Segmenting irregular shapes in images using deep region growing

1. A method for identifying an object of interest in a medical image, the method comprising:

initializing internal states of nodes of a spatial lattice, wherein each node corresponds to a pixel of the medical image and is connected to at least one node representing a neighboring pixel of the medical image;

iteratively updating, using a neural network, the internal states of the nodes in the spatial lattice using spatially gated propagation, wherein at each iteration each node updates its internal state based on at least one selected from the group consisting of a value of the node from a previous iteration, a value of a neighboring node from the previous iteration, and a new value of the node; and

identifying the object of interest within the medical image based on the values of the nodes at a convergence of the spatial lattice.

2. The method according to claim 1 , wherein iteratively updating, using a neural network, the internal states of the nodes includes updating a value in a vector of values associated with the internal states of the nodes.

3. The method according to claim 2 , wherein the values in the vector of values include a value representing the brightness of the pixel corresponding to the node and a value representing the internal state of the node.

4. The method according to claim 1 , wherein a convolution involving previous internal states of the nodes is performed for each iteration.

5. The method according to claim 1 , wherein the method further includes performing, in a first iteration, convolutions on each value representing a brightness of each pixel.

6. The method according to claim 1 , wherein identifying an object of interest within the medical image based on the values of the nodes at a convergence of the spatial lattice includes using a final layer of the neural network to calculate a probability that each pixel is included in the object of interest based on a value included in a vector of values associated with each pixel; and

determining, for each pixel, if the calculated probability is above a predetermined threshold.

7. The method according to claim 1 , wherein each node updates its internal state based on at least one selected from the group consisting of a value of the node from a previous iteration, a value of a neighboring node from the previous iteration, and a new value of the node using a squashing function.

8. The method according to claim 1 , wherein the neighboring node is one selected from a group consisting of a node that represents a pixel that is directly above, directly below, to the right of, and to the left of a pixel represented by the node.

9. The method according to claim 1 , the method further comprising generating an image pyramid with a plurality of layers, wherein each successive layer represents the medical image with fewer values.

10. The method according to claim 9 , the method further comprising concatenating values from a plurality of layers of the image pyramid in each iteration.

11. A system for determining a region of interest in an image, the system comprising

a memory; and

an electronic processor, connected to the memory and configured to initialize internal states of nodes of a spatial lattice, wherein each node corresponds to a pixel of the image and is connected to at least one node representing a neighboring pixel of the image,

iteratively update, using a neural network, the internal states of each nodes in the spatial lattice using spatially gated propagation, wherein at each iteration the internal state of each node is updated; and

identify the region of interest within the image based on the internal states of the nodes at a convergence of the spatial lattice,

wherein the electronic processor is configured to update the internal states of the nodes by, at each iteration, updating the internal state based on at least one selected from the group consisting of a value of the node from a previous iteration, a value of a neighboring node from the previous iteration, or a new value of the node.

12. The system according to claim 11 , wherein the electronic processor is configured to iteratively update, using a neural network, the internal states of the nodes by updating a value in a vector of values associated with the internal states of the nodes.

13. The system according to claim 12 , wherein the values in the vector of values include a value representing the brightness of a pixel corresponding to the node and a value representing the internal state of the node.

14. The system according to claim 11 , wherein the electronic processor is further configured to perform, in each iteration, a convolution involving previous internal states of the nodes.

15. The system according to claim 11 , wherein the electronic processor is further configured to perform, in the first iteration, convolutions on each value representing a brightness of each pixel.

16. The system according to claim 11 , wherein the electronic processor is configured to identify an object of interest within the image based on the values of the nodes at a convergence of the spatial lattice by using a final layer of the neural network to calculate a probability that each pixel is included in the object of interest based on the vector associated with each pixel, and

determining, for each pixel, if the calculated probability is above a predetermined threshold.

17. The system according to claim 11 , wherein the electronic processor is configured to update the internal state based on at least one selected from the group consisting of a value of the node from a previous iteration, a value of a neighboring node from the previous iteration, or a new value of the node by using a squashing function.

18. The system according to claim 11 , wherein the neighboring node is one selected from a group consisting of a node that represents a pixel that is directly above, directly below, to the right of, and to the left of the pixel represented by the node.

19. Non-transitory computer-readable medium storing instructions that, when executed with an electronic processor, perform a set of functions, the set of functions comprising:

initializing internal states of nodes of a spatial lattice, wherein each node represents a pixel of an image and is connected to at least one neighboring pixel of the image;

iteratively updating, using a neural network, the internal states of the nodes in the spatial lattice using spatially gated propagation, wherein at each iteration each node updates its internal state based on at least one selected from the group consisting of a value of the node from a previous iteration, a value of a neighboring node from the previous iteration, or a new value of the node; and

identifying an object of interest within the image based on the values of the nodes at a convergence of the spatial lattice.

20. The non-transitory computer-readable medium according to claim 19 , wherein iteratively updating, using a neural network, the internal states of the nodes includes updating a value in a vector of values associated with the internal states of the nodes.

21. The non-transitory computer-readable medium according to claim 19 , wherein identifying an object of interest within the image based on the values of the nodes at a convergence of the spatial lattice includes using a final layer in the neural network to calculate a probability that each pixel is included in the object of interest based on the vector associated with each pixel; and determining, for each pixel, if the calculated probability is above a predetermined threshold.