Abstract: A method and for quantifying a three-dimensional medical image volume are provided. An embodiment of the method includes: providing a two-dimensional representation image based on the medical image volume; defining a region of interest in the two-dimensional representation image; generating a feature signature for the region of interest; defining a plurality of two-dimensional image patches in the medical image volume; calculating, for each of the image patches, a degree of similarity between the region of interest and the respective image patch on the basis of the feature signature; visualizing the degrees of similarities.

Abstract: A visualization framework based on document representation learning is described herein. The framework may first convert a free text document into word vectors using learning word embeddings. Document representations may then be determined in a fixed-dimensional semantic representation space by passing the word vectors through a trained machine learning model, wherein more related documents lie closer than less related documents in the representation space. A clustering algorithm may be applied to the document representations for a given patient to generate clusters. The framework then generates a visualization based on these clusters.

Abstract: A method and for quantifying a three-dimensional medical image volume are provided. An embodiment of the method includes: providing a two-dimensional representation image based on the medical image volume; defining a region of interest in the two-dimensional representation image; generating a feature signature for the region of interest; defining a plurality of two-dimensional image patches in the medical image volume; calculating, for each of the image patches, a degree of similarity between the region of interest and the respective image patch on the basis of the feature signature; visualizing the degrees of similarities.

Abstract: The disclosure relates to method and device for determining at least a portion of an anatomy from a set of medical images. Thereafter, the image acquisition protocol associated with the medical images is determined automatically based on the determined anatomy. The image acquisition protocol is automatically determined by extracting one or more features associated with the medical images characterizing the acquisition protocol. Thereafter, the medical images are classified based in the features. Further, the medical images are assigned with an acquisition protocol label based on the classification of the medical images. The medical images are displayed with the respective protocol labels. Further, medical images acquired with an identified image acquisition protocol at different time instances are displayed for comparative analysis.

Abstract: 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.

Abstract: A framework for coarse orientation detection in image data. In accordance with one aspect, the framework trains a learning structure to recognize a coarse orientation of the anatomical structure of interest based on training images. The framework may then pass one or more current images through the trained learning structure to generate a coarse orientation of the anatomical structure of interest. The framework then outputs the generated coarse orientation of the anatomical structure of interest.

Abstract: A method and system are provided for automatically positioning a medical diagnostic device. The method and system use a database of images that includes reference images and user-preferred images to position the medical diagnostic device. The medical diagnostic device in an initial position is moved to a final position based on the images in the database.

Abstract: 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.

Abstract: Perceptual visual quality of different image sets is automatically assessed. A computing device includes a processor and a memory coupled to the processor. The memory includes an image assessment module configured to obtain features from each of the image sets, and generate feature vectors corresponding to the obtained features. The image quality assessment module is configured to compute a set of quality scores corresponding to the feature vectors associated with each of the image sets. The image quality assessment module is also configured to automatically determine an image set having an optimal perceptual visual quality among the image sets using the corresponding set of quality scores.

Abstract: A framework for coarse orientation detection in image data. In accordance with one aspect, the framework trains a learning structure to recognize a coarse orientation of the anatomical structure of interest based on training images. The framework may then pass one or more current images through the trained learning structure to generate a coarse orientation of the anatomical structure of interest. The framework then outputs the generated coarse orientation of the anatomical structure of interest.

Abstract: A visualization framework based on document representation learning is described herein. The framework may first convert a free text document into word vectors using learning word embeddings. Document representations may then be determined in a fixed-dimensional semantic representation space by passing the word vectors through a trained machine learning model, wherein more related documents lie closer than less related documents in the representation space. A clustering algorithm may be applied to the document representations for a given patient to generate clusters. The framework then generates a visualization based on these clusters.

Abstract: A method and system are provided for automatically positioning a medical diagnostic device. The method and system use a database of images that includes reference images and user-preferred images to position the medical diagnostic device. The medical diagnostic device in an initial position is moved to a final position based on the images in the database.

Abstract: The disclosure relates to method and device for determining at least a portion of an anatomy from a set of medical images. Thereafter, the image acquisition protocol associated with the medical images is determined automatically based on the determined anatomy. The image acquisition protocol is automatically determined by extracting one or more features associated with the medical images characterizing the acquisition protocol. Thereafter, the medical images are classified based in the features. Further, the medical images are assigned with an acquisition protocol label based on the classification of the medical images. The medical images are displayed with the respective protocol labels. Further, medical images acquired with an identified image acquisition protocol at different time instances are displayed for comparative analysis.

Abstract: The embodiments relate to a method and device for determining plaque deposits in coronary arteries. According to the method, a coronary angiography radiography image of a subject is obtained using an imaging modality. The obtained radiography image is calibrated for further analysis of the radiography image. Image processing techniques such as thresholding and histogram equalization are applied on the radiography image to extract vessel tree structure for analysis. Further, an inner lumen width and an outer vessel width are computed based on the processed radiography image. Further, a level of plaque deposition is determined based on the inner lumen and outer vessel dimensions. Further, the result based on the analysis is displayed to the user. The result includes the level of plaque deposits within the lumen on the vascular structure and possible risks posed by the plaque deposits to a subject.

Abstract: The embodiments relate to a method and device for determining plaque deposits in coronary arteries. According to the method, a coronary angiography radiography image of a subject is obtained using an imaging modality. The obtained radiography image is calibrated for further analysis of the radiography image. Image processing techniques such as thresholding and histogram equalization are applied on the radiography image to extract vessel tree structure for analysis. Further, an inner lumen width and an outer vessel width are computed based on the processed radiography image. Further, a level of plaque deposition is determined based on the inner lumen and outer vessel dimensions. Further, the result based on the analysis is displayed to the user. The result includes the level of plaque deposits within the lumen on the vascular structure and possible risks posed by the plaque deposits to a subject.

Abstract: Perceptual visual quality of different image sets is automatically assessed. A computing device includes a processor and a memory coupled to the processor. The memory includes an image assessment module configured to obtain features from each of the image sets, and generate feature vectors corresponding to the obtained features. The image quality assessment module is configured to compute a set of quality scores corresponding to the feature vectors associated with each of the image sets. The image quality assessment module is also configured to automatically determine an image set having an optimal perceptual visual quality among the image sets using the corresponding set of quality scores.