Segmentation of medical images

1. A method for segmenting medical images comprising:

integrating image descriptors with a three-dimensional neural network, the image descriptors including a medical image, a Gibbs energy for a Markov-Gibbs random field model of the medical image, and an adaptive shape prior model of the medical image;

generating, using the three-dimensional neural network, probabilities for a goal region; and

designating, based on the generated probabilities, the goal region in the medical image.

2. The method of claim 1 , wherein the goal region is a region of the medical image corresponding to a biological feature of interest.

3. The method of claim 1 , wherein the Gibbs energy for the Markov-Gibbs random field model is a Gibbs energy for a 7th-order Markov-Gibbs random field model.

4. The method of claim 1 , wherein the Markov-Gibbs random field model is generated by using grayscale patterns of exemplary goal objects as samples of a trainable Markov-Gibbs random field.

5. The method of claim 4 , wherein the goal region is a region of the medical image corresponding to a biological feature of interest, and wherein the exemplary goal objects are the same biological feature.

6. The method of claim 1 , wherein the adaptive shape prior depends on a set of manually-segmented co-aligned subject images of a biological feature of interest corresponding to the goal region.

7. The method of claim 1 , wherein the medical image includes a plurality of voxels, and wherein the generating comprises generating probabilities that each voxel depicts a biological feature of interest.

8. The method of claim 1 , further comprising outputting a segmented medical image identifying the goal region.

9. A method for segmenting medical images comprising:

receiving a medical image including a plurality of voxels;

inputting into a three-dimensional neural network a plurality of image descriptors describing the medical image, wherein the plurality of image descriptors include a Gibbs energy for a Markov-Gibbs random field model, an adaptive shape prior model, and a first-order appearance model of the original volume to be segmented;

calculating, by the neural network, probabilities that each voxel represents a goal region in the medical image; and

segmenting, by the neural network, the medical image to identify the goal region.

10. The method of claim 9 , wherein the goal region is a region of the medical image corresponding to a biological feature of interest.

11. The method of claim 9 , wherein the first-order appearance model of the original volume to be segmented is the medical image.

12. The method of claim 9 , further comprising outputting a segmented medical image identifying the goal region.

13. The method of claim 9 , wherein the Markov-Gibbs random field model is generated by using grayscale patterns of exemplary goal objects as samples of a trainable Markov-Gibbs random field.

14. The method of claim 9 , wherein the adaptive shape prior model is generated by adapting a shape prior in accord with the medical image and a database of customized training images.

15. A method for segmenting a three-dimensional medical image, comprising:

receiving medical image data representing a three-dimensional medical image, the medical image data including a plurality of voxels;

integrating a plurality of image descriptors of the medical image using a three-dimensional neural network; and

outputting, by the neural network, segmentation data relating to the three-dimensional medical image, wherein the segmentation data is based on the plurality of integrated image descriptors;

wherein the plurality of image descriptors include the medical image and a Gibbs energy for a Markov-Gibbs random field model of the medical image.

16. The method of claim 15 , wherein the plurality of image descriptors further include an adaptive shape prior model.

17. The method of claim 15 , wherein the segmentation data identifies a goal region in the medical image.