Apparatus and method for computer-aided diagnosis

• US 9,639,929 B2
• Filed: 08/27/2014
• Issued: 05/02/2017
• Est. Priority Date: 10/24/2013
• Status: Active Grant

• Current Assignee: Samsung Electronics Co. Ltd.
• Original Assignee: Samsung Electronics Co. Ltd.
• Inventors: Kim, Ye-Hoon, Park, Moon-Ho, Seong, Yeong-Kyeong, Kim, Jung-Hoe, Cho, Baek-Hwan
• Primary Examiner(s): CHANG, JON CARLTON
• Application Number:
  US14/469,854
• Publication Number:
  US 20150117737A1
• Time in Patent Office: 979 Days
• Field of Search: 382131
• CPC Class Codes: A61B 5/0033 Features or image-related aspects of imaging apparatus classified in A61B5/00, e.g. for MRI, optical tomography or impedance tomography apparatus; arrangements of imaging apparatus in a room image data processing or generation G06T
• CPC Class Codes: G01R 33/4814 MR combined with ultrasound
• CPC Class Codes: G06F 2218/12 Classification; Matching
• CPC Class Codes: G06T 15/005 General purpose rendering architectures
• CPC Class Codes: G06T 15/08 Volume rendering
• CPC Class Codes: G06T 19/003 Navigation within 3D models or images
• CPC Class Codes: G06T 2200/04 involving 3D image data
• CPC Class Codes: G06T 2200/08 involving all processing steps from image acquisition to 3D model generation
• CPC Class Codes: G06T 2207/10072 Tomographic images
• CPC Class Codes: G06T 2207/10088 Magnetic resonance imaging [MRI]
• CPC Class Codes: G06T 2207/10136 3D ultrasound image
• CPC Class Codes: G06T 7/0012 Biomedical image inspection

Abstract

An apparatus and method for medical diagnostics includes receiving three-dimensional (3D) volume data of a part of a patient'"'"'s body, and generating two-dimensional (2D) slices including cross-sections of the 3D volume data cut from a cross-section cutting direction. The apparatus and the method also determine whether a lesion in each of the 2D slices is benign or malignant and output results indicative thereof, select a number of the 2D slices based on the results, and make a final determination whether the lesion is benign or malignant based on the selected 2D slices.

Claims

1. A method for medical diagnostics, comprising:

receiving three-dimensional (3D) volume data of at least a part of a body;

generating two-dimensional (2D) slices including cross-sections of the 3D volume data cut from a cross-section cutting direction;

outputting results of a determination of whether a lesion in at least two of the 2D slices is benign;

selecting a plurality of the at least two 2D slices based on the results; and

outputting a result of a final determination of whether a lesion included in the 3D volume data is benign based on the selected 2D slices.

2. The method of claim 1, wherein the outputting of the results of the determination comprises

applying the at least two 2D slices to a diagnostic model to determine whether the lesion in the at least two 2D slices is benign.

3. The method of claim 2, wherein the outputting of the results of the determination comprises

applying the at least two 2D slices to a single diagnostic model that is generated based on a cross-section cutting direction to determine whether the lesion in the at least two 2D slices is benign.

4. The method of claim 2, wherein the outputting of the results of the determination comprises

applying the at least two 2D slices to respective diagnostic models that are generated based on cross-section cutting directions of the at least two 2D slices to determine whether the lesion in the at least 2D slices is benign.

5. The method of claim 1, wherein the results comprise a classification result of whether the lesion in the at least two 2D slices is benign, and a confidence level of the classification result.

6. The method of claim 1, wherein the generating of the 2D slices comprises:

generating at least one virtual plane, and

generating the 2D slices including cross-sections of the 3D volume data cut by the virtual plane.

7. The method of claim 6, wherein the generating of the at least one virtual plane comprises generating the at least one virtual plane by changing coefficient values of a plane equation that represents an arbitrary plane of the 3D volume data.

8. The method of claim 6, wherein the generating of the at least one virtual plane comprises generating the at least one virtual plane by performing principal component analysis (PCA) on the 3D volume data.

9. The method of claim 8, wherein the generating of the at least one virtual plane further comprises:

determining feature points having a predetermined feature from among voxels of the 3D volume data based on values of the voxels, and

generating the at least one virtual plane based on a distribution of the feature points by performing the PCA.

