# US 11,992,329 B2 · Processing optical coherence tomography scans

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- Number: US 11,992,329 B2
- Document ID: US11992329B2
- Status: Granted
- Kind: B2
- Application number: 17/620,052
- Filed: 2020-06-19
- Published: 2022-09-29
- Granted: 2024-05-28
- Earliest claimed: 2019-06-19
- Assignees: Michelson Diagnostics Ltd.
- Applicants: Michelson Diagnostics Limited
- Inventors: Jonathan Denis Holmes
- Web page: https://patentgrep.com/patent/US11992329B2

## CPC

- A61B 5/443: {Evaluating skin constituents, e.g. elastin, melanin, water}
- A61B 5/0033: {Features or image-related aspects of imaging apparatus, e.g. for MRI, optical tomography or impedance tomography apparatus; Arrangements of imaging apparatus in a room}
- A61B 5/0066: {Optical coherence imaging}
- A61B 5/0077: {Devices for viewing the surface of the body, e.g. camera, magnifying lens}
- A61B 5/0082: {adapted for particular medical purposes}
- A61B 5/1075: {for measuring dimensions by non-invasive methods, e.g. for determining thickness of tissue layer (A61B8/0858 takes precedence)}
- A61B 5/441: {Skin evaluation, e.g. for skin disorder diagnosis}
- A61B 5/442: {Evaluating skin mechanical properties, e.g. elasticity, hardness, texture, wrinkle assessment}
- A61B 5/489: {Blood vessels}
- G06T 7/0012: {Biomedical image inspection}
- G06T 7/11: Region-based segmentation
- G06T 7/73: using feature-based methods
- G06V 20/698: {Matching; Classification}
- G16H 30/20: for handling medical images, e.g. DICOM, HL7 or PACS
- G16H 30/40: for processing medical images, e.g. editing
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- A61B 2576/02: specially adapted for a particular organ or body part
- G06T 2200/04: involving 3D image data
- G06T 2207/10101: Optical tomography; Optical coherence tomography [OCT]
- G06T 2207/20081: Training; Learning
- G06T 2207/20092: Interactive image processing based on input by user
- G06T 2207/30088: Skin; Dermal
- G06T 2207/30096: Tumor; Lesion
- G06T 2207/30101: Blood vessel; Artery; Vein; Vascular
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

A method of processing optical coherence tomography (OCT) scans through a subject's skin, the method comprising: receiving at least a plurality of OCT scans through the subject's skin (7), each scan representing an OCT signal in a slice through the subject's skin (7), with the plurality of OCT scans being spaced apart such that the OCT scans represent the OCT signal through a volume through the subject's skin; processing each OCT scan using a processor so as to so as to detect at least one three dimensional structure in the skin; classifying, using a processor, the structure as belonging to a class of a plurality of classes of structures; and outputting at least one of the structure and the class.

## Claims

### Claim 1

A method of processing optical coherence tomography (OCT) scans through a subject's skin, the method comprising:
receiving at least a plurality of OCT scans through the subject's skin, each scan representing an OCT signal in a slice through the subject's skin, with the plurality of OCT scans being spaced apart such that the OCT scans represent the OCT signal through a volume through the subject's skin;
processing each OCT scan using a processor so as to detect at least one three dimensional structure in the skin;
classifying, using a processor, the structure as belonging to a class of a plurality of classes of structures; and
outputting at least one of the structure and the class.

### Claim 2 (depends on 1)

The method of claim 1, comprising generating a confidence with which the structure can be assigned to the class of structures, which may be output in the step of outputting.

### Claim 3 (depends on 2)

The method of claim 2, comprising generating a confidence with which the structure could be assigned to a plurality of the classes in the plurality of classes.

### Claim 4 (depends on 1)

The method of claim 1, comprising segmenting the subject's skin within the OCT scan into different components, in which the segmentation is of the volume and is carried out in three dimensions.

### Claim 5 (depends on 4)

The method of claim 4, in which the segmentation determines the position of the structure relative to the components of the subject's skin and the step of classifying uses the position of the structure relative to the components of the subject's skin to classify the structure as belonging to a class.

### Claim 6 (depends on 1)

The method of claim 1, in which the step of classifying the structure, and optionally the step of detecting the structure, comprises using a machine learning algorithm.

### Claim 7 (depends on 1)

The method of claim 1, in which the OCT scans comprise dynamic OCT scans, in that they comprise time-varying OCT scans through the subject's skins, and the method comprises determining the position of blood vessels in the dynamic OCT scans and using the position of the blood vessels relative to the structure in the step of classifying the structure.

### Claim 8 (depends on 1)

The method of claim 1, comprising capturing at least one image of the surface of the user's skin, typically using a camera, and using each image to classify the structure.

### Claim 9 (depends on 8)

The method of claim 8, in which the images are captured using at least one of visible light and infrared light.

### Claim 10 (depends on 8)

The method of claim 8, comprising determining a pigment of the skin, and using the pigment to classify the structure.

### Claim 11 (depends on 1)

The method of claim 1, comprising the determination of at least one numerical parameter of the skin and using each numerical parameter in classifying the structure.

### Claim 12 (depends on 11)

The method of claim 11, in which the at least one numerical parameter comprises at least one parameter selected from the group comprising: optical attenuation coefficient, surface topography/roughness, depth of blood vessel plexus, and blood vessel density.

### Claim 13 (depends on 1)

The method of claim 1, comprising:
processing the OCT data for each scan with depth to produce a indicative depth scan representative of the OCT signal at each depth through all of the scans;
fitting a curve to the indicative depth scan, the curve comprising a first term which exponentially decays with respect to the depth and a second term which depends on the noise in the OCT signal; and
calculating a compensated intensity for the uncompensated OCT signal at each point through each scan, the compensated intensity comprising a ratio of a term comprising a logarithm of the OCT signal to a term comprising the fitted curve.

### Claim 14 (depends on 13)

The method of claim 13, comprising using the compensated intensity to classify the structure.

### Claim 15 (depends on 14)

The method of claim 14, in which the compensated intensity is used together with the uncompensated intensity in classifying the structure.

### Claim 16 (depends on 1)

The method of claim 1, in which the plurality of classes of structures are selected from the group comprising:
basal cell carcinoma (BCC);
squamous cell carcinoma (SCC);
actinic keratosis (AK);
seborrheic keratosis (SebK);
benign nevus;
dysplastic nevus;
hair follicle;
cyst;
sebaceous gland; and
melanoma.

### Claim 17 (depends on 1)

The method of claim 1, comprising allowing a user to select which structures are in the plurality of classes.

### Claim 18 (depends on 1)

The method of claim 1, in which the step of outputting comprises outputting any features of the OCT scans which contributed as part of the classification of the structure as belonging to the class.

### Claim 19 (depends on 18)

The method of claim 18, comprising allowing a user to reject some of the features which contributed, and repeating the step of classifying the structure without using the rejected features.

### Claim 20 (depends on 1)

An optical coherence tomography (OCT) image processing apparatus, comprising a processor, a display coupled to the processor and storage coupled to the processor, the storage carrying program instructions which, when executed on the processor, cause it to carry out the method of claim 1.