Prediction of patient-specific structure based on statistical shape model

Patent metadata

Field	Value
Publication number	US-10043277-B2
Application number	US-201615218067-A
Country	US
Kind code	B2
Filing date	Jul 24, 2016
Priority date	Jan 27, 2014
Publication date	Aug 7, 2018
Grant date	Aug 7, 2018

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 anatomical object including bone and cartilage based on predicting a shape of the anatomical object, the system comprising:
a memory configured to store at least one database configured to store a first set of training models corresponding to bone models, a second set of training models corresponding to cartilage models, and a subject model corresponding to a bone model, wherein each model in the first set of training models corresponds to a model in the second set of training models, wherein the first set of training models and the subject model are characterized by a first parameter being bone, and wherein the second set of training models is characterized by a second parameter being cartilage;
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 anatomical object based on a predicted shape of the anatomical 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 claim 2 , wherein to generate the digital 3D model of the anatomical object based on the predicted shape comprises to generate the predicted shape by modifying the subject model.

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

5. A computer implemented method of generating a digital 3D model of an anatomical object including bone and cartilage based on predicting a shape of the anatomical object, the method comprising:
determining an average shape for a first set of training models corresponding to bone models;
determining a decomposition of the first set of training models;
determining an average shape for a second set of training models corresponding to cartilage models;
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, the first set of training models being characterized by a first parameter being bone; the second set of training models being characterized by a second parameter being cartilage;
determining a relationship between a subject model corresponding to a bone model and the first set of training models, the subject model being characterized by the first parameter; and
generating the digital 3D model of the anatomical object based on a predicted shape of the anatomical 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, the predicted shape being characterized by the second parameter.

6. The computer implemented method of claim 5 , wherein the first set of training models and the second set of training models are derived from MRI images, and wherein the subject model is derived from an x-ray image.

7. The computer implemented method of claim 5 , wherein determining the relationship between the subject model and the first set of training models comprises determining a fitting vector.

8. The computer implemented method of claim 7 , wherein generating the digital 3D model of the anatomical object based on the predicted shape comprises modifying the subject model.

9. The computer implemented method of claim 7 , wherein generating the digital 3D model of the anatomical object based on the predicted shape is based at least in part on the fitting vector.

10. The computer implemented method of claim 7 , further comprising identifying at least one region of the predicted shape having an accuracy above a threshold accuracy level.

11. 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 anatomical object including bone and cartilage base on predicting a shape of the anatomical object, the method comprising:
determining an average shape for a first set of training models corresponding to bone models;
determining a decomposition of the first set of training models;
determining an average shape for a second set of training models corresponding to cartilage models;
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, the first set of training models being characterized by a first parameter being bone; the second set of training models being characterized by a second parameter being cartilage;
determining a relationship between a subject model corresponding to a bone model and the first set of training models, the subject model being characterized by the first parameter; and
generating the digital 3D model of the anatomical object based on a predicted shape of the anatomical 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, the predicted shape being characterized by the second parameter.

Assignees

• Mat Nv

Inventors

• Maes Jan
• Cluckers Tom
• Van Deun Joris

Classifications

• G06F18/2148

  characterised by the process organisation or structure, e.g. boosting cascade · CPC title

• G16H50/50

  for simulation or modelling of medical disorders · CPC title

• G06T2207/30008

  Bone · CPC title

• G06T7/50

  Depth or shape recovery · CPC title

• G06T7/586

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