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WO2017047819A1 - Dispositif d'analyse de forme de vaisseau sanguin, procédé pour ce dernier, et programme logiciel informatique pour ce dernier - Google Patents

Dispositif d'analyse de forme de vaisseau sanguin, procédé pour ce dernier, et programme logiciel informatique pour ce dernier Download PDF

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Publication number
WO2017047819A1
WO2017047819A1 PCT/JP2016/077754 JP2016077754W WO2017047819A1 WO 2017047819 A1 WO2017047819 A1 WO 2017047819A1 JP 2016077754 W JP2016077754 W JP 2016077754W WO 2017047819 A1 WO2017047819 A1 WO 2017047819A1
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blood vessel
shape
fragment
computer
unit
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PCT/JP2016/077754
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English (en)
Japanese (ja)
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高伸 八木
将彦 中本
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イービーエム株式会社
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Priority to JP2017540051A priority Critical patent/JPWO2017047819A1/ja
Publication of WO2017047819A1 publication Critical patent/WO2017047819A1/fr

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/02—Arrangements for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis
    • A61B6/03—Computed tomography [CT]

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  • the present invention relates to a blood vessel shape analysis apparatus having a function of determining and displaying an abnormality of a blood vessel shape, which is a hotbed for the onset and growth of vascular lesions, a method thereof, and a computer software program thereof.
  • Vascular lesions are the main cause of death if the heart and brain are summed up.
  • advanced countries that are facing a super-aging society there is an urgent need to try to prevent vascular lesions. For this reason, it is required to clarify the causal relationship between lesions.
  • the fragment analysis unit discriminates the type of vascular lesion as the attribute of the fragment.
  • FIG. 1 is a schematic configuration diagram of a blood vessel shape analysis apparatus according to an embodiment of the present invention.
  • FIG. 2 is a diagram showing a blood vessel shape analysis flow in one embodiment of the present invention.
  • FIGS. 3A to 3C are diagrams for explaining the thinning of the blood vessel shape.
  • FIG. 4 is a diagram for explaining graphing.
  • FIG. 5 is a diagram for explaining a phenomenon that appears to be adhered.
  • FIG. 6 is a diagram for explaining the graphing of the blood vessel structure and the depth-first search of the graph.
  • FIGS. 7A and 7B are diagrams illustrating the adhesion separation process.
  • FIG. 8 is a diagram for explaining measurement of a blood vessel shape.
  • FIGS. 1 is a schematic configuration diagram of a blood vessel shape analysis apparatus according to an embodiment of the present invention.
  • FIGS. 2 is a diagram showing a blood vessel shape analysis flow in one embodiment of the present invention.
  • FIGS. 3A to 3C are diagrams for explaining the th
  • FIGS. 9A and 9B are diagrams for explaining measurement of irregularities on the surface of a blood vessel.
  • FIG. 10 is a diagram illustrating an example of a table of shape measurement data.
  • FIGS. 11A and 11B are views for explaining determination of blood vessel shape classification.
  • FIG. 12 is a diagram graphically showing the determination result superimposed on the three-dimensional shape of the blood vessel.
  • FIG. 13 is a diagram for explaining a conventional method for measuring a vascular lesion site.
  • FIG. 14 is a schematic configuration diagram of a blood vessel abnormality detection division apparatus according to the second embodiment of the present invention.
  • FIG. 15 is a diagram showing a blood vessel abnormality detection division flow in the second embodiment of the present invention.
  • FIGS. 16A to 16C are views for explaining generation of a standard blood vessel in the second embodiment.
  • FIG. 17 is a diagram for explaining a difference image in the second embodiment.
  • FIG. 18 is a diagram for explaining fragment analysis in the second embodiment.
  • FIG. 19 is a diagram
  • the first aspect of the present invention is an apparatus that analyzes (diagnose) a blood vessel shape serving as a hotbed for the onset / growth of a vascular lesion and outputs the result.
  • calculating the degree of abnormality of the blood vessel shape identifies the blood vessel site that can be a factor that causes the onset and progression of vascular tissue lesions. -Determine the state.
