CN115769252A - 基于深度学习获取主动脉的方法和存储介质 - Google Patents

基于深度学习获取主动脉的方法和存储介质 Download PDF

Info

Publication number
CN115769252A
CN115769252A CN202080100603.2A CN202080100603A CN115769252A CN 115769252 A CN115769252 A CN 115769252A CN 202080100603 A CN202080100603 A CN 202080100603A CN 115769252 A CN115769252 A CN 115769252A
Authority
CN
China
Prior art keywords
image
aorta
circle
center
obtaining
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202080100603.2A
Other languages
English (en)
Inventor
冯亮
刘广志
王之元
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Suzhou Rainmed Medical Technology Co Ltd
Original Assignee
Suzhou Rainmed Medical Technology Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from CN202010606963.1A external-priority patent/CN111815587A/zh
Priority claimed from CN202010606964.6A external-priority patent/CN111815588B/zh
Application filed by Suzhou Rainmed Medical Technology Co Ltd filed Critical Suzhou Rainmed Medical Technology Co Ltd
Publication of CN115769252A publication Critical patent/CN115769252A/zh
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/12—Edge-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/136—Segmentation; Edge detection involving thresholding
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/174—Segmentation; Edge detection involving the use of two or more images
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G06T7/66—Analysis of geometric attributes of image moments or centre of gravity
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2200/00—Indexing scheme for image data processing or generation, in general
    • G06T2200/04—Indexing scheme for image data processing or generation, in general involving 3D image data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10081—Computed x-ray tomography [CT]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20021—Dividing image into blocks, subimages or windows
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30008—Bone
    • G06T2207/30012—Spine; Backbone
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30048—Heart; Cardiac
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30061—Lung
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30101—Blood vessel; Artery; Vein; Vascular

Abstract

一种基于深度学习获取主动脉的方法和存储介质，所述方法包括：获取主动脉层的切片数据库与非主动脉层的切片数据库(S100)；分别对主动脉层的切片数据库和非主动脉层的切片数据库进行深度学习，获得深度学习模型(S200)；获取待处理的CT序列图像或CT序列图像的三维数据(S300)；提取待处理的CT序列图像或CT序列图像的三维数据的特征数据(S400)；根据深度学习模型、特征数据从CT序列图像中获取主动脉图像(S500)。依据特征数据和数据库获取深度学习模型，通过深度学习模型获取主动脉图像，具有提取效果好，鲁棒性高的优点，计算结果准确，在临床上具有较高的推广价值。

Description

PCT国内申请，说明书已公开。

Claims (14)

  1. PCT国内申请，权利要求书已公开。
CN202080100603.2A 2020-06-29 2020-11-30 基于深度学习获取主动脉的方法和存储介质 Pending CN115769252A (zh)

Applications Claiming Priority (5)

Application Number Priority Date Filing Date Title
CN2020106069631 2020-06-29
CN2020106069646 2020-06-29
CN202010606963.1A CN111815587A (zh) 2020-06-29 2020-06-29 基于ct序列图像拾取主动脉中心线上的点的方法和系统
CN202010606964.6A CN111815588B (zh) 2020-06-29 2020-06-29 基于ct序列图像获取降主动脉的方法和系统
PCT/CN2020/132796 WO2022000976A1 (zh) 2020-06-29 2020-11-30 基于深度学习获取主动脉的方法和存储介质

Publications (1)

Publication Number Publication Date
CN115769252A true CN115769252A (zh) 2023-03-07

Family

ID=79317360

Family Applications (2)

Application Number Title Priority Date Filing Date
CN202080100603.2A Pending CN115769252A (zh) 2020-06-29 2020-11-30 基于深度学习获取主动脉的方法和存储介质
CN202080100602.8A Pending CN115769251A (zh) 2020-06-29 2020-11-30 基于深度学习获取主动脉图像的系统

Family Applications After (1)

Application Number Title Priority Date Filing Date
CN202080100602.8A Pending CN115769251A (zh) 2020-06-29 2020-11-30 基于深度学习获取主动脉图像的系统

Country Status (5)

Country Link
US (2) US20230260133A1 (zh)
EP (2) EP4174760A1 (zh)
JP (2) JP7446645B2 (zh)
CN (2) CN115769252A (zh)
WO (2) WO2022000976A1 (zh)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116645372B (zh) * 2023-07-27 2023-10-10 汉克威(山东)智能制造有限公司 一种制动气室外观图像智能检测方法及系统

