin Wang

Perikumar Mukundbhai Javia

Sloane Allebes Phillips

Christopher E Griffin

Thomas Varner

Lavi Erisson

Neelima Chavali

Wesley K Cobb

Joseph Henry

Mary Cacciatore

Kenneth J Eyring

Bret F Draayer

Stephen T Sum

Jerry Jennings

Kevin T Schomacker

Chunsheng Jlang

Ross F Flewelling

This specification describes systems and methods for performing endoscopy, obtaining medical images for inflammatory bowel disease (IBD) and scoring severity of IBD in patients. The methods and systems are configured for using machine learning to determine measurements of various characteristics related to IBD. The methods and systems may also obtain and incorporate electronic health data of patients along with endoscopic data to use for scoring purposes.

Systems and methods for analysis of medical images for scoring of inflammatory bowel disease

This specification describes systems and methods for obtaining various patient related data for inflammatory bowel disease (IBD). The methods and systems are configured for using machine learning to determine measurements of various characteristics and provide analysis related to IBD. The methods and systems may also obtain and incorporate electronic health data as well as other relevant data of patients along with endoscopic data to use for prediction IDB progression and recommending treatment.

Methods for treatment of inflammatory bowel disease

Disclosed is the automated rapid advancement of a guide wire through a guide catheter using x-ray markers. The procedure involves providing a guide wire with a marker at or adjacent its tip, providing a guide catheter with a marker at or adjacent its distal terminus, rapidly advancing the guide wire through the guide catheter from its proximal end to its distal end under the control of an automated apparatus, and terminating the automated rapid advancement when the guide wire tip marker becomes adjacent to the guide catheter distal end marker. Fluoroscopic images may be taken of the of the guide wire as it rapidly advances through the guide catheter and image processing software used to determine when to terminate the rapid advancement. This software may use either the position or the velocity of the guide wire to make this determination.

X-ray marker guided automated guide wire or working catheter advancement

The invention provides methods for determining the probability that a given region of a tissue sample contains tissue of a given category, such as CIN 1 (cervical intraepithelial neoplasia, grade 1), CIN 2/3 (cervical intraepithelial neoplasia grades 2 and/or 3), normal squamous, normal columnar, and metaplasia, for example. The invention provides increased diagnostic accuracy by combining a plurality of statistical classification techniques. Furthermore, in one embodiment, the invention comprises combining one or more statistical techniques with one or more non-statistical classification techniques.

Methods and apparatus for characterization of tissue samples

The invention provides methods for displaying diagnostic results obtained from a tissue sample. In general, the invention assigns tissue-class probability values to discrete regions of a patient sample, and creates an overlay for displaying the results. The overlay facilitates display of the tissue class probabilities in a way that reflects the diagnostic relevance of the data. For example, methods of the invention comprise applying filtering and color-blending techniques in order to facilitate display of diagnostic results.

Methods and apparatus for displaying diagnostic data

A technique for temporal smoothing of results from a scene analysis process which creates a sequence of visually pleasing and acceptable images generated in whole or part from such results. The technique applies temporal smoothing across time-related sets of scene analysis results. The spatial smoothing can be applied at various steps in the process: to images in the original sequence, to intermediate or final results of scene analysis in either a pixel-oriented or geometric domain, or to the images generated in whole or part from the scene analysis results. In a preferred embodiment, different levels of smoothing are applied to different parts of the intermediate or final results. The differentiation can be done using image masks (for pixel-oriented results) or geometry selection techniques (for geometric results). This allows a higher level of smoothing in certain areas where the generated visual images will benefit from such additional smoothing, while avoiding over-smoothing in other areas where the smoothing would obscure critical details within the generated images.

Temporal smoothing of scene analysis data for image sequence generation

A technique for displaying a visual representation of an estimated three-dimensional scene structure and the values of various parameters associated with the scene, together with a visual representation of at least one two-dimensional image used in the scene structure estimation algorithm. A user inputs information by adjusting parameters and/or specifying an element or region of the visual representations and supplies mark-ups and other information such as attributes for the element or region to be applied during a next iteration of the scene structure estimation algorithm. The scene structure estimation algorithm is then re-executed and the process repeats until the user is satisfied with the resulting visual scene structure.

Computer assisted mark-up and parameterization for scene analysis

A technique for converging upon a computer-based model of a real world or synthetic scene. The computer model makes use of abstraction-based data objects as well as image-based data objects. A correlation mesh provides links between related image-based and abstraction-based objects. An initial step in a process analyzes an input image stream and user inputs to derive initial image-based objects and abstraction-based objects for the scene model. Subsequent steps in the process allow user inputs to refine the image-based objects, abstraction-based objects and/or the correlation mesh. As a result, refinements to the image-based object model of the scene can improve the abstraction-based model of the scene, and refinements to the abstraction-based object model can improve the image-based model.

Adaptive modeling and segmentation of visual image streams

A method for Golden Template Comparison (GTC) is provided that can be used to efficiently perform flaw and defect detection on a two-dimensional test image that is at least rotated and/or scaled and/or sub-pixel translated. Run-time inspection speed and accuracy is substantially improved by retreiving a golden template image that is rotated and/or scaled and/or translated in a manner substantially similar to the test image. This is accomplished by storing, in an array, a varied plurality of golden template images, each golden template image being characterized by a different combination of at least rotation and/or scale and/or sub-pixel translation. The array is indexed by the respective quantized rotation and/or quantized scale and/or sub-pixel translation of each version of the golden template image. The array can be either one-dimensional or multi-dimensional. At run-time, the values of the rotation and/or scale and/or sub-pixel translation of each test image are measured, and then quantized, thereby providing a unique index into the multi-dimensional array of reference and threshold images. The reference and threshold images stored at the memory location corresponding to the index are retrieved and then used for comparison with the test image to provide a difference image to be analyzed for flaws or defects.

Golden template comparison for rotated and/or scaled images

A very fast, memory efficient, single-step solution for coarse alignment with determination of angular displacement, and inspection for surface defects on circular objects with non-symmetric patterns and random orientation is described. A suitable geometric partition model of an image is constructed that includes a plurality of spatial bins that can be used for rotation estimation and defect detection. In one embodiment for inspection of circular objects, the geometric partitioning includes 256 spatial bins constituted by concentric circular rings divided into sectors. The number of sectors and thickness of the rings are such that the approximate number of pixels in each region is the same. A set of numbers that encode rotational position and gray level information on the plurality of spatial bins of a reference object are acquired during training and used during run-time wherein the same information relative to a circular object being inspected is computed, and the data are correlated to determine both the coarse rotation and gray level difference in pertinent ones of the plurality of spatial bins.

Single step coarse registration and inspection of circular objects

A coprocessor in an image processing system is coupled to the bus to which a CPU and RAM holding image data are also coupled. The coprocessor extracts an input pixel stream corresponding to input images from selected bus transactions, performs computations on the input stream to produce output pixel streams corresponding to output images, and inserts the output pixel streams into selected CPU-to-memory bus transactions so that the memory stores the data. The CPU generates the selected bus transactions with specially marked address and/or control signals. The coprocessor includes a lookup table, and a first row delay. The row delay accumulates the three most recent rows of input pixels, which are sent to Sobel and rank processing sections for neighborhood processing. The results are thresholded and formatted, and are either output directly or passed through an additional pair of row delays to accumulate three rows of result data for neighborhood peak detection.

Vision coprocessing

Growing community of inventors

Weston, MA, United States of America

Jean-Pierre Schott