  • the configuration (input unit 14, thinning unit 15, graphing unit 16, shape measuring unit 17, determining unit 18, and display unit 19) is actually configured by computer software stored in a storage area of the hard disk. By being called by the CPU 11 and expanded and executed on the RAM 12, it is configured and functions as each component of the present embodiment.
  • the input unit 14 reads a medical image or the like (step S0).
  • the input medical image is a device capable of acquiring a tomographic image of a target blood vessel site such as MRA (magnetic resonance image), CTA (X-ray computed tomography image), DSA (angiographic image), or the like.
  • MRA magnetic resonance image
  • CTA X-ray computed tomography image
  • DSA angiographic image
  • it may be obtained by various apparatuses capable of acquiring image data in a target blood vessel site, such as US (ultrasonic image), IVUS (intravascular ultrasonic image), OCT (near infrared image).
  • the thinning unit 15 binarizes and thins the medical image to acquire the center line of the blood vessel (step S1).
  • the thinning unit 15 first binarizes the read medical image as shown in FIG. 3A, extracts a target blood vessel region, and generates blood vessel three-dimensional shape data (FIG. 3B).
  • the thinning unit 15 automatically sets a binarization threshold so as to extract a characteristic specific to a blood vessel wall based on a histogram of luminance values of the entire image.
  • the user may select a binarization threshold.
  • the thinning unit 15 performs thinning processing on the binarized blood vessel three-dimensional shape data (hereinafter referred to as “blood vessel shape”) to obtain a blood vessel center line (FIG. 3C).
  • blood vessel shape binarized blood vessel three-dimensional shape data
  • a plurality of algorithms are known for thinning and are not limited to a specific algorithm.
  • the thinning unit 15 centers the extracted voxel of the blood vessel region from the outer peripheral side (that is, the surface) of the blood vessel region.
  • the center line is obtained by extracting the core of the blood vessel by cutting it toward the center and fitting a spline curve or the like in the blood vessel running direction.
  • the graphing unit 16 performs graphing using the blood vessel center line obtained by the thinning process (step S2).
  • graphing means labeling center line data corresponding to each blood vessel portion, and the graphed data is referred to as graph data.
  • the graphing unit 16 first divides the blood vessel center line into elements for each region. As shown in FIG. 4, this element division is performed by specifying the end / branch point (A, B, C,%) In the center line acquired by the thinning process, and at the end / branch point. This is done by dividing the center line.
  • each blood vessel portion corresponding to the center line between the divided end points and branch points is referred to as a “blood vessel element”.
  • the graphing unit 16 performs labeling (# 1, 2, 3,%) On the center line data of each blood vessel element to generate graph data (graph).
  • the blood vessel is a loop circuit of a closed circuit at the whole body level, but a loop is not formed in a relatively microscopic region that is a target of blood vessel shape diagnosis. Therefore, in this embodiment, the presence or absence of adhesion is detected according to the presence or absence of a loop.
  • detection and separation of adhesions will be described in more detail.
  • the adhesion site is detected by executing a depth-first search on the graph. That is, end points and branch points are detected by analyzing the three-dimensional blood vessel shape of the extracted blood vessels, and their connection relationship is examined.
  • a solid arrow indicates a forward path
  • a dotted arrow indicates a return path.
  • a closed circuit is formed between No. 5 and No. 6.
  • the depth-first search proceeds from the 5th node to 6th, 7th, 8th and returns to 6th.
  • the forward path has passed through the lower side between No. 5 and No. 6, the upper side has not yet passed. However, if you select the upper side, you will arrive at the number 5 that you have already visited. If there is no closed path, only the return path will return to the visited node, but if there is a closed path, the visited node will be reached in the forward path.
  • the flow of separation processing is as follows. (1) The adhesion section is obtained from the change in the cross-sectional area of the blood vessel. (2) Estimate the center line of the adhesion section using the center line of the blood vessels before and after the adhesion section. (3) The shape of each blood vessel is estimated by fitting two tangent ellipses in the blood vessel cross section of the adhesion section to the contour of the blood vessel surface. (4) Divide the cross section of the adhesion section into two.
