Siemens Medical Solutions USA, Inc.

Siemens Healthcare Gmbh

Siemens Akteingesellschaft

Carestream Health, Inc.

Siemens Healthineers Ag

Siemens Corporation

Luca Bogoni

Dorin Comaniciu

Xiang Sean Zhou

David H Foos

Jianming Liang

Gerardo Hermosillo Valadez

Sarang Lakare

Anna Jerebko

Arun Krishnan

Yiqiang Zhan

Yoshihisa Shinagawa

Marcos Salganicoff

Adrian Barbu

Robert S Jones

Matthias Wolf

Pascal Cathier

Le Lu

Senthil Periaswamy

Zhuowen Tu

Kazunori Okada

Alok Gupta

Parmeet Singh Bhatia

Zhigang Peng

Venkat Raghavan Ramamurthy

Shu Liao

Fitsum Aklilu Reda

Eliot L Siegel

Gerardo Hermosillo-Valadez

Zhen Qian

Dharmendu Damany

A framework diagnostic test planning is described herein. In accordance with one aspect, the framework receives data representing one or more sample patients, diagnostic tests administered to the one or more sample patients, diagnostic test results and confirmed medical conditions associated with the administered diagnostic tests. The framework trains one or more classifiers based on the data to identify diagnostic test plans from the diagnostic tests. The one or more classifiers may then be applied to current patient data to generate a diagnostic test plan for a given patient.

Diagnostic test planning using machine learning techniques

A framework for patient management based on anatomic measurements is described herein. In accordance with one aspect, patient records are clustered into a set of sub-populations based on first anatomic measurements and characteristics extracted from first patient data associated with a population of patients. A representative sub-population similar to a patient may be determined from the set of sub-populations based on the patient data of the patient. A report that presents the second anatomic measurements associated with the patient in relation to corresponding first anatomic measurements associated with the representative sub-population may then be generated.

Patient management based on anatomic measurements

A framework for selective image reconstruction. In accordance with one aspect, the framework receives at least one first image that is reconstructed based on at least one first reconstruction attribute. At least one region of interest may then be identified in the at least one first image. The framework may selectively reconstruct at least one second image of the region of interest based on at least one second reconstruction attribute, wherein the second reconstruction attribute is different from the first reconstruction attribute. Results may then be generated based on the at least one second image.

Selective image reconstruction

A framework for automated measurement. In accordance with one aspect, the framework detects a centerline point of a structure of interest in an image. A centerline of the structure of interest may be traced based on the detected centerline point. A trained segmentation learning structure may be used to generate one or more contours of the structure of interest along the centerline. One or more measurements may then be extracted from the one or more contours.

Automated measurement based on deep learning

Described herein is a technology for enhanced visualization of medical image data. In one implementation, a region of interest is identified in a first set of images along at least one viewing direction. Based on the first set of images, a second set of images is reconstructed to include at least one selectively enhanced rendering of the region of interest. The selectively enhanced rendering may include a three-dimensional rendering or any other type of enhanced rendering to facilitate detection of abnormalities.

Enhanced visualization of medical image data

Systems and methods for supporting a diagnostic workflow from a computer system are disclosed herein. In accordance with one implementation, a set of pre-identified anatomical landmarks associated with one or more structures of interest within one or more medical images are presented to a user. In response to a user input selecting at least one or more regions of interest including one or more of the pre-identified anatomical landmarks, the user is automatically navigated to the selected region of interest. In another implementation, a second user input selecting one or more measurement tools is received. An evaluation may be automatically determined based on one or more of the set of anatomical landmarks in response to the second user input.

Systems and methods for viewing and analyzing anatomical structures

Locations, such as computer assisted detection marks, are presented in medical imaging diagnosis, such as for colon computed tomography diagnosis. To avoid fly-through and/or to assist in more rapid and thorough review of CAD marks, a summary of a plurality of detected candidates is pre-computed and presented to the user in a single collection. For example, a single display or screen view includes a gallery of images for different candidates. These pre-computed images are displayed on the screen, allowing the user to quickly identify locations of interest and rule out other locations. The summary may be used for navigation outside the CAD context, such as presenting different portions of an organ to be viewed.

Presentation of locations in medical diagnosis

A method of detecting malacia in airways includes providing a plurality of 3-dimensional (3D) digital lung images acquired over an inhalation/exhalation cycle from a same subject, each said image comprising a set of intensities on a 3-dimensional grid of points, registering a successive pair of images, wherein a registration mapping of the point grid of one image is calculated, locating airways in each of said pair of images, and collecting those points between the airways in each of said pair of images, wherein a volume of said collected points is a measure of an extent of malacia in said airways.

