Generation of a digital 3D model of an object based on predicting a shape of the object

Patent metadata

Field	Value
Publication number	US-10373328-B2
Application number	US-201816028303-A
Country	US
Kind code	B2
Filing date	Jul 5, 2018
Priority date	Jan 27, 2014
Publication date	Aug 6, 2019
Grant date	Aug 6, 2019

Abstract

Systems and methods for predicting shape are provided. A system for predicting shape can include a database, a training analysis module, a subject analysis module, and a prediction module. The database can store two sets of training models characterized by first and second parameters, respectively (e.g., bone and cartilage), as well as a subject model characterized by the first parameter (e.g., a bone model). The relationships between these models can be determined by a training analysis module and a subject module. Based on these relationships, the prediction module can generate a predicted shape characterized by the second parameter (e.g., a predicted cartilage model corresponding to the bone model).

First claim

What is claimed is:

1. A system for generating a digital 3D model of an object of an object type based on predicting a shape of the object, the system comprising:
a memory configured to store at least one database configured to store a first set of training models corresponding to 2D models of the object type, a second set of training models corresponding to 3D models of the object type, and a subject model corresponding to a 2D model corresponding to the object, wherein each 2D model in the first set of training models corresponds to a 3D model in the second set of training models;
a processor configured to:
determine an average shape for the first set of training models;
determine a decomposition of the first set of training models;
determine an average shape for the second set of training models;
determine a decomposition of the second set of training models;
determine a relationship between the first set of training models and the second set of training models as function of the average shape for the first set of training models, the decomposition of the first set of training models, the average shape for the second set of training models, and the decomposition of the second set of training models;
determine a relationship between the subject model and the first set of training models; and
generate the digital 3D model of the object based on a predicted shape of the object based on the relationship between the first set of training models and the second set of training models and on the relationship between the subject model and the first set of training models.

2. The system of claim 1 , wherein to determine the relationship between the subject model and the first set of training models comprises to determine a fitting vector.

3. The system of claim 2 , wherein to generate the digital 3D model of the object based on the predicted shape comprises to generate the predicted shape based at least in part on the fitting vector.

4. The system claim 1 , wherein to generate the digital 3D model of the object based on the predicted shape comprises to generate the predicted shape by modifying the subject model.

5. The system of claim 1 , wherein the object type comprises anatomy of a patient.

6. A computer implemented method of generating a digital 3D model of an object of an object type based on predicting a shape of the object, the method comprising:
determining an average shape for a first set of training models comprising 2D models of the object type;
determining a decomposition of the first set of training models;
determining an average shape for a second set of training models comprising 3D models of the object type;
determining a decomposition of the second set of training models;
determining a relationship between the first set of training models and the second set of training models as a function of the average shape for the first set of training models, the decomposition of the first set of training models, the average shape for the second set of training models, and the decomposition of the second set of training models, each model in the first set of training models corresponding to a model in the second set of training models;
determining a relationship between a subject model corresponding to a 2D model corresponding to the object and the first set of training models; and
generating the digital 3D model of the object based on a predicted shape of the object based on the relationship between the first set of training models and the second set of training models and on the relationship between the subject model and the first set of training models.

7. The computer implemented method of claim 6 , wherein the first set of training models are derived from x-ray images, and wherein the subject model is derived from an x-ray image.

8. The computer implemented method of claim 7 , wherein the second set of training models are derived from one of CT scans and MRI scans.

9. The computer implemented method of claim 6 , wherein the second set of training models are derived from one of CT scans and MRI scans.

10. The computer implemented method of claim 6 , wherein determining the relationship between the subject model and the first set of training models comprises determining a fitting vector.

11. The computer implemented method of claim 10 , wherein generating the digital 3D model of the object based on the predicted shape is based at least in part on the fitting vector.

12. The computer implemented method of claim 6 , wherein generating the digital 3D model of the object based on the predicted shape comprises modifying the subject model.

13. A non-transitory computer readable medium comprising computer executable instructions stored thereon which when executed by a processor cause a computer to perform a method of generating a digital 3D model of an object of an object type based on predicting a shape of the object, the method comprising:
determining an average shape for a first set of training models comprising 2D models of the object type;
determining a decomposition of the first set of training models;
determining an average shape for a second set of training models comprising 3D models of the object type;
determining a decomposition of the second set of training models;
determining a relationship between the first set of training models and the second set of training models as a function of the average shape for the first set of training models, the decomposition of the first set of training models, the average shape for the second set of training models, and the decomposition of the second set of training models, each model in the first set of training models corresponding to a model in the second set of training models;
determining a relationship between a subject model and the first set of training models, the subject model being a 2D model corresponding to the object; and
generating the digital 3D model of the object based on a predicted shape of the object based on the relationship between the first set of training models and the second set of training models and on the relationship between the subject model and the first set of training models.

14. The non-transitory computer readable medium of claim 13 , wherein the first set of training models are derived from x-ray images, and wherein the subject model is derived from an x-ray image.

15. The non-transitory computer readable medium of claim 14 , wherein the second set of training models are derived from one of CT scans and MRI scans.

16. The non-transitory computer readable medium of claim 13 , wherein the second set of training models are derived from one of CT scans and MRI scans.

17. The non-transitory computer readable medium of claim 13 , wherein determining the relationship between the subject model and the first set of training models comprises determining a fitting vector.

18. The non-transitory computer readable medium of claim 17 , wherein generating the digital 3D model of the object based on the predicted shape is based at least in part on the fitting vector.

19. The non-transitory computer readable medium of claim 13 , wherein generating the digital 3D model of the object based on the predicted shape comprises modifying the subject model.

Assignees

• Mat Nv
• Mat Nv

Inventors

• Maes Jan
• Cluckers Tom
• Van Deun Joris

Classifications

• G06T2210/41

  Medical · CPC title

• G06T19/20

  Editing of three-dimensional [3D] images, e.g. changing shapes or colours, aligning objects or positioning parts · CPC title

• G06T17/20

  Finite element generation, e.g. wire-frame surface description, {tesselation} · CPC title

• G06T7/586

  from multiple light sources, e.g. photometric stereo · CPC title

• G06T7/50

  Depth or shape recovery · CPC title