  • the separation processes (1) to (4) will be described in more detail with reference to FIG.
  • the cross-sectional area of the blood vessel before and after the adhesion section is measured. Since the two blood vessels are united in the adhesion part, when the cross-sectional area is plotted along the travel of the blood vessel, the cross-sectional area only in the adhesion section as shown in the lower graph of FIG. Will increase. This change is detected and the adhesion interval is determined.
  • the center line of the blood vessel the two center lines originally shifted to the inside, and finally touched to become a branch point. This is indicated by the one-dot chain line in FIG.
  • FIG. 7B shows a cross section of the adhesion section.
  • Two black dots 51 indicate the estimated center line.
  • the cross section of the blood vessel is an ellipse
  • the two ellipses 52 are fitted to the blood vessel contour 53 of the adhesion section under the condition that the centers of the two ellipses 52 are known and are in contact.
  • Two ellipses 52 fitted by dotted lines in FIG. 7B are shown.
  • the inside of the adhesion section is divided into two regions according to the ratio of the diameters of the two blood vessels, and the separation process is completed.
  • the shape measuring unit 17 measures the shape of each vascular element obtained by the graphing process (step S3).
  • the following parameters (1) to (5) are measured.
  • Concavity and convexity (information on blood vessel surface shape) (3) Bending (Information on geometric features of blood vessel center line)
  • Twist (Information on geometric features of blood vessel center line) (5) Bifurcation angle (Information on geometric characteristics of blood vessel center line)
  • “(1) Diameter” is an equivalent diameter of the blood vessel cross-sectional area in the blood vessel centerline traveling direction. As shown in FIG.
  • this parameter (1) is one or more discrete positions (for example, along the blood vessel centerline running direction in each blood vessel element (# 1, 2, 3,).
  • the shape measurement unit 17 may determine the minimum value, the maximum value, the average value of the measurement values for each vascular element from the measurement values. A fluctuation value (standard deviation or the like) can be calculated.
  • the parameters (2) to (4) described in detail below can also calculate the minimum value, maximum value, average value, variation value (standard deviation, etc.), etc., of each measurement value for each blood vessel element. .
  • FIG. 9A is a diagram for explaining the blood vessel surface coordinate system
  • FIG. 9B is a three-dimensional information mapping the blood vessel height H measured at each position Q in the blood vessel surface coordinate system. An example is shown.
  • solid lines 41 and 42 extending in the blood vessel running direction indicate examples of line segments in the blood vessel surface running coordinate system.
  • the position on the line segment extending in the blood vessel traveling direction is shown as the blood vessel surface traveling distance L.
  • the number of each line segment arranged in the circumferential direction of the blood vessel is shown as a line segment number N.
  • FIG. 10 shows an example of a table of shape measurement data measured by the shape measurement unit 17.
  • the above (1) to (5) are measured, but the present invention is not limited to this.
  • one or more parameters of the above (1) to (5) may be measured, and shape parameters other than the above parameters may be measured.
  • the determination unit 18 determines the blood vessel shape classification based on the result output by the shape measurement unit 17 (step S4).
  • the judgment algorithm is based on statistical data.
  • FIG. 11A is statistical data showing a change ratio of the equivalent diameter.
  • the normal range of the change rate of the equivalent diameter is determined by calculating a change value (change rate) based on the average value of the change ratios.
  • the “variation value” is a standard deviation value or a multiple thereof. In the example shown in FIG. 11A, it is shown that the normal range of the variation value of the equivalent diameter is determined to be ⁇ 20 deg to +20 deg.
  • the reference information (threshold value) determined based on the statistical data is stored in the storage unit of the apparatus, and the determination unit 18 uses the range based on the read statistical data as a reference for the measured shape.
  • the presence or absence of the risk of vascular lesion occurrence for each vascular shape is determined from the parameters. In the example shown in FIG. 11, it is determined that there is a risk of vascular lesion occurrence when the variation rate of the diameter exceeds a threshold value ⁇ 20 deg.
