Image diagnosis assistance apparatus, data collection method, image diagnosis assistance method, and image diagnosis assistance program

A technology for image diagnosis and auxiliary devices, which is applied in computer-aided medical procedures, diagnosis, medical images, etc., and can solve problems such as false negative judgment, false positive judgment, and reduced accuracy

CN111655116APending Publication Date: 2020-09-11JAPANESE FOUND FOR CANCER RES +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Publication Date
2020-09-11

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Abstract

Provided are: an image diagnosis assistance apparatus capable of assisting diagnosis of an endoscopic image captured by an endoscopist; a data collection method; an image diagnosis assistance method;and an image diagnosis assistance program. The image diagnosis assistance apparatus is provided with a lesion assessment unit that assesses, by a convolutional neural network, the denomination and theposition of a lesion which is present in a digestive system endoscopic image of a patient captured by a digestive system endoscopic imaging device and information about accuracies thereof; and a display control unit that performs control for generating an analysis result image in which the denomination and the position of the lesion and the accuracies thereof are displayed and for displaying theimage on the digestive system endoscopic image. In the convolutional neural network, learning processing is performed on the basis of the denominations and the positions of the lesions that are present in a plurality of digestive system tumor endoscopic images predetermined by feature extraction of conditions of atrophy, intestinal metaplasia, swelling or depression of a mucous membrane, and mucous membrane color tone.
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Description

technical field

[0001] The invention relates to an image diagnosis auxiliary device, a data collection method, an image diagnosis auxiliary method and an image diagnosis auxiliary program. Background technique

[0002] Cancer is the leading cause of death in the world. According to the World Health Organization (WHO), 8.8 million people died in 2015. According to internal organs, the digestive system including the stomach or large intestine accounts for the most. Stomach cancer, in particular, is the fifth most common malignant tumor and the third most common cause of cancer-related death in the world, with approximately 1 million new cases and approximately 700,000 deaths each year. The prognosis of gastric cancer patients depends on the stage (degree of progression) of the cancer at the time of diagnosis. Progressed gastric cancer has a poor prognosis, but the five-year survival rate of early gastric cancer is over 90%. Many gastric cancers can be cured by early detection...

Examples

experiment example

[0111] Finally, an evaluation test for confirming the effect of the configuration of the above-mentioned embodiment will be described.

[0112] [Preparation of training data set]

[0113] Endoscopic images of EGD performed from April 2004 to December 2016 were prepared as a training data set (training data) used for learning a convolutional neural network in an image diagnosis support device. EGD is performed for screening or preoperative examination in daily diagnosis and treatment, using standard endoscopes (GIF-H290Z, GIF-H290, GIF-XP290N, GIF-H260Z, GIF-Q260J, GIF-XP260, GIF -XP260NS, GIF-N260, etc., Olympus Medical Systems (OlympusMedical Systems, Tokyo) and standard endoscopic video systems (EVIS LUCERA CV-260 / CLV-260, EVISLUCERA ELITE CV-290 / CLV-290SL , Olympus Medical Systems Corporation), collected endoscopic images.

[0114] The endoscopic images used as the training data set include: endoscopic images captured by irradiating the digestive organs of the subject wit...