Siemens Aktiengesellschaft

Tina Ehtiati

Bernhard Geiger

John V Frangioni

Christophe Chefd'hotel

Ali Khamene

Jens Gühring

Fred S Azar

Vida Kianzad

Christophe Lenglet

A method for generating a positron emission tomography (PET) attenuation correction map from magnetic resonance (MR) images includes segmenting a 3-dimensional (3D) magnetic resonance (MR) whole-body image of a patient into low-signal regions, fat regions, and soft tissue regions; classifying the low-signal regions as either lungs, bones, or air by identifying lungs, identifying an abdominal station, and identifying a lower body station; and generating an attenuation map from the segmentation result by replacing the segmentation labels with corresponding representative attenuation coefficients.

System and method for the generation of attenuation correction maps from MR images

A method for unsupervised classification of histological images of prostatic tissue includes providing histological image data obtained from a slide simultaneously co-stained with NIR fluorescent and Hematoxylin-and-Eosin (H&E) stains, segmenting prostate gland units in the image data, forming feature vectors by computing discriminating attributes of the segmented gland units, and using the feature vectors to train a multi-class classifier, where the classifier classifies prostatic tissue into benign, prostatic intraepithelial neoplasia (PIN), and Gleason scale adenocarcinoma grades 1 to 5 categories.

System and method for unsupervised detection and gleason grading of prostate cancer whole mounts using NIR fluorscence

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Tina Ehtiati