Method and apparatus for registration of different mammography image views

1. A method of identifying potential lesions in mammographic images, the method comprising:

receiving, by an image processing device, first image data;

receiving, by the image processing device, second image data, one of the first image data or the second image data being two-dimensional Craniocaudal (CC) mammographic image data or two-dimensional Mediolateral Oblique (MLO) mammographic image data;

registering, by the image processing device, the first image data and the second image data by employing an image registration convolutional neural network (CNN) using pixel level registration; wherein registering the first image data with the second image data comprises:

inputting the first image data and the second image data into the image registration CNN;

generating, via convolutions performed by the image registration CNN on the first image data and the second image data, a deformation field of deformation vectors that map pixels of the first image data to pixels of the second image data; the deformation field comprising, to define the deformation vectors,

a vertical deformation data array that defines row-wise relationships between the pixels of the first image data and the pixels of the second image data and

a horizontal deformation data array that defines column-wise relationships between the pixels of the first image data and the pixels of the second image data;

determining, by the image processing device, whether a candidate detection of a lesion exists in both the first image data and the second image data based on the first image data and the second image data and a mapping of the first image data to the second image data provided by deformation field output from the image registration CNN; and

generating, by the image processing device, display output identifying the lesion.

2. The method of claim 1 , wherein the first image data is two-dimensional Craniocaudal (CC) mammographic image data and the second image data is two-dimensional Mediolateral Oblique (MLO) mammographic image data.

3. The method of claim 2 , wherein generating the deformation field comprises:

learning a mapping from a first image of the first image data to a second image of the second image data via the CNN; and

generating a warped image output based on the mapping.

4. The method of claim 3 , wherein determining whether the candidate detection exists in both the first image data and the second image data comprises analyzing the warped image output for correlated features associated with the candidate detection, and

wherein generating the display output comprises generating an object identification on the warped image.

5. The method of claim 3 , wherein the CNN is a fully convolutional network,

wherein the CNN does not include any fully connected layer, and

wherein the first image data and the second image data are input to the CNN in separate channels such that an output of the CNN comprises two channels defining the vertical deformation data array and the horizontal deformation data array.

6. The method of claim 3 , wherein the CNN comprises a skip architecture including one or more skip paths, and

wherein each of the one or more skip paths is implemented to generate the deformation field.

7. The method of claim 2 , further comprising performing object level registration, wherein performing object level registration comprises:

employing a first stage CNN architecture configured to independently analyze the first image data and the second image data to identify candidate regions;

conducting pairwise evaluation of the candidate regions to determine whether the candidate detection exists; and

employing a second stage CNN architecture configured to determine candidate matches and generate object identification based on the pairwise evaluation.

8. The method of claim 7 , wherein employing the first stage CNN architecture comprises:

providing a plurality of first images associated with the first image data to a first region-based CNN (R-CNN) trained to identify first candidate regions; and

providing a plurality of second images associated with the second image data to a second R-CNN trained to identify second candidate regions,

wherein the first and second candidate regions comprise the candidate regions on which the pairwise evaluation is conducted.

9. The method of claim 8 , wherein employing the second stage CNN architecture comprises providing the second stage CNN with data associated with the first candidate regions and the second candidate regions, and distance from nipple information for each instance of the candidate detection in the data associated with the first candidate regions and the second candidate regions.

10. The method of claim 9 , wherein generating the display output comprises generating an object identification on both the first images and the second images.

11. The method of claim 2 , further comprising utilizing both the pixel level registration to generate a first object identification and an object level registration to generate a second object identification, and comparing the first and second object identifications.

12. A method of identifying potential lesions in mammographic images, the method comprising:

receiving, by an image processing device, first image data of a first type;

receiving, by the image processing device, second image data of a second type;

learning, by the image processing device, a mapping from a first image of the first image data to a second image of the second image data by employing an image registration convolutional neural network (CNN) to perform pixel level registration between the first image data and the second image data, wherein registering the first image data with the second image data comprises:

inputting the first image data and the second image data into the image registration CNN;

generating, via convolutions performed by the image registration CNN on the first image data and the second image data, a deformation field of deformation vectors that map pixels of the first image data to pixels of the second image data; the deformation field comprising, to define the deformation vectors,

a vertical deformation data array that defines row-wise relationships between the pixels of the first image data and the pixels of the second image data and

a horizontal deformation data array that defines column-wise relationships between the pixels of the first image data and the pixels of the second image data;

generating, by the image processing device as an output of the image registration CNN based on the deformation field, a warped image output of the first image data mapped to the second image data using the deformation field;

determining, by the image processing device, whether a candidate detection of a lesion exists in both the first image data and the second image data based on the warped image; and

generating, by the image processing device, display output identifying the lesion.

13. The method of claim 12 , wherein the first image data is two-dimensional Craniocaudal (CC) mammographic image data and the second image data is two-dimensional Mediolateral Oblique (MLO) mammographic image data.

14. The method of claim 13 , wherein determining whether the candidate detection exists in both the first image data and the second image data comprises analyzing the warped image output for correlated features associated with the candidate detection, and

wherein generating the display output comprises generating an object identification on the warped image.

15. The method of claim 13 , wherein the CNN is a fully convolutional network,

wherein the CNN does not include any fully connected layer, and

wherein the first image data and the second image data are input to the CNN in separate channels such that an output of the CNN comprises two channels defining the vertical deformation data array and the horizontal deformation data array.

16. The method of claim 13 , wherein the CNN comprises a skip architecture including one or more skip paths, and

wherein each of the one or more skip paths is implemented to generate the deformation field.

17. A method of identifying potential lesions in mammographic images, the method comprising:

receiving, by an image processing device, first image data of a first type;

receiving, by the image processing device, second image data of a second type;

identifying, by the image processing device, candidate regions via a first stage convolutional neural network (CNN) architecture configured to independently analyze the first image data using a first region-based convolutional neural network (R-CNN) to identify first candidate regions and independently analyze the second image data using a second R-CNN to identify second candidate regions, and perform object level registration of the first candidate regions with the second candidate regions candidate regions;

conducting, by the image processing device, pairwise evaluation of the first candidate regions with the second candidate regions to determine sets of pair candidates; and

determining, by the image processing device, candidate matches via a second stage CNN architecture comprising a second stage CNN that is applied to the sets of pair candidates, and generating a display output illustrating the candidate matches, wherein the second stage CNN is independent from the first R-CNN and the second R-CNN.

18. The method of claim 17 , wherein the first image data is two-dimensional Craniocaudal (CC) mammographic image data and the second image data is two-dimensional Mediolateral Oblique (MLO) mammographic image data.

19. The method of claim 18 , wherein employing the second stage CNN architecture comprises providing the second stage CNN architecture with data associated with the first candidate regions and the second candidate regions, and distance from nipple information for each instance of the sets of pair candidates in the data associated with the first candidate regions and the second candidate regions.

20. The method of claim 19 , wherein generating the display output comprises generating an object identification on both the first images and the second images.