Brain imaging system and brain imaging method
AssigneeA-moy Limited
InventorFAN-PEI YANG
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Abstract
A brain imaging system includes a first imaging device, a second imaging device and a processor. The first imaging device captures first brain images by scanning a patient, and the second imaging device captures second brain images. The processor is configured to: receive and distinguish the first and second brain images; convert the first and second brain images to a first format and a second format; identify and analyzing first features and second features in the converted first and second brain images to obtain cerebral perfusion data and brain lesion data by a first deep learning model and a second deep learning model. A brain imaging method is operated with the brain imaging system.
Claims20
A brain imaging system, comprising:
- a first imaging device, configured to capture a plurality of first brain images that provide cerebral data representing a contrast agent in a brain of the patient over time by scanning a patient;
- a second imaging device, configured to capture a plurality of second brain images by scanning the patient;
- and
- a processor electrically connected to the first image device and the second image device, wherein the processor is configured to:
- receive the first brain images from the first imaging device and the second brain images from the second imaging device, and distinguish the first brain images and the second brain images;
- convert the first brain images into a first format;
- identify first features in the converted first brain images by a first deep learning model;
- obtain cerebral perfusion data by analyzing the first features identified in the converted first brain images through the first deep learning model;
- convert the second brain images into a second format;
- identify second features in the converted second brain images by a second deep learning model;
- and
- obtain brain lesion identification data by analyzing the second features identified in the converted second brain images through the second deep learning model;
- wherein the first deep learning model includes a long-short-term memory (LSTM) neural network, by which the first features in the converted first brain images are identified;
- and
- wherein the second deep learning model includes you-look-only-once (YOLO) algorithm and region-convolutional neural network (R-CNN), by both of which the second features in the converted second images are identified.
A brain imaging method, comprising:
- configuring a first imaging device to capture a plurality of first brain images that provide cerebral data representing a contrast agent by scanning a patient;
- configuring a second imaging device to capture a plurality of second brain images by scanning a patient;
- configuring a processor, which is electrically connected to the first imaging device and the second imaging device, to:
- receive the first brain images from the first imaging device and the second brain images from the second imaging device, and distinguish the first brain images and the second brain images;
- convert the first brain images into a first format;
- identify first features in the converted first brain images by a first deep learning model;
- obtain cerebral perfusion data by analyzing the first features identified in the converted first brain images through the first deep learning model;
- convert the second brain images into a second format;
- identify second features in the converted second brain images by a second deep learning model;
- and
- obtain brain lesion identification data by analyzing the second features identified in the converted second brain images through the second deep learning model;
- wherein the first deep learning model includes a long-short-term memory (LSTM) neural network, by which the first features in the converted first brain images are identified;
- and
- wherein the second deep learning model includes you-look-only-once algorithm (YOLO) and region-convolutional neural network (R-CNN), by both of which the second features in the converted second images are identified.
Dependencies are inferred from the claim text.