Children's National Medical Center

Other

Marius George Linguraru

Marshall Lynn Summar

Kazunori Okada

Nabile M Safdar

Qian Zhao

Kenneth Rosenbaum

Juan Jose Cerrolaza

Raymond W Sze

Gary F Rogers

Laurie S Conklin

Craig Peters

Hans G Pohl

Awais Mansoor

Antonio Reyes Porras Perez

Robert A Avery

Carlos S Mendoza

Emily S Blum

Described is a method for segmenting an anatomical part, including identifying a landmark on a contour/surface of a 2D/3D model of the anatomical part in an ultrasound image, assigning a weight to the landmark, identifying different appearance patterns of a region around the landmark based on a previously stored training set; and applying a filter to the different appearance patterns of the region around the landmark in order to identify contours of the anatomical part.

Medical anatomy quantification: computer-aided diagnosis tool

Systems, apparatuses, and methods for diagnosing ureteropelvic junction obstruction. A set of biomarkers may be extracted from each of one or more time-activity curves associated with diuresis renography and/or functional magnetic resonance urography of one or more kidneys of a patient. One or more calculations can be performed based on the set of biomarkers to identify uretero-pelvic junction obstruction and a classification of severity or criticality thereof.

System, apparatus, and method for detection of ureteropelvic junction obstruction

Image segmentation of a structure of complex shape proceeds by acquiring by circuitry, a plurality of training images of the structure. A shape model of the structure is formed by fusing the plurality of training images. The shape model is partitioned by circuitry into a predetermined number of portions, an appearance and shape of each portion being determined by a classifier.

Image segmentation of complex structures

An image analysis device includes circuitry that receives one or more input images and detects a plurality of anatomical landmarks on the one or more input images using a pre-determined face model. The circuitry extracts a plurality of geometric and local texture features based on the plurality of anatomical landmarks. The circuitry selects one or more condition-specific features from the plurality of geometric and local texture features. The circuitry classifies the one or more input images into one or more conditions based on the one or more condition-specific features.

Device and method for classifying a condition based on image analysis

Discussed herein is an image processing device and a corresponding method for assessing inflammatory bowel diseases. A plurality of dynamic MRI sequence of images of a bowel that include peristalsis information of the bowel and static MRI image(s) of the bowel that identify at areas of inflammation, stenosis, and increased mural thickness of the bowel are processed. A displacement of the bowel caused by respiration of the patient is conducted on the dynamic MRI sequences by performing a non-rigid registration. A peristaltic activity is determined by performing motion flow analysis on the dynamic sequences of images. A peristaltic activity of the static MRI image(s) based on a weighted sum of two closest dynamic MRI sequences of images is computed and the static image is combined with the closest dynamic MRI sequence of images by performing a multi-modal registration.

Management, assessment and treatment planning for inflammatory bowel disease

A technique for performing computational analysis of craniosynostosis from CT images is discussed. An image-computing platform assesses craniofacial malformations by using a shape analysis methodology for image processing. The shape of a patient's craniofacial anatomy is assessed in terms of a statistical shape model that incorporates normal shape variability. The model allows for the identification of a closest normal variant to a patient's anatomy rather than comparing the patient's anatomy to an age-specific average shape. Further, an image registration and segmentation method is discussed which enables obtaining a personalized anatomical model for each patient, thereby achieving a precise and systematic characterization of shape abnormality. Visual (qualitative) and quantitative surgical planning, as well as pre-operative and post-operative assessments are achieved. Further, automatic surgical planning for cranial vault reconstruction using tessellation algorithms to establish the relationship between patient's anatomy and the closest normal variant is discussed.

Quantitative assessment of the skull

Growing community of inventors

Washington, DC, United States of America

Marius George Linguraru