  • FIG. 11B shows the relationship between the position of each blood vessel element in the blood vessel centerline running direction and the measured equivalent diameter. In this embodiment, as shown in FIG.
  • a disease risk in which a region where the variation rate of the diameter is a positive value and exceeds a threshold value (+20 deg or more) is an area where the blood vessel indicates an enlarged type. Is determined. Further, a region where the fluctuation rate is a negative value and exceeds the threshold value ( ⁇ 20 deg or less) is determined to be a disease risk as a region showing a reduced type with strong locality.
  • the blood vessel shape analyzer After the determination process, the blood vessel shape analyzer outputs a determination result (step 5).
  • the determination result is displayed to the user in a three-dimensional or two-dimensional manner superimposed on the blood vessel shape.
  • FIG. 12 shows an example of a result output by computer processing. A hatched line in FIG. 12 indicates a portion of the determination result that exceeds the threshold value.
  • data measured by the shape measuring unit 17 may be output.
  • this apparatus can determine the risk of occurrence or occurrence of vascular lesions, the type and extent thereof based on the local shape change of the blood vessel shape, and can visually display the result on the user interface.
  • FIG. 13 is a diagram for explaining a conventional method for measuring a vascular lesion performed at a medical site, and shows a case of a cerebral aneurysm as an example.
  • the conventional measurement is performed in a state in which a three-dimensional cerebral aneurysm is viewed two-dimensionally from a certain viewpoint direction.
  • the neck length (n), maximum length (l), height (h), etc. of the aneurysm as shown by the dotted line in FIG. 13 are measured.
  • the difference image includes information and noise in which a part of a blood vessel such as a cerebral aneurysm is specifically changed, and the apparatus according to the present embodiment identifies the information and noise as “fragments”. Then, by classifying the fragments by supervised machine learning, the connection points between the morphological features of the fragments and the vascular lesions are constructed, and vascular abnormalities are analyzed by automatically analyzing the fragments based on this correspondence. Is detected and divided.
  • FIG. 14 is a schematic configuration diagram of a blood vessel abnormality detection division apparatus according to the second embodiment of the present invention.
  • the input is a medical image
  • the output is a blood vessel shape abnormal portion divided and attributed based on the medical image, shape information of the shape abnormal portion, and the shape abnormal portion superimposed on the input medical image, etc. .
  • the blood vessel abnormality detection / division device 100 has a program storage unit 160 and data storage units 170 and 171 connected to a bus 150 to which a CPU 111, a RAM 112, and an input / output IF 113 are connected. .
  • the program storage unit 160 includes an input unit 114, a thinning unit 115, a graphing unit 116, a shape measurement unit 117, a standard blood vessel generation unit 118, a difference image calculation unit 119, a fragment analysis unit 120, a blood vessel abnormality division unit 121, and a display. Part 122 is provided.
  • the data storage unit 170 stores the input medical image.
  • the data storage unit 171 stores blood vessel shape data 123, blood vessel shape measurement data 124, standard blood vessel shape data 125, fragment shape data 126, fragment shape measurement data 127, and risk determination result 128.
  • the above configuration (input unit 114, thinning unit 115, graphing unit 116, shape measuring unit 117, standard blood vessel generating unit 118, difference image calculating unit 119, fragment analyzing unit 120, abnormal blood vessel dividing unit 121, and display unit 122) Is configured by computer software stored in the storage area of the hard disk, and is configured to function as each component of the present embodiment by being called by the CPU 111 and expanded and executed on the RAM 112. It has become.
  • the shape measuring unit 117 measures the shape of each vascular element obtained by the graphing process in the same manner as the shape measuring unit 17 of the apparatus 10 of the first embodiment (step S13).
  • the shape measuring unit 117 measures the diameter (equivalent diameter) of each blood vessel. That is, as shown in FIG. 8, the shape measuring unit 117 sets a plurality of vertical cross sections at different positions (p1, p2, p3...) In the blood vessel centerline running direction in each blood vessel element, and the cross sectional areas thereof. From these, the diameter (equivalent diameter) of the blood vessel at each position is calculated.
