Sparse Volume Segmentation for 3D Scans

• US 20110123095A1
• Filed: 05/29/2007
• Published: 05/26/2011
• Est. Priority Date: 06/09/2006
• Status: Active Grant

• Current Assignee: Siemens Medical Solutions USA Incorporated (Siemens AG)
• Original Assignee: Siemens Corporate Research Incorporated (Siemens AG)
• Inventors: Williams, James, Funka-Lea, Gareth, Paragios, Nikolaos, Florin, Charles
• Granted Patent: US 8,073,252 B2
• Time in Patent Office: Days
• US Class Current: 382/154
• CPC Class Codes: G06T 2207/10072 Tomographic images
• CPC Class Codes: G06T 2207/20081 Training; Learning
• CPC Class Codes: G06T 2207/30056 Liver; Hepatic
• CPC Class Codes: G06T 7/12 Edge-based segmentation
• CPC Class Codes: G06T 7/143 involving probabilistic approaches, e.g. Markov random field [MRF] modelling
• CPC Class Codes: G06T 7/149 involving deformable models, e.g. active contour models

Abstract

A computer readable medium is provided embodying instructions executable by a processor to perform a method for sparse volume segmentation for 3D scan of a target. The method including learning prior knowledge, providing volume data comprising the target, selecting a plurality of key contours of the image of the target, building a 3D spare model of the image of the target given the plurality of key contours, segmenting the image of the target given the 3D sparse model, and outputting a segmentation of the image of the target.

Claims

1. A computer readable medium embodying instructions executable by a processor to perform a method for sparse volume segmentation for 3D scan of a target, the method comprising:

learning prior knowledge;

providing volume data comprising the target;

selecting a plurality of key contours of the volume data;

building a 3D spare model of the volume data given the plurality of key contours;

segmenting the volume data using the 3D sparse model; and

outputting a representation of the volume data based on the segmentation using the 3D sparse model.

2. The method of claim 1, wherein learning prior knowledge includes learning variability to parameterize the contours in a reduced dimension space.

3. The method of claim 1, wherein selecting the key contours includes determining key contour indices so that a squared distance between an interpolated volume and the target is minimized.

4. The method of claim 1, wherein the 3D sparse model includes key indices, the key contours at the key indices, an interpolation operator, and an interpolated volume.

5. The method of claim 4, wherein segmenting the volume data comprises:

setting a globally register of the volume data to the 3D sparse model in a model space;

segmenting the plurality of key contours at the key indices; and

inferring a segmentation of a portion of the volume data from the plurality of key contours.

6. The method of claim 5, further comprising adjusting, locally, an interpolated volume to the volume data.

7. The method of claim 1, wherein the plurality of key contours are 2D contours.

8. A computer readable medium embodying instructions executable by a processor to perform a method for sparse volume segmentation for 3D scan of a target, the method comprising:

providing a 3D sparse model of a volume data including the target comprising key indices, key contours at the key indices, an interpolation operator, and an interpolated volume;

setting a globally register of the volume data to the 3D sparse model in a model space;

segmenting the plurality of key contours at the key indices;

inferring a segmentation of a portion of the volume data from the plurality of key contours; and

outputting a representation of the volume including the plurality of key contours and the portion of the volume data inferred from the plurality of key contours.

9. The method of claim 8, further comprising adjusting, locally, an interpolated volume to the volume.

10. A computer system comprising:

a processor; and

a computer readable medium embodying instructions executable by the processor to perform a method for sparse volume segmentation for 3D scan of a target, the method comprising;

learning prior knowledge;

providing volume data comprising the target;

selecting a plurality of 2D key contours of the volume data;

building a 3D spare model of the volume data comprising the plurality of 2D key contours;

segmenting volume data using the 3D sparse model, wherein a segmentation of a portion of the volume data is inferred from the 3D sparse model; and

outputting to a memory device a representation of the volume based on the segmentation using the 3D sparse model.

11. The system of claim 10, wherein the 3D sparse model includes key indices, the plurality of 2D key contours at the key indices, an interpolation operator, and an interpolated volume.