Segmenting occluded anatomical structures in medical images

• US 20060122480A1
• Filed: 11/22/2004
• Published: 06/08/2006
• Est. Priority Date: 11/22/2004
• Status: Active Grant

• Current Assignee: CareStream Health Incorporated
• Original Assignee: CareStream Health Incorporated
• Inventors: Luo, Jiebo, Singhal, Amit
• Granted Patent: US 7,382,907 B2
• Time in Patent Office: Days
• US Class Current: 600/407
• CPC Class Codes: G06T 2207/10116 X-ray image
• CPC Class Codes: G06T 2207/20081 Training; Learning
• CPC Class Codes: G06T 2207/30048 Heart; Cardiac
• CPC Class Codes: G06T 2207/30061 Lung
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06T 7/12 Edge-based segmentation
• CPC Class Codes: G06T 7/149 involving deformable models, e.g. active contour models

Abstract

A method for segmenting an anatomical structure having portions occluded in a digital image wherein the digital image is produced by a projection-based medical imaging modality. The method includes the steps of: outlining an unoccluded portion of the anatomical structure in a training image; augmenting the occluded portion of the anatomical structure according to a pre-determined atlas of the anatomical structure; generating an augmented training example using the outlined and augmented portions; repeating the steps of outlining, augmenting, and generating for a plurality of training images to train a statistical model of the structure; accessing the digital image; and segmenting the anatomical structure in the accessed digital image according to the statistical model.

Claims

1. A method for segmenting an anatomical structure having portions occluded in a digital image, the digital image being produced by a projection-based medical imaging modality, the method comprising the steps of:

outlining an unoccluded portion of the anatomical structure in a training image;

augmenting the occluded portion of the anatomical structure according to a pre-determined atlas of the anatomical structure;

generating an augmented training example using the outlined and augmented portions;

repeating the steps of outlining, augmenting, and generating for a plurality of training images to train a statistical model of the structure;

accessing the digital image; and

segmenting the anatomical structure in the accessed digital image according to the trained statistical model.

2. The method of claim 1, wherein the statistical model is comprised of a shape model and an appearance model.

3. The method of claim 1, wherein the statistical model is an active shape model.

4. The method of claim 1, wherein the statistical model is a shape regularized active contour model.

5. The method of claim 1, further comprising the step of determining a size of the anatomical structure according to an area within the segmented anatomical structure.

6. The method of claim 1, further comprising the step of displaying, printing, or transmitting the segmented anatomical structure.

7. A computer product embodied in a computer readable medium for performing the steps of claim 1.

8. A system for segmenting an anatomical structure having portions occluded in a digital image, the digital image being produced by a projection-based medical imaging modality, the system comprising:

means for accessing the digital image produced by the projection-based medical imaging modality; and

a programmable computer having a microprocessor, computer memory, and a computer program stored in the computer memory for performing the steps of;

outlining an unoccluded portion of the anatomical structure in a training image;

augmenting the occluded portion of the anatomical structure according to a pre-determined atlas of the anatomical structure;

generating an augmented training example using the outlined and augmented portions;

repeating the steps of outlining, augmenting, and generating for a plurality of training images to train a statistical model of the structure;

accessing the digital image; and

segmenting the anatomical structure in the accessed digital image according to the statistical model.