Broncus Medical Inc.

The Penn State Research Foundation

Lav Rai

Henky Wibowo

Jason David Gibbs

Scott A Merritt

William E Higgins

Yixun Liu

Erkang Cheng

Thomas M Keast

Kun-Chang Yu

Ben Chow

Described herein is a novel method and system for segmentation of the spine using 3D volumetric data. In embodiments, a method includes an extracting step, localization step, and segmentation step. The extracting step comprises detecting the spine centerline and the spine canal centerline. The localization step comprises localizing the vertebra and intervertebral disc centers. Background and foreground constraints are created for each vertebra digit. Segmentation is performed for each vertebra digit and based on the hard constraints.

Learning-based spine vertebra localization and segmentation in 3D CT

Systems and methods for assisting a physician in a medical intervention comprises performing a 2D-3D deformable registration, and more particularly, performing a 2D-3D registration based on multiple live 2D fluoroscopic views, and implemented on a multi-core processing framework such as a Graphics Processing Unit.

GPU-based system for performing 2D-3D deformable registration of a body organ using multiple 2D fluoroscopic views

A method and system for estimating calibration parameters of a medical fluoroscope and more particularly a method and system which automatically determines intrinsic and distortion correction parameters of a fluoroscopy device.

System and method for automatically determining calibration parameters of a fluoroscope

The present invention is a method to register 3D image data with fluoroscopic images of the chest of a patient. The ribs and spine, which are visible in the fluoroscopic images, are analyzed and a rib signature or cost map is generated. The rib signature or cost map is matched to corresponding structures of the 3D image data of the patient. Registration is evaluated by computing a difference between the fluoroscopic image and a virtual fluoroscopic projected image of the 3D data. Related systems are also described.

Registration of fluoroscopic images of the chest and corresponding 3D image data based on the ribs and spine

A system and method are described for determining candidate fiducial marker locations in the vicinity of a lesion. Imaging information and data are input or received by the system and candidate marker locations are calculated and displayed to the physician. Additionally, interactive feedback may be provided to the physician for manually selected or identified sites. The physician may thus receive automatic real time feedback for a candidate fiducial marker location and adjust or accept a constellation of fiducial marker locations. 3D renderings of the airway tree, lesion, and marker constellations may be displayed.

Automated fiducial marker planning method

Radio-opaque marker

Display screen portion with icon

A medical analysis method for estimating a motion vector field of the magnitude and direction of local motion of lung tissue of a subject is described. In one embodiment a first 3D image data set of the lung and a second 3D image data set is obtained. The first and second 3D image data sets correspond to images obtained during inspiration and expiration respectively. A rigid registration is performed to align the 3D image data sets with one another. A deformable registration is perforated to match the 3D image data sets with one another. A motion vector field of the magnitude and direction of local motion of lung tissue is estimated based on the deforming step. The motion vector field may be computed prior to treatment to assist with planning a treatment as well as subsequent to a treatment to gauge efficacy of a treatment. Results may be displayed to highlight.

Surgical assistance planning method using lung motion analysis

A method for assisting a physician track a surgical device in a body organ of a subject during a procedure includes fluoroscopic based registration, and tracking. An initial registration step includes receiving a 3D image data of a subject in a first body position, receiving a real time fluoroscopy image data, and estimating a deformation model or field to match points in the real time fluoro image with a corresponding point in the 3D model. A tracking step includes computing the 3D location of the surgical device based on a reference mark present on the surgical device, and displaying the surgical device and the 3D model of the body organ in a fused arrangement.

Fluoroscopy-based surgical device tracking method

A medical analysis method for estimating a motion vector field of the magnitude and direction of local motion of lung tissue of a subject is described. In one embodiment a first 3D image data set of the lung and a second 3D image data set is obtained. The first and second 3D image data sets correspond to images obtained during inspiration and expiration respectively. A rigid registration is performed to align the 3D image data sets with one another. A deformable registration is performed to match the 3D image data sets with one another. A motion vector field of the magnitude and direction of local motion of lung tissue is estimated based on the deforming step. The motion vector field may be computed prior to treatment to assist with planning a treatment as well as subsequent to a treatment to gauge efficacy of a treatment. Results may be displayed to highlight.

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State College, PA, United States of America

Lav Rai