Capsule endoscope for determining lesion area and receiving device

Patent metadata

Field	Value
Publication number	US-11055843-B2
Application number	US-201816001750-A
Country	US
Kind code	B2
Filing date	Jun 6, 2018
Priority date	Jun 14, 2017
Publication date	Jul 6, 2021
Grant date	Jul 6, 2021

Abstract

Provided is a capsule endoscope. The capsule endoscope includes: an imaging device configured to perform imaging on a digestive tract in vivo to generate an image; an artificial neural network configured to determine whether there is a lesion area in the image; and a transmitter configured to transmit the image based on a determination result of the artificial neural network.

First claim

What is claimed is:

1. A capsule endoscope comprising:
an imaging device configured to perform imaging on a digestive tract in a living body to generate an image;
an artificial neural network configured to determine whether there is a lesion area in the image; and
a transmitter configured to transmit the image based on a determination result of the artificial neural network,
wherein the transmitter generates a flag bit indicating the determination result of the artificial neural network, and transmits the flag bit together with the image.

2. The capsule endoscope of claim 1 , wherein the artificial neural network is based on convolution neural network (CNN).

3. The capsule endoscope of claim 2 , wherein the artificial neural network stores a kernel matrix for determining pixels in the image and a weight indicating a degree of coupling of neurons in the CNN.

4. The capsule endoscope of claim 3 , wherein the kernel matrix and the weight are previously learned data.

5. The capsule endoscope of claim 4 , wherein the kernel matrix and the weight are updated by the artificial neural network.

6. The capsule endoscope of claim 1 , wherein the artificial neural network activates the transmitter if the lesion area is present in the image and generates a control signal for deactivating the transmitter if the lesion area is not present in the image.

7. The capsule endoscope of claim 6 , further comprising:
a power supply circuit configured to supply power to the imaging device, the artificial neural network, and the transmitter; and
a switch configured to connect the power supply circuit and the transmitter.

8. The capsule endoscope of claim 7 , wherein the switch connects the power supply circuit and the transmitter in accordance with the control signal.

9. The capsule endoscope of claim 1 , wherein the artificial neural network is based on spiking neural network (SNN).

10. The capsule endoscope of claim 9 , wherein the artificial neural network a weight indicating a degree of coupling of neurons in the SNN.

11. A receiving device comprising:
a receiver configured to receive from a capsule endoscope an image of a digestive tract in a living body and a flag bit indicating whether there is a lesion area in the image;
a decoder configured to decode the flag bit and determine whether to store the image; and
a storage device configured to store the image in accordance with a decoding result of the decoder.

12. The receiving device of claim 11 , wherein the decoder decodes the flag bit before the image is stored in the storage device to determine whether the lesion area is present in the image.

13. The receiving device of claim 12 , wherein if the lesion area is present in the image, the image is stored in the storage device, and if the lesion area is not present in the image, the image is not stored in the storage device.

14. The receiving device of claim 11 , wherein the flag bit indicates a result that the artificial neural network of the capsule endoscope determines whether the lesion area is present in the image.

15. A capsule endoscope comprising:
an imaging device configured to perform imaging on a digestive tract in a living body to generate an image;
an artificial neural network configured to determine whether there is a lesion area in the image; and
a transmitter configured to transmit the image based on a determination result of the artificial neural network,
wherein the artificial neural network activates the transmitter if the lesion area is present in the image and generates a control signal for deactivating the transmitter if the lesion area is not present in the image.

Assignees

• Electronics & Telecommunications Res Inst

Inventors

• Oh Kwang Il
• Kang Tae Wook
• Kim Sung Eun
• Park Mi Jeong
• Park Seong Mo
• Park Hyung-Il
• Lee Jae-Jin
• Lim In Gi

Classifications

• G06N3/045

  Combinations of networks · CPC title

• G06N3/08

  Learning methods · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• A61B1/000096

  using artificial intelligence · CPC title

• A61B1/000094

  extracting biological structures · CPC title