  • the standard blood vessel generation unit 118 generates a standard blood vessel based on the blood vessel diameter in the shape measurement unit 117 (step S14).
  • FIG. 16 schematically shows the processing in the standard blood vessel generation unit 118.
  • the standard blood vessel generating unit 118 calculates the diameter of each cross section (P1 to P6) of the blood vessel element as shown in FIG. 16A acquired in the previous step (step S13) as shown in FIG.
  • an image mapped with the center line traveling direction as an axis is generated.
  • the standard blood vessel generation unit 118 generates a graph showing the change characteristic of the blood vessel diameter from the mapped image, with the horizontal axis as the center line position and the vertical axis as the equivalent diameter, By applying an approximate curve to the discrete points, the feature quantity of the blood vessel diameter change is converted into a mathematical expression. Thereafter, a new standard blood vessel image is generated so as to satisfy the feature amount. As described above, the standard blood vessel generation unit 118 calculates a feature amount related to a change in blood vessel shape, and artificially generates a blood vessel model as a standard blood vessel based on the feature amount.
  • the difference image calculation unit 119 calculates a difference image between the input medical image and the standard blood vessel image obtained by the standard blood vessel generation unit 118 (step S15).
  • FIG. 17 schematically shows the processing in the difference image calculation unit 119.
  • a solid line indicates a medical image as an input
  • a broken line indicates a standard blood vessel image.
  • the difference image calculation unit 119 outputs two locations 43 and 44 indicated by hatching in FIG. 17 as a difference image between the medical image and the standard blood vessel image.
  • the result obtained from supervised machine learning for a large-scale database is used as the determination algorithm for the fragment analysis process. That is, in this supervised machine learning, for example, in the case of a cerebral aneurysm, the computer determines whether each fragment is a cerebral aneurysm or noise, and tags each of the determined cerebral aneurysm and noise. This will build a database on fragment shape information.
  • the fragment shape information includes fragment volume, surface area, sphericity, representative length, and blood vessel part information where the fragment is located.
  • a multivariate analysis using a single or a plurality of combinations of these shape information is performed to determine a shape parameter that constitutes a significant difference from the noise. Using this shape parameter, the noise and the blood vessel abnormality to be measured are determined. Get identifying geometric features.
  • the fragment analysis unit 120 analyzes a new fragment shape based on the geometric feature and determines whether or not it is a blood vessel abnormality and what kind of abnormality is. In machine learning, the determination result of a blood vessel abnormality is improved by creating a database and analyzing the determination result.
  • the abnormal blood vessel division unit 121 searches the surroundings of a target divided into regions as an abnormal part (lesioned part) with the surface of the target as a starting point, and calculates the feature amount of the corresponding part in a high dimension, thereby improving the division accuracy. May be raised. More specifically, abnormal blood vessels such as cerebral aneurysms are accompanied by inversion of curvature from the parent blood vessel, and the abnormal blood vessel division unit 121 increases the abnormal blood vessel portion by tracking the inversion of curvature. Can be divided with accuracy.
  • the display unit 122 superimposes and displays the input medical image (step S18).
  • the thick solid line in FIG. 19 is a neckline specified with high accuracy by high-dimensional processing.
  • the shape information of the abnormal part measured by the shape measuring unit 117 may be displayed together with the superimposed image.
  • the configuration of each part of the apparatus in the present invention is not limited to the illustrated configuration example, and various modifications are possible as long as substantially the same operation is achieved.

