System and method for an automated parsing pipeline for anatomical localization and condition classification

• US 10,991,091 B2
• Filed: 10/30/2018
• Issued: 04/27/2021
• Est. Priority Date: 10/30/2018
• Status: Active Grant

• Current Assignee: Diagnocat, Inc.
• Original Assignee: Diagnocat, Inc.
• Inventors: Ezhov, Matvey Dmitrievich, Aleksandrovskiy, Vladimir Leonidovich, Shumilov, Evgeny Sergeevich
• Primary Examiner(s): Huynh, Van D
• Application Number:
  US16/175,067
• Publication Number:
  US 20200134815A1
• Time in Patent Office: 910 Days
• Field of Search: 382224
• CPC Class Codes: G06F 2218/02 Preprocessing
• CPC Class Codes: G06F 2218/12 Classification; Matching
• CPC Class Codes: G06F 2218/22 Source localisation; Inverse modelling
• CPC Class Codes: G06N 3/044 Recurrent networks, e.g. Hopfield networks
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06N 3/048 Activation functions
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06N 3/082 modifying the architecture, e.g. adding, deleting or silencing nodes or connections
• CPC Class Codes: G06T 1/20 Processor architectures; Processor configuration, e.g. pipelining
• CPC Class Codes: G06T 2207/30036 Dental; Teeth
• CPC Class Codes: G06T 3/4007 based on interpolation, e.g. bilinear interpolation image demosaicing G06T3/4015; edge-driven or edge-based scaling G06T3/403
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06V 10/764 using classification, e.g. of video objects
• CPC Class Codes: G06V 10/82 using neural networks
• CPC Class Codes: G06V 20/64 Three-dimensional objects
• CPC Class Codes: G06V 2201/03 Recognition of patterns in medical or anatomical images
• CPC Class Codes: G16H 30/40 for processing medical images, e.g. editing
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

An automated parsing pipeline system and method for anatomical localization and condition classification is disclosed. The system comprises an input even source, a memory unit and processor including a volumetric image processor, a voxel parsing engine, localization layer and a detection module. The volumetric image processor is configured to receive volumetric image from the input source and parse the received volumetric image. The voxel parsing engine is configured to assign each voxel a distant anatomical structure. The localization layer is configured to crop a defined anatomical structure with surroundings. The detection module is configured to classify conditions for each defined anatomical structure within the cropped image. The disclosed system and method provide accurate localization of a tooth and detects several common conditions in each tooth.

Claims

1. An automated parsing pipeline system for anatomical localization and condition classification, said system comprising:

a processor;

a non-transitory storage element coupled to the processor;

encoded instructions stored in the nor-transitory storage element, wherein the encoded instructions when implemented by the processor, configure the automated parsing pipeline system to;

receive at least one volumetric image;

parse the received volumetric image into at least a single image frame field of view;

localize a present tooth inside the parsed volumetric image and identifying it by number;

extract the identified tooth and surrounding context within the localized volumetric image; and

classify a tooth'"'"'s conditions based on the extracted volumetric image using at least one of a multi-task approach, one network per condition approach, or a sub-network approach, wherein the multi-task approach is a single network outputting a prediction for multiple tooth conditions, the one network per condition approach is a single network outputting a prediction for a single tooth condition, and the sub-network approach is multiple networks outputting predictions for multiple tooth conditions.

2. The system of claim 1, wherein the at least one received volumetric image comprises a 3-D pixel array.

3. The system of claim 2, further configured to pre-process by converting the 3-D pixel array into an array of Hounsfield Unit (HU) radio intensity measurements.

4. The system of claim 1, further configured to pre-process at least one of the localization or classification steps by rescaling using linear interpolation.

5. The system of claim 1, wherein the pre-processing comprises using any one of a normalization schemes to account for variations in image value intensity depending on at least one of an input or output of volumetric image.

6. The system of claim 1, wherein the localization is achieved using a V-Net-based fully convolutional neural network.

7. The system of claim 1, further configured to extract anatomical structure by finding a minimum bounding rectangle around the localized and identified tooth.

8. The system of claim 7, wherein the bounding rectangle extends by at least 15 mm vertically and 8 mm horizontally (equally in all directions) to capture the tooth and surrounding context.

9. The system of claim 1, wherein the classification is achieved using a DenseNet 3-D convolutional neural network.

10. A method for localizing a tooth and classifying a tooth condition, said method comprising the steps of:

receiving at least one volumetric image;

parsing the received volumetric image into at least a single image frame field of view;

localizing a present tooth inside the parsed volumetric image and identifying it by number;

extracting the identified tooth and surrounding context within the localized volumetric image; and

classify a tooth'"'"'s conditions based on the extracted volumetric image using at least one of a multi-task approach, one network per condition approach, or a sub-network approach, wherein the multi-task approach is a single network outputting a prediction for multiple tooth conditions, the one network per condition approach is a single network outputting a prediction for a single tooth condition, and the sub-network approach is multiple networks outputting predictions for multiple tooth conditions.

11. The method of claim 10, wherein the at least one received volumetric image comprises a 3-D pixel array.

12. The method of claim 10, further includes a step of:

pre-processing by converting the 3-D pixel array into an array of Hounsfield Unit (HU) radio intensity measurements.

13. The method of claim 10, wherein the pre-processing for at least one of the localization or classification steps comprises rescaling using linear interpolation.

14. The method of claim 10, wherein the pre-processing comprises using any one of a normalization schemes to account for variations in image value intensity depending on at least one of an input or output of volumetric image.

15. The method of claim 10, wherein the localization is achieved using a V-Net-based fully convolutional neural network.

16. The method of claim 10, further comprises a step of:

achieving extraction by finding a minimum bounding rectangle around the localized and identified tooth.

17. The method of claim 16, wherein the bounding rectangle extends by at least 15 mm vertically and 8 mm horizontally (equally in all directions) to capture the tooth and surrounding context.

18. The method of claim 10, wherein the classification is achieved using a DenseNet 3-D convolutional neural network.