CN111863209B - 基于图像识别的结肠镜检查质量评估工作站 - Google Patents

基于图像识别的结肠镜检查质量评估工作站 Download PDF

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CN111863209B
CN111863209B CN201910339987.2A CN201910339987A CN111863209B CN 111863209 B CN111863209 B CN 111863209B CN 201910339987 A CN201910339987 A CN 201910339987A CN 111863209 B CN111863209 B CN 111863209B
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王玉峰
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Tianjin Yujin Artificial Intelligence Medical Technology Co ltd
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    • 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
    • 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
    • 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
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/20—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06395—Quality analysis or management
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06398—Performance of employee with respect to a job function
    • 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
    • G16H30/20—ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
    • 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
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • 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
    • 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
    • G06T2207/30028—Colon; Small intestine
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Abstract

本发明涉及智能医疗技术领域，尤其涉及一种基于图像识别的结肠镜检查质量评估工作站，包括：算法模块、计时模块、数据传输模块、显示设备、肠镜设备和电脑主机；所述肠镜设备与所述数据传输模块连接，所述数据传输模块通过算法模块和计时模块与所述电脑主机连接，所述显示设备用于将电脑主机的结果进行显示。本发明能够通过不同的图像识别算法对医生每次的结肠镜检查过程中不同手法进行评估，在检查过程判断医生操作是否得当并给出相应的参考建议，对患者负责，而且可以使医生在检查过程中不断提高自己的能力，这样不仅大大减轻医生的压力，让医生更专注于其它更富创造性的任务中，而且有着巨大的经济及社会效益。