Family Cites Families (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2008142482A (ja) 2006-12-13 2008-06-26 Med Solution Kk 縦隔リンパ節郭清で切除される領域を複数の区域にセグメンテーションする装置およびプログラム
US20170235915A1 (en) * 2016-02-17 2017-08-17 Siemens Healthcare Gmbh Personalized model with regular integration of data
CN106803251B (zh) * 2017-01-12 2019-10-08 西安电子科技大学 由ct影像确定主动脉缩窄处压力差的装置与方法
JP6657132B2 (ja) 2017-02-27 2020-03-04 富士フイルム株式会社 画像分類装置、方法およびプログラム
US10685438B2 (en) * 2017-07-17 2020-06-16 Siemens Healthcare Gmbh Automated measurement based on deep learning
CN107563983B (zh) * 2017-09-28 2020-09-01 上海联影医疗科技有限公司 图像处理方法以及医学成像设备
CN109035255B (zh) * 2018-06-27 2021-07-02 东南大学 一种基于卷积神经网络的ct图像中带夹层主动脉分割方法
CN110264465A (zh) * 2019-06-25 2019-09-20 中南林业科技大学 一种基于形态学和深度学习的主动脉夹层动态检测方法
CN111815589B (zh) * 2020-06-29 2022-08-05 苏州润迈德医疗科技有限公司 基于ct序列图像获取无干扰冠脉树图像的方法和系统
CN111815588B (zh) * 2020-06-29 2022-07-26 苏州润迈德医疗科技有限公司 基于ct序列图像获取降主动脉的方法和系统
CN111815587A (zh) * 2020-06-29 2020-10-23 苏州润心医疗器械有限公司 基于ct序列图像拾取主动脉中心线上的点的方法和系统
CN111815584B (zh) * 2020-06-29 2022-06-07 苏州润迈德医疗科技有限公司 基于ct序列图像获取心脏重心的方法和系统
CN111815585B (zh) * 2020-06-29 2022-08-05 苏州润迈德医疗科技有限公司 基于ct序列图像获取冠脉树和冠脉入口点的方法和系统
CN111815583B (zh) * 2020-06-29 2022-08-05 苏州润迈德医疗科技有限公司 基于ct序列图像获取主动脉中心线的方法和系统
CN111815586B (zh) * 2020-06-29 2022-08-05 苏州润迈德医疗科技有限公司 基于ct图像获取左心房、左心室的连通域的方法和系统

Also Published As

Publication number Publication date
JP2023532269A (ja) 2023-07-27
CN115769251A (zh) 2023-03-07
US20230260133A1 (en) 2023-08-17
JP7446645B2 (ja) 2024-03-11
EP4174762A1 (en) 2023-05-03
US20230153998A1 (en) 2023-05-18
JP2023532268A (ja) 2023-07-27
WO2022000977A1 (zh) 2022-01-06
EP4174760A1 (en) 2023-05-03
WO2022000976A1 (zh) 2022-01-06

Similar Documents

Publication Publication Date Title
JP6790179B2 (ja) リアルタイムの診断上有用な結果
CN111815583B (zh) 基于ct序列图像获取主动脉中心线的方法和系统
CN111815585B (zh) 基于ct序列图像获取冠脉树和冠脉入口点的方法和系统
CN111815589B (zh) 基于ct序列图像获取无干扰冠脉树图像的方法和系统
CN111815586B (zh) 基于ct图像获取左心房、左心室的连通域的方法和系统
CN111932554A (zh) 一种肺部血管分割方法、设备及存储介质
Maklad et al. Blood vessel‐based liver segmentation using the portal phase of an abdominal CT dataset
WO2022000734A1 (zh) 基于ct序列图像拾取主动脉中心线上的点的方法和系统
CN111815588B (zh) 基于ct序列图像获取降主动脉的方法和系统
CN112132882A (zh) 从冠状动脉二维造影图像中提取血管中心线的方法和装置
US20230260133A1 (en) Methods for acquiring aorta based on deep learning and storage media
CN111815584B (zh) 基于ct序列图像获取心脏重心的方法和系统
CN111815590A (zh) 基于ct序列图像获取心脏重心和脊椎重心的方法和系统
EP4315237A1 (en) Systems and methods for automatic blood vessel extraction

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination