Probabilistic tree tracing and large vessel occlusion detection in medical imaging

Patent metadata

Field	Value
Publication number	US-12112470-B2
Application number	US-202117449263-A
Country	US
Kind code	B2
Filing date	Sep 29, 2021
Priority date	Sep 29, 2021
Publication date	Oct 8, 2024
Grant date	Oct 8, 2024

Abstract

Systems and methods for generating a probabilistic tree of vessels are provided. An input medical image of vessels of a patient is received. Anatomical landmarks are identified in the input medical image. A centerline of the vessels in the input medical image is determined based on the anatomical landmarks. A probabilistic tree of the vessels is generated based on a probability of fit of the anatomical landmarks and the centerline of the vessels. The probabilistic tree of the vessels is output.

First claim

The invention claimed is:

1. A method comprising:
receiving an input medical image of vessels of a patient;
identifying anatomical landmarks in the input medical image;
determining a centerline of the vessels in the input medical image based on the anatomical landmarks;
generating a probabilistic tree of the vessels based on a probability of fit of the anatomical landmarks and the centerline of the vessels; and
outputting the probabilistic tree of the vessels.

2. The method of claim 1 , wherein determining a centerline of the vessels in the input medical image based on the anatomical landmarks comprises generating a centerline probability map of the centerline of the vessels and wherein generating a probabilistic tree of the vessels based on a probability of fit of the anatomical landmarks and the centerline of the vessels comprises:
extracting centerline segments from the centerline probability map;
generating candidate mappings between the centerline segments and anatomical landmarks to the probabilistic tree;
calculating a score for each candidate mapping based on a probability of detection of the anatomical landmarks and the centerline segments and a probability of location of the anatomical landmarks and the centerline segments; and
selecting one of the candidate mappings based on the scores.

3. The method of claim 1 , wherein generating a probabilistic tree of the vessels based on a probability of fit of the anatomical landmarks and the centerline of the vessels comprises:
determining paths between the anatomical landmarks using RL (reinforcement learning) agents; and
generating the probabilistic tree based on a likelihood of the paths.

4. The method of claim 3 , wherein each of the RL agents is trained to navigate to a respective anatomical landmark based on geometric features and local image context features of the input medical image and wherein one or more of the RL agents share the geometric features and the local image context features with connected RL agents.

5. The method of claim 4 , further comprising:
determining an order for running the RL agents using a meta-agent based on a configuration of the RL agents.

6. The method of claim 1 , further comprising:
segmenting the vessels from the input medical image based on the centerline of the vessels; and
computing geometric indices of the vessels based on the segmented vessels.

7. The method of claim 1 , further comprising:
detecting a location of a large vessel occlusion by identifying discontinuities in the probabilistic tree that result in missing downstream segments.

8. The method of claim 1 , wherein identifying anatomical landmarks in the input medical image comprises:
identifying the anatomical landmarks in the input medical image using RL (reinforcement learning) agents, each of the RL agents is trained to navigate to a respective anatomical landmark based on geometric features and local image context features of the input medical image and wherein one or more of the RL agents share the geometric features and the local image context features with connected RL agents.

9. The method of claim 8 , wherein the geometric features comprise one or more of relative position, relative distance, relative angle, current agent location confidence, or embedded image intensity features.

10. An apparatus comprising:
means for receiving an input medical image of vessels of a patient;
means for identifying anatomical landmarks in the input medical image;
means for determining a centerline of the vessels in the input medical image based on the anatomical landmarks;
means for generating a probabilistic tree of the vessels based on a probability of fit of the anatomical landmarks and the centerline of the vessels; and
means for outputting the probabilistic tree of the vessels.

11. The apparatus of claim 10 , wherein the means for determining a centerline of the vessels in the input medical image based on the anatomical landmarks comprises generating a centerline probability map of the centerline of the vessels and wherein the means for generating a probabilistic tree of the vessels based on a probability of fit of the anatomical landmarks and the centerline of the vessels comprises:
means for extracting centerline segments from the centerline probability map;
means for generating candidate mappings between the centerline segments and anatomical landmarks to the probabilistic tree;
means for calculating a score for each candidate mapping based on a probability of detection of the anatomical landmarks and the centerline segments and a probability of location of the anatomical landmarks and the centerline segments; and
means for selecting one of the candidate mappings based on the scores.

12. The apparatus of claim 10 , wherein the means for generating a probabilistic tree of the vessels based on a probability of fit of the anatomical landmarks and the centerline of the vessels comprises:
means for determining paths between the anatomical landmarks using RL (reinforcement learning) agents; and
means for generating the probabilistic tree based on a likelihood of the paths.

13. The apparatus of claim 12 , wherein each of the RL agents is trained to navigate to a respective anatomical landmark based on geometric features and local image context features of the input medical image and wherein one or more of the RL agents share the geometric features and the local image context features with connected RL agents.

14. The apparatus of claim 13 , further comprising:
means for determining an order for running the RL agents using a meta-agent based on a configuration of the RL agents.

15. A non-transitory computer readable medium storing computer program instructions, the computer program instructions when executed by a processor cause the processor to perform operations comprising:
receiving an input medical image of vessels of a patient;
identifying anatomical landmarks in the input medical image;
determining a centerline of the vessels in the input medical image based on the anatomical landmarks;
generating a probabilistic tree of the vessels based on a probability of fit of the anatomical landmarks and the centerline of the vessels; and
outputting the probabilistic tree of the vessels.

16. The non-transitory computer readable medium of claim 15 , wherein determining a centerline of the vessels in the input medical image based on the anatomical landmarks comprises generating a centerline probability map of the centerline of the vessels and wherein generating a probabilistic tree of the vessels based on a probability of fit of the anatomical landmarks and the centerline of the vessels comprises:
extracting centerline segments from the centerline probability map;
generating candidate mappings between the centerline segments and anatomical landmarks to the probabilistic tree;
calculating a score for each candidate mapping based on a probability of detection of the anatomical landmarks and the centerline segments and a probability of location of the anatomical landmarks and the centerline segments; and
selecting one of the candidate mappings based on the scores.

17. The non-transitory computer readable medium of claim 15 , the operations further comprising:
segmenting the vessels from the input medical image based on the centerline of the vessels; and
computing geometric indices of the vessels based on the segmented vessels.

18. The non-transitory computer readable medium of claim 15 , the operations further comprising:
detecting a location of a large vessel o

Assignees

• Siemens Healthineers Ag

Inventors

• Georgescu Bogdan
• Gibson Eli
• Re Thomas
• Comaniciu Dorin
• Ghesu Florin-Cristian
• Singh Vivek

Classifications

• G06T2207/30204

  Marker · CPC title

• G06T2207/30101

  Blood vessel; Artery; Vein; Vascular · CPC title

• G06T2207/20081

  Training; Learning · CPC title

• G06T7/143

  involving probabilistic approaches, e.g. Markov random field [MRF] modelling · CPC title

• G06T2207/20084

  Artificial neural networks [ANN] · CPC title