Method and system for automatic prostate segmentation in magnetic resonance images

Patent metadata

Field	Value
Publication number	US-9269156-B2
Application number	US-201313947300-A
Country	US
Kind code	B2
Filing date	Jul 22, 2013
Priority date	Jul 24, 2012
Publication date	Feb 23, 2016
Grant date	Feb 23, 2016

Abstract

A method and system for fully automatic segmentation the prostate in magnetic resonance (MR) image data is disclosed. Intensity normalization is performed on an MR image of a patient to adjust for global contrast changes between the MR image and other MR scans and to adjust for intensity variation within the MR image due to an endorectal coil used to acquire the MR image. An initial prostate segmentation in the MR image is obtained by aligning a learned statistical shape model of the prostate to the MR image using marginal space learning (MSL). The initial prostate segmentation is refined using one or more trained boundary classifiers.

First claim

The invention claimed is:

1. A method for automatic prostate segmentation in magnetic resonance (MR) image data, comprising:
performing a first intensity normalization on the MR image to adjust for global contrast changes between the MR image and other MR scans;
performing a second intensity normalization on the MR image to adjust for intensity variation within the MR image due to an endorectal coil used to acquire the MR image by:
obtaining a mask image from the MR image using intensity thresholding,
extracting a bright region from the MR image using the mask image, and
calculating adjusted intensities to reduce an overall intensity within the bright region such that the adjusted intensities at a boundary of the bright region match a surrounding region in the MR image and gradient features within the bright region are retained;

obtaining an initial prostate segmentation in the MR image by aligning a learned statistical shape model of the prostate to the MR image using marginal space learning (MSL); and
refining the initial prostate segmentation using one or more trained boundary classifiers.

2. The method of claim 1 , wherein the MR image is a 3D MR image resulting from a T2-weighted axial abdominal MR scan.

3. The method of claim 1 , wherein performing the first intensity normalization on the MR image to adjust for global contrast changes between the MR image and other MR scans comprises:
calculating a linear transformation of the MR image that minimizes a least square error between at least a portion of intensity distributions of a stored target image and the MR image; and
adjusting intensities of the MR image using the linear transformation.

4. The method of claim 3 , wherein:
calculating the linear transformation of the MR image that minimizes the least square error between at least the portion of intensity distributions of the stored target image and the MR image comprises calculating the linear transformation as:

a
,

b
=

arg
⁢

⁢

min

a
,
b

⁢

∑

j
=
3

98

⁢

(

prctile
⁡

(

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j

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prctile
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,

where Î is the stored target image, I i is the MR image, prctile(Î, j) is the j th percentile of the intensities of the stored target image and prctile(I i , j) is the j th percentile of the intensities of the MR image; and
adjusting intensities of the MR image using the linear transformation comprises generating an adjusted image as: I i ′=I i a+b.

5. The method of claim 1 , wherein performing the second intensity normalization on the MR image to adjust for intensity variation within the MR image due to the endorectal coil used to acquire the MR image comprises:
obtaining the mask image as M=((I>τ 1 )⊕B) (I>τ 2 ), where I is the MR image, τ 1 and τ 2 are intensity thresholds, τ 1 >τ 2 , and ⊕B is a dilation with a circular ball;
extracting the bright region Ω R 2 from the MR image Ω R 2 , as the non-zero elements of the mask image M;
generating a high pass filtered image as g(x)=(I−G σ *I)(x), where G σ is a Gaussian function; and
calculating adjusted intensities within the bright region f: Ω R as E(f)=min∫ Ω |∇f−∇g| 2 dx where f=I on δΩ by solving a Poisson equation: ∇ 2 f=∇ 2 g.

6. The method of claim 1 , wherein the statistical shape model of the prostate represents the shape of the prostate as a linear combination of a mean prostate shape and a number of strongest shape modes each weighted by a respective shape coefficient.

7. The method of claim 6 , wherein the mean prostate shape and the number of strongest shape modes are learned from a set of annotated training images.

8. The method of claim 6 , wherein obtaining the initial prostate segmentation in the MR image by aligning the learned statistical shape model of the prostate to the MR image using marginal space learning (MSL) comprises:
detecting position candidates for the prostate in the MR image using a first discriminative classifier;
detecting position-orientation candidates for the prostate in the MR image based on the detected position candidates using a second discriminative classifier;
detecting position-orientation-scale candidates for the prostate in the MR image based on the detected position-orientation candidates using a third discriminative classifier, wherein each position-orientation-scale candidate defines a candidate bounding box for aligning the mean prostate shape to the MR image; and
detecting the respective shape coefficient for each of the number of strongest shape modes based on the detected position-orientation-scale candidates using a fourth discriminative classifier.

9. The method of claim 8 , wherein the number of strongest shape modes comprises three strongest shape modes.

10. The method of claim 1 , wherein refining the initial prostate segmentation using one or more trained boundary classifiers comprises:
refining a prostate surface mesh resulting from the initial prostate segmentation by calculating a displacement for each of a plurality of vertices of the mes

Assignees

• Birkbeck Neil
• Zhang Jingdan
• Requardt Martin
• Kiefer Berthold
• Gall Peter
• Zhou Shaohua Kevin
• Siemens Ag

Inventors

• Birkbeck Neil
• Zhang Jingdan
• Requardt Martin
• Kiefer Berthold
• Gall Peter
• Zhou Shaohua Kevin

Classifications

• G06T5/009

  Physics · mapped topic

• G06T2207/20016

  Hierarchical, coarse-to-fine, multiscale or multiresolution image processing; Pyramid transform · CPC title

• G06T2200/04

  involving 3D image data · CPC title

• G06T7/0089

  Physics · mapped topic

• G06T2207/20081

  Training; Learning · CPC title