Description

基于图像识别的结肠镜检查质量评估工作站
技术领域
本发明涉及智能医疗技术领域，尤其涉及一种基于图像识别的结肠镜检查质量评估工作站。
背景技术
结肠镜广泛用于肠道疾病的诊断与治疗中，不论是对结直肠癌(colorectalcancer，CRC)最初的筛查还是后续的检测，它都是一种安全、准确、耐受性良好的重要方法。结肠镜检查对于早期发现癌前病变、预防CRC的发生显得非常重要，研究表明结肠镜检查可以减低77％的CRC风险。
但同时有很多因素影响结肠镜检查的质量，包括检查前(患者基本特征、肠道准备等)、检查过程中(盲肠插镜率、退镜时间、腺瘤检出率等)、检查后(出血率、穿孔率等)三大类影响结肠镜检查质量的评价指标；其中，结肠镜操作者相关的因素(即检查过程中)是最重要的组成部分。
因此重视提高结肠镜检查过程中的质量，对于降低CRC，尤其是降低间期CRC的发病率显得至关重要。然而结肠镜检查医师的水平参差不齐，一天多手术的疲惫会是医生降低检查标准，因此亟待开发一种基于图像识别的结肠镜检查质量评估工作站。
发明内容
本发明的目的在于克服上述技术的不足，而提供一种基于图像识别的结肠镜检查质量评估工作站。
本发明为实现上述目的，采用以下技术方案：一种基于图像识别的结肠镜检查质量评估工作站，其特征在于：包括：算法模块、计时模块、数据传输模块、显示设备、肠镜设备和电脑主机；所述肠镜设备与所述数据传输模块连接，所述数据传输模块通过算法模块和计时模块与所述电脑主机连接，所述显示设备用于将电脑主机的结果进行显示；
所述算法模块包括结肠镜检查模糊检测算法、检查完整度算法、病变识别算法、静止检测算法和碰壁检测算法；
其中，模糊检测算法利用opencv中自带的函数对输入图像进行灰度化处理后采用laplace算子对整幅图像进行全局方差检测，及对整幅图像进行边缘化检测，计算出整幅图像的全局方差，确定合适阈值可判定是否模糊；检查完整度算法对输入图像中的四个角的部分区域进行平均灰度值检测并选取合适的阈值判断四角亮与暗，若如连续输入的一定数目图像死角中每个角的亮的情况都包含，则检查完整，否则不完整；病变识别算法采用YOLOV3算法，能后实时检测传入视频图像中病变的位置；静止检测算法计算相距一定帧数的两幅图像的灰度直方图，其匹配程度达到一定阈值则判断改帧数对应时间内，肠镜镜头处于静止状态；碰壁检测算法采用深度学习的方法对采集到的距离肠壁过近的图片进行训练，得到检测模型；所述计时模块用计算总检查时间和退镜时间。
本发明的有益效果是:本发明能够通过不同的图像识别算法对医生每次的结肠镜检查过程中不同手法进行评估，在检查过程判断医生操作是否得当并给出相应的参考建议，不仅能监督医生更加认真的对待结肠镜检查，对患者负责，而且可以使医生在检查过程中不断提高自己的能力，这样不仅大大减轻医生的压力，让医生更专注于其它更富创造性的任务中，而且有着巨大的经济及社会效益。
附图说明
图1是本发明的结构示意图；
图2是本发明中算法模块的组成示意图。
具体实施方式
下面结合附图及较佳实施例详细说明本发明的具体实施方式。如图1所示，本发明一种基于图像识别的结肠镜检查质量评估工作站包括算法模块、计时模块、数据传输模块、显示设备、肠镜设备、电脑主机部分。
所述算法模块如图2所示，包括结肠镜检查模糊检测算法、检查完整度算法、病变识别算法、静止检测算法、碰壁检测算法。
所述检查完整度算法通过上述方法统计在退镜时，镜头旋转的周数。视野静止时，不进行本项功能的判定，当旋转周数达到一定数目则评判医生对肠道检查完整；所述模糊检测算法通过上述方法计算出从退镜到结束的时间内模糊帧数，根据公式退镜清晰度＝1–模糊帧数/总帧数计算该次检查的退镜清晰度评判医生操作；所述病变识别算法用于检查过程中识别病变并标记位置，最终记录总病变数目；所述静止检测算法通过上述方法计算出从退镜到结束的时间内静止时间，并与计时模块合作计算出有效的退镜时间；所述碰壁检测算法用上述方法计算退镜过程中与肠壁过近的图像帧数，并通过公式：安全指数＝1–发红模糊帧数/总帧数，计算出安全指数。
所述计时模块与肠镜设备相连，通过医生踩踏踏板标记进入肛门、到达阑尾开口和退出肛门的时间节点计算出检查总时长、入镜和退镜时间；联合上述算法通过公式：有效退镜时间(秒)＝退镜时间–(静止帧数+模糊帧数)/帧率，计算出有效退镜时间。
本发明每个算法计算出的参数意义如下：
总检查时间：评估医生熟练度的打分指标；总退镜时间：提高退镜速度可以体现医生效率；退镜清晰度：退镜过程中，清晰视帧占比越高，说明检查越有效，遗漏越少；有效退镜时间：有效退镜运动时间，指单位时间内，视野是存在变化的，且视野清晰可识别，此项功能用于评估医生退镜操作的有效程度，减少无效退镜操作，监测此项是为了避免医生无意义停止的时间过长。医生要求此项时间≥6分钟为合格；安全指数：评估医生在操作过程中碰触肠壁的操作占比多少，如果占比高，则安全指数底，说明医生操作导致的风险发生率高。
以上所述仅是本发明的优选实施方式，应当指出，对于本技术领域的普通技术人员来说，在不脱离本发明原理的前提下，还可以做出若干改进和润饰，这些改进和润饰也应视为本发明的保护范围。

Claims (1)