10. The method of claim 8, wherein the generating of the at least one virtual plane further comprises:

calculating a first principal component vector corresponding to an axis in a direction in which a change in the 3D volume data is the greatest by performing the PCA, and

generating the at least one virtual plane with reference to the first principal component vector.

11. The method of claim 8, wherein the generating of the at least one virtual plane further comprises:

detecting a mass included in the 3D volume data based on values of voxels of the 3D volume data, and

generating the at least one virtual plane based on a distribution of points included in the mass by performing the PCA.

12. The method of claim 6, wherein the generating of the at least one virtual plane comprises generating the at least one virtual plane based on a user'"'"'s input information.

13. An apparatus for medical diagnostics, comprising:

a receiver configured to receive three-dimensional (3D) volume data of at least a part of a body; and

at least one processor configured to;

generate two-dimensional (2D) slices including cross sections of the 3D volume data cut from a cross-section cutting direction,

output results of a determination of whether a lesion in at least two of the 2D slices is benign,

select a plurality of the at least two 2D slices based on the results, and

output a result of a final determination of whether a lesion included in the 3D volume data is benign based on the selected 2D slices.

14. The apparatus of claim 13, wherein the at least one processor is further configured to determine whether the lesion in the at least two 2D slices is benign by applying the at least two 2D slices to a diagnostic model.

15. The apparatus of claim 14, wherein the at least one processor is configured to determine whether the lesion in the at least two 2D slices is benign by applying the at least two 2D slices to a single diagnostic model generated based on a cross-section cutting direction.

16. The apparatus of claim 14, wherein the at least one processor is configured to determine whether the lesion in the at least two 2D slices is benign by applying the at least two 2D slices to respective diagnostic models generated based on cross-section cutting directions of the at least two 2D slices.

17. The apparatus of claim 13, wherein the results comprise a classification result of whether the lesion in the at least two 2D slices is benign, and a confidence level of the classification result.

18. The apparatus of claim 13, wherein the at least one processor further comprises:

a virtual plane generator configured to generate at least one virtual plane, and

a 2D slice generator configured to generate 2D slices of cross-sections of the 3D volume data cut by the virtual plane.

19. The apparatus of claim 18, wherein the virtual plane generator is further configured to generate the at least one virtual plane by changing coefficients of a plane equation of an arbitrary plane of the 3D volume data.

20. The apparatus of claim 18, wherein the virtual plane generator is further configured to generate the at least one virtual plane by performing principal component analysis (PCA) on the 3D volume data.

21. The apparatus of claim 20, wherein the virtual plane generator is further configured to:

determine feature points with a predetermined feature from among voxels of the 3D volume data based on values of the voxels, and

generate the at least one virtual plane based on a distribution of the feature points.

22. The apparatus of claim 20, wherein the virtual plane generator is further configured to:

calculate a first principal component vector corresponding to an axis in a direction toward which a greatest change occurs in the 3D volume data by performing the PCA, and

generate the at least one virtual plane based on the first principal component vector.

23. The apparatus of claim 20, wherein the virtual plane generator is further configured to:

detect a mass included in the 3D volume data based on values of voxels of the 3D volume data, and

generate the at least one virtual plane based on a distribution of points included in the mass by performing the PCA.

24. The apparatus of claim 18, wherein the virtual plane generator is further configured to generate the at least one virtual plane based on a user'"'"'s input information.

25. The apparatus of claim 13, wherein the at least one processor is further configured to:

generate a feature vector using extracted features of the lesion in the at least two 2D slices, apply the feature vector to a diagnostic model to classify whether the lesion in the at least two 2D slices is benign, and calculate a confidence level of the classification result.

26. The apparatus of claim 13, wherein the at least processor is further configured to determine whether the lesion included in the 3D volume data is benign based on a classification result of the lesion in each of the selected 2D slices.

27. The apparatus of claim 13, wherein the at least one processor is further configured to:

bind two or more 2D slices into one group, compare confidence levels of the 2D slices in the group, and select one or more 2D slices in descending order of the confidence levels thereof.

28. The apparatus of claim 13, wherein the at least one processor is further configured to:

randomly bind the selected 2D slices into one group, compare confidence levels thereof, and select one or more 2D slices in descending order of the confidence levels thereof.