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Abstract

L'invention concerne un dispositif qui comprend : une unité d'acquisition pour acquérir une image médicale ; une unité d'amincissement qui extrait une zone de vaisseau sanguin contenue dans au moins une partie de l'image médicale, et acquiert une ligne centrale d'un vaisseau sanguin dans la zone de vaisseau sanguin ; une unité de division qui divise la zone de vaisseau sanguin en éléments de vaisseau sanguin sur la base de la ligne centrale du vaisseau sanguin ; et une unité de mesure de forme qui génère des données de mesure de forme en mesurant la forme tridimensionnelle de chacun des éléments de vaisseau sanguin, les données de mesure de forme contenant au moins un élément parmi des informations se rapportant à la section transversale de vaisseau sanguin au niveau d'une ou plusieurs positions dans chacun des éléments de vaisseau sanguin, des informations se rapportant à la forme de surface du vaisseau sanguin, et des informations se rapportant aux caractéristiques géométriques de la ligne centrale du vaisseau sanguin.
PCT/JP2016/077754 2015-09-18 2016-09-20 Dispositif d'analyse de forme de vaisseau sanguin, procédé pour ce dernier, et programme logiciel informatique pour ce dernier WO2017047819A1 (fr)

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WO2018222717A1 (fr) * 2017-05-30 2018-12-06 University Of Massachusetts Système et méthodes pour le traitement d'une compression neurovasculaire
WO2018221152A1 (fr) * 2017-05-30 2018-12-06 国立大学法人九州大学 Procédé de génération d'informations cartographiques, procédé de détermination, et programme
CN109886953A (zh) * 2019-02-27 2019-06-14 数坤（北京）网络科技有限公司 一种血管异常检测方法、装置及计算机可读存储介质
JP2020011042A (ja) * 2018-07-13 2020-01-23 ニューロアナリティクス ピーティーワイ．リミテッド 脳血管異常の同定のための方法およびシステム
WO2020096321A1 (fr) * 2018-11-06 2020-05-14 사회복지법인 삼성생명공익재단 Appareil de diagnostic d'âge de vaisseau sanguin, procédé de diagnostic d'âge de vaisseau sanguin et programme informatique pour exécuter ledit procédé
KR102095731B1 (ko) * 2019-06-25 2020-05-18 주식회사 딥노이드 딥러닝 기반의 보조 진단 시스템의 mra 영상 학습 방법 및 혈관 병변 보조 진단 방법
KR20200061874A (ko) * 2018-11-26 2020-06-03 사회복지법인 삼성생명공익재단 혈관 지도 구축 장치, 방법 및 상기 방법을 실행하기 위한 컴퓨터 프로그램
CN111932535A (zh) * 2020-09-24 2020-11-13 北京百度网讯科技有限公司 用于处理图像的方法、装置、设备以及存储介质
JP2021030048A (ja) * 2019-08-23 2021-03-01 キヤノンメディカルシステムズ株式会社 経路決定方法、医用画像処理装置、モデル学習方法及びモデル学習装置
CN112862741A (zh) * 2019-11-12 2021-05-28 株式会社日立制作所 医用图像处理装置及医用图像处理方法、医用图像处理程序
CN112907504A (zh) * 2020-12-10 2021-06-04 深圳先进技术研究院 一种脑血管壁轮廓标注的几何解析方法
WO2021251425A1 (fr) * 2020-06-09 2021-12-16 国立大学法人東北大学 Dispositif de génération de vaisseau sanguin standard, dispositif d'évaluation de vaisseau sanguin, programme de génération de vaisseau sanguin standard, programme d'évaluation de vaisseau sanguin, procédé de génération de vaisseau sanguin standard et procédé d'évaluation de vaisseau sanguin
US20220198667A1 (en) * 2020-12-17 2022-06-23 GE Precision Healthcare LLC Method and device for extracting blood vessel wall
KR102454116B1 (ko) * 2022-05-12 2022-10-14 주식회사 메디픽셀 심혈관 병변 탐지를 위한 심혈관 조영 영상 처리 방법 및 시스템
WO2024075899A1 (fr) * 2022-10-06 2024-04-11 (의)삼성의료재단 Procédé et dispositif d'estimation d'anévrisme de l'aorte thoracique ascendante
CN117974543A (zh) * 2023-10-23 2024-05-03 郑州大学 一种用于血管介入手术的图像处理方法、系统及存储介质

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