Automatic Multi-label Segmentation Of Abdominal Images Using Non-Rigid Registration

1 . A method for segmenting an anatomical image, comprising:

receiving a patient anatomical image;

receiving a baseline anatomical image having pre-segmented labels, wherein the pre-segmented labels identify regions of interest in the baseline anatomical image;

aligning the patient anatomical image with the baseline anatomical image to produce a transformation that when applied to the pre-segmented labels roughly identifies regions of interest in the patient anatomical image that correspond to the regions of interest in the baseline anatomical image; and

updating the pre-segmented labels, which have been deformed by application of the transformation, with a new transformation that minimizes the likelihood of intensity distributions within the regions of interest of the patient anatomical image to produce a gradient image that better identifies the regions of interest of the patient anatomical image.

2 . The method of claim 1 , further comprising computing the new transformation, wherein computing the new transformation comprises:

computing a gradient for all the regions of interest of the patient anatomical image;

regularizing the gradient; and

generating the new transformation by using the regularized gradient.

3 . The method of claim 1 , wherein the new transformation is applied to the deformed pre-segmented labels by computing a composition of the deformed pre-segmented labels and the new transformation.

4 . The method of claim 2 , wherein computing the gradient for all the regions of interest of the patient anatomical image comprises:

(1) for a region of interest of the patient anatomical image,

computing a temporary image for the region of interest;

computing an intensity distribution for the region of interest; and

computing a gradient for the region of interest;

(2) updating the gradient image with the gradient for the region of the interest; and

repeating (1) and (2) until the gradient image has been updated with a gradient for all the regions of interest of the patient anatomical image.

5 . The method of claim 1 , wherein the pre-segmented labels are repeatedly updated with new transformations until all the regions of interest of the patient anatomical image are better identified.

6 . The method of claim 1 , wherein the patient anatomical image comprises an abdomen.

7 . The method of claim 1 , wherein the patient anatomical image is a computed tomography (CT) image.

8 . A system for segmenting an anatomical image, comprising:

a memory device for storing a program:

a processor in communication with the memory device, the processor operative with the program to:

receive a patient anatomical image;

receive a baseline anatomical image having pre-segmented labels, wherein the pre-segmented labels identify regions of interest in the baseline anatomical image;

align the patient anatomical image with the baseline anatomical image to produce a transformation that when applied to the pre-segmented labels roughly identifies regions of interest in the patient anatomical image that correspond to the regions of interest in the baseline anatomical image; and

update the pre-segmented labels, which have been deformed by application of the transformation, with a new transformation that minimizes the likelihood of intensity distributions within the regions of interest of the patient anatomical image to produce a gradient image that better identifies the regions of interest of the patient anatomical image.

9 . The system of claim 8 , wherein the processor is further operative with the program to compute the new transformation, wherein when computing the new transformation the processor is further operative with the program to:

compute a gradient for all the regions of interest of the patient anatomical image;

regularize the gradient; and

generate the new transformation by using the regularized gradient.

10 . The system of claim 8 , wherein the new transformation is applied to the deformed pre-segmented labels by computing a composition of the deformed pre-segmented labels and the new transformation.

11 . The system of claim 9 , wherein when computing the gradient for all the regions of interest of the patient anatomical image the processor is further operative with the program to:

(1) for a region of interest of the patient anatomical image,

compute a temporary image for the region of interest;

compute an intensity distribution for the region of interest; and

compute a gradient for the region of interest;

(2) update the gradient image with the gradient for the region of the interest; and

repeat (1) and (2) until the gradient image has been updated with a gradient for all the regions of interest of the patient anatomical image.

12 . The system of claim 8 , wherein the pre-segmented labels are repeatedly updated with new transformations until all the regions of interest of the patient anatomical image are better identified.

13 . The system of claim 8 , wherein the patient anatomical image comprises an abdomen.

14 . The system of claim 8 , wherein the patient anatomical image is a computed tomography (CT) image.

15 . A computer readable medium tangibly embodying a program of instructions executable by a processor to perform method steps for segmenting an anatomical image, the method steps comprising:

receiving a patient anatomical image;

receiving a baseline anatomical image having pre-segmented labels, wherein the pre-segmented labels identify regions of interest in the baseline anatomical image;

aligning the patient anatomical image with the baseline anatomical image to produce a transformation that when applied to the pre-segmented labels roughly identifies regions of interest in the patient anatomical image that correspond to the regions of interest in the baseline anatomical image; and

updating the pre-segmented labels, which have been deformed by application of the transformation, with a new transformation that minimizes the likelihood of intensity distributions within the regions of interest of the patient anatomical image to produce a gradient image that better identifies the regions of interest of the patient anatomical image.

16 . The computer readable medium of claim 15 , the method steps further comprising computing the new transformation, wherein computing the new transformation comprises:

computing a gradient for all the regions of interest of the patient anatomical image;

regularizing the gradient; and

generating the new transformation by using the regularized gradient.

17 . The computer readable medium of claim 15 , wherein the new transformation is applied to the deformed pre-segmented labels by computing a composition of the deformed pre-segmented labels and the new transformation.

18 . The computer readable medium of claim 16 , wherein computing the gradient for all the regions of interest of the patient anatomical image comprises:

(1) for a region of interest of the patient anatomical image,

computing a temporary image for the region of interest;

computing an intensity distribution for the region of interest; and

computing a gradient for the region of interest;

(2) updating the gradient image with the gradient for the region of the interest; and

repeating (1) and (2) until the gradient image has been updated with a gradient for all the regions of interest of the patient anatomical image.

19 . The computer readable medium of claim 15 , wherein the pre-segmented labels are repeatedly updated with new transformations until all the regions of interest of the patient anatomical image are better identified.

20 . The computer readable medium of claim 15 , wherein the patient anatomical image comprises an abdomen.

21 . The computer readable medium of claim 15 , wherein the patient anatomical image is a computed tomography (CT) image.