System for Automatic Segmentation of Male Pelvis Structures from CT Images

Award Information
Agency:
Department of Health and Human Services
Branch
n/a
Amount:
$2,038,001.00
Award Year:
2008
Program:
SBIR
Phase:
Phase II
Contract:
4R44CA119571-02
Award Id:
88621
Agency Tracking Number:
CA119571
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
6320 Quadrangle Drive, SUITE 380, DURHAM, NC, 27517
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
188546803
Principal Investigator:
EDWARD CHANEY
(919) 361-2148
CHANEY@MED.UNC.EDU
Business Contact:
() -
ed.chaney@morphormics.com
Research Institution:
n/a
Abstract
DESCRIPTION (provided by applicant): Image segmentation is a commonly performed clinical practice for extracting geometrical descriptions of internal anatomical objects from volume images such as computed tomography and magnetic resonance images. Shortcomi ngs of current practice have motivated an important area of research involving statistically trainable deformable-shape models (DSMs) for automatic segmentation. The general approach is to apply a DSM to an image and cause the model to undergo a series of deformations converging to a close match between the model and the target anatomical object(s). The deformations are driven, in an appropriate statistical framework, by mathematical optimization. The overall aim is to develop and clinically evaluate a work station for automatic segmentation of anatomical structures in the male pelvis from CT images for image- guided applications in radiation therapy using technology based on a particularly powerful class of DSMs called m-reps. Segmented CT images provide gui dance for critical treatment planning and radiation delivery decisions in radiation therapy. It is likely that segmentation is performed more often for these purposes than for all the other medical applications combined. Current interactive contouring meth ods in clinical practice are extremely time consuming and expensive, and the contours demonstrate significant inter- and intra-user variabilities that adversely affect the clinical decisions that rely on them. The proposed methodology will overcome these s hortcomings and improve the effectiveness of radiation therapy for treating cancer.

* information listed above is at the time of submission.

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