Siemens Medical Solutions Usa, Inc.

Google Inc.

Maneesh Dewan

Yiqiang Zhan

Xiang Sean Zhou

Gerardo Hermosillo Valadez

Zhigang Peng

Zhao Yi

Marcos Salganicoff

Paul Vincent Byrne

Vikas Raykar

Anna Jerebko

Christine Lorenz

Harald Steck

Zhaoyang Xu

Tatsuo Nomura

A first digital map is displayed in a viewport at an initial position. When a user gesture that communicates motion to the viewport is detected, a trajectory of the viewport from the initial position to a target position is determined based on kinematic quantities of the communicated motion. Map data for displaying a second digital map in the viewport at the target position is retrieved from a first memory, prior to the viewport reaching the target position. The retrieved map data is stored in a second memory having a higher speed of access than the first memory. The second memory is retrieved for display via the user interface when the viewport is at the target position.

Efficient fetching of a map data during animation

Disclosed herein is a framework for facilitating adaptive anatomical region prediction. In accordance with one aspect, a set of exemplar images including annotated first landmarks is received. User definitions of first anatomical regions in the exemplar images are obtained. The framework may detect second landmarks in a subject image. It may further compute anatomical similarity scores between the subject image and the exemplar images based on the first and second landmarks, and predict a second anatomical region in the subject image by adaptively combining the first anatomical regions based on the anatomical similarity scores.

Adaptive anatomical region prediction

Systems and methods are provided for detecting anatomical components in images. In accordance with one implementation, at least one anchor landmark is detected in an image. The position of the anchor landmark is used to detect at least one bundle landmark in the image. In accordance with another implementation, at least two neighboring landmarks are detected in an image, and used to detect at least one anatomical primitive in the image.

Image-based detection using hierarchical learning

Described herein is a technology for facilitating deformable model-based segmentation of image data. In one implementation, the technology includes receiving training image data () and automatically constructing a hierarchical structure () based on the training image data. At least one spatially adaptive boundary detector is learned based on a node of the hierarchical structure ().

Hierarchical deformable model for image segmentation

A method of detecting an anatomical primitive in an image volume includes detecting a plurality of transformationally invariant points (TIPS) in the volume, aligning the volume using the TIPs, detecting a plurality landmark points in the aligned volume that are indicative of a given anatomical object, and fitting a target geometric primitive as the anatomical primitive based using the detected landmark points.

Anatomical primitive detection

A method and system are provided for imaging by predicting, from multiple real time MR imaging data, motion of an object and subsequently obtaining high-resolution imaging data of the object using the predicted motion of the object. Thus, the process uses real time images to derive a history of the motion of the object and thereby generate a predicted trajectory of the object and then uses this trajectory to determine the projected position of the object during a subsequent, separate, high-resolution data acquisition phase.

Method and system for motion compensation in magnetic resonance (MR) imaging

Described herein is a framework for constructing a hierarchical classifier for facilitating classification of digitized data. In one implementation, a divergence measure of a node of the hierarchical classifier is determined. Data at the node is divided into at least two child nodes based on a splitting criterion to form at least a portion of the hierarchical classifier. The splitting criterion is selected based on the divergence measure. If the divergence measure is less than a predetermined threshold value, the splitting criterion comprises a divergence-based splitting criterion which maximizes subsequent divergence after a split. Otherwise, the splitting criterion comprises an information-based splitting criterion which seeks to minimize subsequent misclassification error after the split.

Hierarchical classifier for data classification

According to an aspect of the invention, a method for training a classifier for classifying candidate regions in computer aided diagnosis of digital medical images includes providing a training set of annotated images, each image including one or more candidate regions that have been identified as suspicious, deriving a set of descriptive feature vectors, where each candidate region is associated with a feature vector. A subset of the features are conditionally dependent, and the remaining features are conditionally independent. The conditionally independent features are used to train a na&#xef;ve Bayes classifier that classifies the candidate regions as lesion or non-lesion. A joint probability distribution that models the conditionally dependent features, and a prior-odds probability ratio of a candidate region being associated with a lesion are determined from the training images. A new classifier is formed from the na&#xef;ve Bayes classifier, the joint probability distribution, and the prior-odds probability ratio.

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West Chester, PA, United States of America

Maneesh Dewan