Optimal path finding based spinal center line extraction

Patent metadata

Field	Value
Publication number	US-2025200744-A1
Application number	US-202318845145-A
Country	US
Kind code	A1
Filing date	Mar 7, 2023
Priority date	Mar 11, 2022
Publication date	Jun 19, 2025
Grant date	—

Abstract

Method and apparatus for finding an optimal spinal canal path center line are disclosed. Imaging scans such as a Computer Tomography (CT) scan are used to diagnosis rib and spine fractures. The spine image is usually reformatted in a straightened CPR (curved planar reformat). A dynamic programming-based techniques to find an optimal path from a spinal canal segmentation is disclosed. The disclosed method and apparatus overcomes spinal canal segmentation errors resulting in spinal canal gaps and unrealistic image distortions. The disclosed techniques are not sensitive to segmentation errors even is the presence of high segmentation noise. The disclosed techniques generate a binary three-dimensional image corresponding to the scanned spine image, perform density estimation to create a three-dimensional having the same size as the original binary three-dimensional, perform dynamic programming based optimal spinal canal path finding with a coarse to fine optimal path finding strategy to accelerate the optimal spinal canal path finding.

First claim

1 . A method of generating an optimal spinal canal path, the method comprising:
receiving scan data as a three-dimensional array of a scanned spine image; executing a three-dimensional voxel wise spinal canal segmentation from the received scan data to generate a binary three-dimensional array corresponding to the scanned spine image; generating an inverse density three-dimensional array corresponding to the generated binary three-dimensional array having a size the same as the generated binary three-dimensional array; down sampling the inverse density three-dimensional array to a predetermined size; performing programming based optimal spinal canal path finding to generate an optimal spinal canal path; sampling the inverse three-dimensional array around the generated optimal spinal canal path with higher resolution and limited size; performing programming based optimal spinal canal path finding to generate a revised optimal spinal canal path; repeating the sampling and the programming based optimal spinal canal path finding by incrementally increasing the resolution and decreasing the size; determining whether an accuracy of the optimal spinal canal path satisfies a predetermined threshold; and generating a final optimal spinal canal path as a spinal canal center line, wherein a reformatted image is generated according to the final optimal spinal canal path as a spinal canal center line.

2 . The method of generating an optimal spinal canal path according to claim 1 , wherein spinal canal segmentation is performed using machine or deep learning techniques, wherein a value of 1 is assigned to each spinal canal voxel and a value of 0 is assigned to background in the generated binary three-dimensional array.

3 . The method of generating an optimal spinal canal path according to claim 2 , wherein generating the inverse density three-dimensional array includes assigning a float value to each voxel where the higher a density value of 1 voxels are in the binary three-dimensional array, the lower the float value assigned to a corresponding voxel in the inverse density three-dimensional array.

4 . The method of generating an optimal spinal canal path according to claim 1 , wherein the programming based optimal spinal canal path finding comprises:
creating a cost-pointer array that is a three-dimensional array corresponding to the inverse density three-dimensional array, wherein each element in the cost-pointer array comprises a cost component and a pointer component, wherein each cost component in the cost-pointer array corresponds to a voxel in a same position in the inverse density three-dimensional array, and wherein the pointer component is a three-dimensional vector which points to a physical coordinate in the binary three-dimensional array.

5 . The method of generating an optimal spinal canal path according to claim 1 , wherein the programming based optimal spinal canal path finding is dynamic programming based optimal spinal canal path finding.

6 . The method of generating an optimal spinal canal path according to claim 1 , wherein a density estimation is applied to generate the inverse density three-dimensional array.

7 . The method of generating an optimal spinal canal path according to claim 6 , wherein the density estimation is performed via kernel density estimation techniques, and wherein the kernel is at least one of gaussian, linear, cosine, uniform, epanechnikov, exponential, and tophat.

8 . An optimal path finding apparatus, comprising:
a memory configured to store computer executable instructions; and at least one processor configured to execute the computer executable instructions to cause the optimal path finding apparatus to:
receive scan data as a three-dimensional array of a scanned spine image;
execute a three-dimensional voxel wise spinal canal segmentation from the received scan data to generate a binary three-dimensional array corresponding to the scanned spine image;
generate an inverse density three-dimensional array corresponding to the generated binary three-dimensional array having a size the same as the generated binary three-dimensional array;
down sample the inverse density three-dimensional array to a predetermined size;
perform programming based optimal spinal canal path finding to generate an optimal spinal canal path;
sample the inverse three-dimensional array around the generated optimal spinal canal path with higher resolution and limited size;
perform programming based optimal spinal canal path finding to generate a revised optimal spinal canal path;
repeat the sampling and the programming based optimal spinal canal path finding by incrementally increasing the resolution and decreasing the size;
determine whether an accuracy of the optimal spinal canal path satisfies a predetermined threshold; and
generate a final optimal spinal canal path as a spinal canal center line, wherein a reformatted image is generated according to the final optimal spinal canal path as a spinal canal center line.

9 . The optimal path finding apparatus according to claim 8 , wherein the spinal canal segmentation is performed using machine or deep learning techniques, wherein a value of 1 is assigned to each spinal canal voxel and a value of 0 is assigned to background in the generated binary three-dimensional array.

10 . The optimal path finding apparatus according to claim 9 , wherein the processor is configured to assign a float value to each voxel in the generated inverse density three-dimensional array, where the higher a density value of 1 voxels are in the binary three-dimensional array, the lower the float value assigned to a corresponding voxel in the inverse density three-dimensional array

11 . The optimal path finding apparatus according to claim 8 , wherein the processor is further configured to create a cost-pointer array that is a three-dimensional array corresponding to the inverse density three-dimensional array, wherein each element in the cost-pointer array comprises a cost component and a pointer component, wherein each cost component in the cost-pointer array corresponds to a voxel in a same position in the inverse density three-dimensional array, and wherein the pointer component is a three-dimensional vector which points to a physical coordinate in the binary three-dimensional array.

12 . The optimal path finding apparatus according to claim 8 , wherein the processor is configured to perform the programming based optimal spinal canal path finding via dynamic programming techniques.

13 . The optimal path finding apparatus according to claim 8 , wherein the processor is configured to apply a density estimation to generate the inverse density three-dimensional array.

14 . The optimal path finding apparatus according to claim 13 , wherein the density estimation is performed via kernel density estimation techniques, and wherein the kernel is at least one of gaussian, linear, cosine, uniform, epanechnikov, exponential, and tophat.

15 . A non-transitory computer-readable medium having stored thereon instructions for causing processing circuitry to execute a process, the process comprising:
receiving scan data as a three-dimensional array of a scanned spine image; executing a three-dimensional voxel wise spinal canal segmentation from the received scan data to generate a binary three-dimensional array corresponding to the scanned spine image; generating an inverse density three-dimensional array corresponding to the generated binary three-dimensional array having a size the same as the generated binary three-dimensional array; down sampling the inverse densit

Assignees

• Koninklijke Philips Nv

Inventors

• Chen Hongxin
• Buerger Christian
• Klinder Tobias
• Lorenz Cristian

Classifications

• G06T2207/30012

  Spine; Backbone · CPC title

• G06T15/00

  Three-dimensional [3D] image rendering · CPC title

• A61B5/4566

  Evaluating the spine (A61B5/4561 takes precedence) · CPC title

• G16H30/40

  for processing medical images, e.g. editing · CPC title

• G16H30/20

  for handling medical images, e.g. DICOM, HL7 or PACS · CPC title