1.一种基于图像识别的结肠镜检查质量评估工作站，其特征在于：包括：算法模块、计时模块、数据传输模块、显示设备、肠镜设备和电脑主机；所述肠镜设备与所述数据传输模块连接，所述数据传输模块通过算法模块和计时模块与所述电脑主机连接，所述显示设备用于将电脑主机的结果进行显示；
所述算法模块包括结肠镜检查模糊检测算法、检查完整度算法、病变识别算法、静止检测算法和碰壁检测算法；
其中，模糊检测算法利用opencv中自带的函数对输入图像进行灰度化处理后采用laplace算子对整幅图像进行全局方差检测，及对整幅图像进行边缘化检测，计算出整幅图像的全局方差，确定合适阈值可判定是否模糊；检查完整度算法对输入图像中的四个角的部分区域进行平均灰度值检测并选取合适的阈值判断四角亮与暗，若如连续输入的一定数目图像死角中每个角的亮的情况都包含，则检查完整，否则不完整；病变识别算法采用YOLOV3算法，能后实时检测传入视频图像中病变的位置；静止检测算法计算相距一定帧数的两幅图像的灰度直方图，其匹配程度达到一定阈值则判断改帧数对应时间内，肠镜镜头处于静止状态；碰壁检测算法采用深度学习的方法对采集到的距离肠壁过近的图片进行训练，得到检测模型；所述计时模块用计算总检查时间和退镜时间。
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CA3137978A CA3137978C (en) 2019-04-25 2020-04-09 Image recognition based workstation for evaluation on quality check of colonoscopy
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CN112597981B (zh) * 2021-03-04 2021-06-01 四川大学 基于深度神经网络的肠镜退镜质量智能监控系统及方法
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CN113052844B (zh) * 2021-06-01 2021-08-10 天津御锦人工智能医疗科技有限公司 肠道内窥镜观察视频中图像的处理方法、装置及存储介质
CN113268623B (zh) * 2021-06-01 2022-07-19 上海市第一人民医院 一种人工智能化胃镜图像识别处理系统
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN202025321U (zh) * 2010-06-28 2011-11-02 深圳市金科威实业有限公司 用于进行图像质量评估的阴道镜网络系统
CN102946784A (zh) * 2010-06-22 2013-02-27 皇家飞利浦电子股份有限公司 用于内窥镜实时校准的系统和方法
CN108615037A (zh) * 2018-05-31 2018-10-02 武汉大学人民医院（湖北省人民医院） 基于深度学习的可控胶囊内镜操作实时辅助系统及操作方法
CN109146884A (zh) * 2018-11-16 2019-01-04 青岛美迪康数字工程有限公司 内窥镜检查监控方法及装置
CN109447987A (zh) * 2018-11-28 2019-03-08 武汉大学人民医院（湖北省人民医院） 基于深度学习的肠镜下溃疡性结肠炎辅助诊断系统及方法

Family Cites Families (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP5816511B2 (ja) 2011-10-04 2015-11-18 オリンパス株式会社 画像処理装置、内視鏡装置及び画像処理装置の作動方法
US20140323802A1 (en) * 2013-03-08 2014-10-30 Stephen C. Lloyd Methods and Systems for Improving the Prevention of Colorectal Cancer
US9978142B2 (en) 2014-04-24 2018-05-22 Arizona Board Of Regents On Behalf Of Arizona State University System and method for quality assessment of optical colonoscopy images
JP6132901B2 (ja) 2015-12-25 2017-05-24 オリンパス株式会社 内視鏡装置
WO2018211674A1 (ja) 2017-05-18 2018-11-22 オリンパス株式会社 画像処理装置、画像処理方法およびプログラム
WO2019012586A1 (ja) 2017-07-10 2019-01-17 オリンパス株式会社 医用画像処理装置及び医用画像処理方法
CN109598716B (zh) * 2018-12-05 2020-08-07 武汉楚精灵医疗科技有限公司 基于计算机视觉的肠镜退镜速度实时监测方法和系统

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102946784A (zh) * 2010-06-22 2013-02-27 皇家飞利浦电子股份有限公司 用于内窥镜实时校准的系统和方法
CN202025321U (zh) * 2010-06-28 2011-11-02 深圳市金科威实业有限公司 用于进行图像质量评估的阴道镜网络系统
CN108615037A (zh) * 2018-05-31 2018-10-02 武汉大学人民医院（湖北省人民医院） 基于深度学习的可控胶囊内镜操作实时辅助系统及操作方法
CN109146884A (zh) * 2018-11-16 2019-01-04 青岛美迪康数字工程有限公司 内窥镜检查监控方法及装置
CN109447987A (zh) * 2018-11-28 2019-03-08 武汉大学人民医院（湖北省人民医院） 基于深度学习的肠镜下溃疡性结肠炎辅助诊断系统及方法

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