System and method for automatic detection and measurement of malacia in the airways

A method and system for polyp segmentation in computed tomography colonogrphy (CTC) volumes is disclosed. The polyp segmentation method utilizes a three-staged probabilistic binary classification approach for automatically segmenting polyp voxels from surrounding tissue in CTC volumes. Based on an input initial polyp position, a polyp tip is detected in a CTC volume using a trained 3D point detector. A local polar coordinate system is then fit to the colon surface in the CTC volume with the origin at the detected polyp tip. Polyp interior voxels and polyp exterior voxels are detected along each axis of the local polar coordinate system using a trained 3D box. A boundary voxel is detected on each axis of the local polar coordinate system based on the detected polyp interior voxels and polyp exterior voxels by boosted 1D curve parsing using a trained classifier. This results in a segmented polyp boundary.

Method and system for polyp segmentation for 3D computed tomography colonography

A method and system for providing a user interface for polyp annotation, segmentation, and measurement in computer tomography colonography (CTC) volumes is disclosed. The interface receives an initial polyp position in a CTC volume, and automatically segments the polyp based on the initial polyp position. In order to segment the polyp, a polyp tip is detected in the CTC volume using a trained 3D point detector. A local polar coordinate system is then fit to the colon surface in the CTC volume with the origin at the detected polyp tip. Polyp interior voxels and polyp exterior voxels are detected along each axis of the local polar coordinate system using a trained 3D box. A boundary voxel is detected on each axis of the local polar coordinate system based on the detected polyp interior voxels and polyp exterior voxels by boosted 1D curve parsing using a trained classifier. This results in a segmented polyp boundary. The segmented polyp is displayed in the user interface, and a user can modify the segmented polyp boundary using the interface. The interface can measure the size of the segmented polyp in three dimensions. The user can also use the interface for polyp annotation in CTC volumes.

User interface for polyp annotation, segmentation, and measurement in 3D computed tomography colonography

A method and system for detecting 3D objects in images is disclosed. In particular, a method and system for Ileo-Cecal Valve detection in 3D computed tomography (CT) images using incremental parameter learning and ICV specific prior learning is disclosed. First, second, and third classifiers are sequentially trained to detect candidates for position, scale, and orientation parameters of a box that bounds an object in 3D image. In the training of each sequential classifier, new training samples are generated by scanning the object's configuration parameters in the current learning projected subspace (position, scale, orientation), based on detected candidates resulting from the previous training step. This allows simultaneous detection and registration of a 3D object with full 9 degrees of freedom. ICV specific prior learning can be used to detect candidate voxels for an orifice of the ICV and to detect initial ICV box candidates using a constrained orientation alignment at each candidate voxel.

Method and system for detection and registration of 3D objects using incremental parameter learning

A method for segmenting a tubular structure includes providing a three-dimensional image containing the tubular structure, providing at least one seed point within the tubular structure, fitting an initial cylinder into the tubular structure at the seed point, adding cylinder segments to the initial cylinder in forward and backward directions within the three-dimensional image of the tubular structure by tracking the cylinder model over the tubular structure, modeling each cylinder segment using parameters to account for a three-dimensional orientation, a radius, a length and a curvature, describing an intensity image of the tubular structure using a second model to represent an edge and intensity distribution of the tubular structure, fitting the parameters as the cylinder segment is added to the intensity image, and outputting cylinder segments as a segmentation of the tube.

3D segmentation of the colon in MR colonography

A system and method for determining the presence or absence of candidate objects in a digital image by computing a gradient field of the digital image and then applying predetermined filters to the gradient field to obtain a response image which can be further processed to detect the objects. The application of these filters to the digital image can be performed by convolution and the filters can be adapted based on the shape of the object to be searched.

System and method for filtering and automatic detection of candidate anatomical structures in medical images

A system and method for toboggan-based object detection in cutting planes are provided. A method for detecting an object in an image includes: determining a region of interest (ROI) in the image; determining a toboggan potential for each image element in the ROI; extracting a plurality of cutting planes from the ROI; and performing a tobogganing in the cutting planes to form a toboggan cluster to determine a location of the object, wherein image elements inside the toboggan cluster are stored in a cluster-member list, image elements on an outer-border of the toboggan cluster are stored in an outer-border list and image elements on an inner-border of the toboggan cluster are stored in an inner-border list.

System and method for toboggan-based object detection in cutting planes

A method for three-dimensional contour tracking includes building a plurality of shape models, building a plurality of appearance models, training a learning/classification algorithm using said shape models and appearance models, localizing a contour using the said learning/classification algorithm about an object in a digitized image, and tracking said contour along said object in 3D incorporating said learning/classification algorithm.

Growing community of inventors

Philadelphia, PA, United States of America

Luca Bogoni