WO2011058949A1 - Diagnosis support system and program - Google Patents

Diagnosis support system and program Download PDF

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WO2011058949A1
WO2011058949A1 PCT/JP2010/069844 JP2010069844W WO2011058949A1 WO 2011058949 A1 WO2011058949 A1 WO 2011058949A1 JP 2010069844 W JP2010069844 W JP 2010069844W WO 2011058949 A1 WO2011058949 A1 WO 2011058949A1
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image processing
image
processing flow
unit
support system
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雅之 太田
久美子 瀬戸
俊太郎 由井
和喜 松崎
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株式会社日立製作所
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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
    • 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
    • A61B6/56—Details of data transmission or power supply, e.g. use of slip rings
    • A61B6/563—Details of data transmission or power supply, e.g. use of slip rings involving image data transmission via a network
    • 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
    • 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

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  • the present invention relates to a diagnosis support system and a program.
  • Radiologists perform advanced image processing such as registration (registration) and region extraction (segmentation) between time-series data and other modality data from basic image processing such as image enlargement / reduction, color change, and filtering. Up to processing, it is possible to perform image processing continuously by combining various image processing functions. However, since there are many parameters even if one image processing is taken up, there are innumerable patterns of a series of image processing, that is, image processing flows that are continuously performed by combining image processing functions. On the other hand, there is a limit to the amount of time that a radiologist can spend on interpretation work, so there is a need for efficient and safe diagnosis support technology.
  • Patent Document 1 describes an information processing apparatus and an information processing method. This is a patent document relating to suitably creating an electronic medical record in which link information relating to a medical image to be diagnosed, image processing history information and a diagnostic report are associated with each other.
  • Patent Document 2 describes a diagnosis support method, a diagnosis support apparatus, a diagnosis support system, and a diagnosis support program. This is a patent document relating to displaying a plurality of icons for inputting operation instructions, extracting icons to be displayed using statistical information, and presenting them to a user.
  • the diagnosis support system and program of the present invention include: An image data input / output unit for inputting / outputting image data; An image data display unit for displaying an image input from the image data input / output unit; An image processing command input unit for inputting an image processing command for the image data displayed on the image data display unit; An image processing execution unit for executing image processing input from the image processing command input unit; An image processing flow recording unit that records a series of image processing executed by the image processing execution unit as an image processing flow; A storage unit for storing the image processing flow; An image processing flow reproduction unit that receives and reproduces the image processing flow from the storage unit; It is characterized by providing.
  • individual image processing histories including at least one of an intermediate image immediately before executing individual image processing, an intermediate image immediately after execution of each image processing, or an image processing parameter are stored in a permutation.
  • the image processing flow stored in the storage unit further includes an optimization execution unit that performs optimization of the image processing flow, and the optimization execution unit deletes the intermediate image one by one. It is preferable to perform optimization.
  • the optimization execution unit calculates a total calculation time of the image processing flow and calculates the data size of the image processing flow so that the total calculation time is within a specified time or the data size is specified. It is preferable to delete the intermediate images of the image processing flow one by one so as to fit within the data size.
  • Example 1 of the present invention It is a system configuration diagram in Example 1 of the present invention. It is an example of the image processing flowchart in three-dimensional quantification of tumor size. It is a figure which shows the data structure of an image processing log
  • FIG. 1 is a system configuration diagram showing a system configuration of an embodiment of the present invention.
  • an image data input / output unit 101 inputs / outputs image data by communication with an image diagnostic device such as CT, MRI, PET, SPECT, US, or communication with an image server. The communication is performed via the network 102.
  • the client area 103 of the system includes an image data display unit 104 that displays an input image, an image processing command input unit 105 that inputs an image processing command, and an image processing flow reproduction unit 106.
  • the image data display unit 104 displays a two-dimensional image such as a US tomographic image or an X-ray transmission image, a three-dimensional image such as CT or MRI, and a four-dimensional image added with a time axis.
  • a plurality of image data are displayed side by side vertically or horizontally.
  • the tumor size of cancer is one of the important factors related to determination of stage of disease and decision of treatment policy as well as tumor properties and position information.
  • By processing examination image data as 3D volume data and executing a series of image processing it is possible to calculate the volume of the tumor and to calculate the maximum major axis and the maximum minor axis of the tumor in three dimensions It becomes.
  • image quantification value calculation methods not only the type of image processing function to be executed and the image processing parameters, but also the order of image processing, and the arterial phase and delay phase, etc., can be used for contrast CT images. if time phase executes image processing for any data to present, since the like becomes an option, a quantitative value users using the method and parameters each determines the optimum, including the radiologist Will be calculated.
  • an image processing command is input from the image processing command input unit 105, then image processing is executed in the image processing execution unit 107, and the image processing result executed by the image processing execution unit 107 Is displayed on the image data display unit 104, and an image processing command is input from the image processing command input unit 105 again to execute image processing.
  • image processing execution unit 107 In the three-dimensional quantification of tumors, calculating the tumor volume, maximum major axis, maximum minor axis, etc. is the purpose and output data of the image processing flow. Complete.
  • the flowchart in FIG. 2 is an example of an image processing flow in which a plurality of image processes are continuously executed.
  • Classic hepatocellular carcinoma tumors show high concentrations in the arterial phase of contrast-enhanced CT and low concentrations in the delayed phase.
  • the image processing flow shown here reads these two temporal data into the system as input images for the purpose of tumor quantification (201), performs registration (202), generates a difference image (203), After performing filtering (204) to binarize at a certain threshold, extract the liver tumor area (205), and finally calculate (206) quantitative values such as tumor volume, maximum major axis, and maximum minor axis It is a series of image processing that goes through.
  • the image processing flow described here is an example of a combination of individual image processing functions, and the user can freely combine the image processing functions or set parameters of individual image processing.
  • FIG. 3 shows the data structure of each image processing history corresponding to the image processing flow of FIG.
  • Each image processing history may include image data 301 immediately before execution of image processing, image data 302 immediately after execution of image processing, image processing parameters 303, image processing calculation time 304, own data size 305, and the like as data items.
  • the image processing calculation time 304 the image processing calculation start time and end time may be recorded instead.
  • data obtained by combining the set of records No. 1 to No. 5 in FIG. 3 and the image quantitative value obtained as a result of the quantitative value calculation which is the last image processing becomes the data of the image processing flow.
  • the image processing flow recording unit 108 When individual image processing is executed, it is recorded in the image processing flow recording unit 108 as an image processing history having a data structure to be described later. When a series of image processing is completed, the image processing history recorded in the image processing flow recording unit 108 is stored in the storage unit 109 as an image processing flow together with the image quantitative value.
  • the optimization execution unit 110 thins out intermediate images as necessary from the image processing flow, and stores them in the storage unit 109.
  • whether or not the image data is stored as image processing flow data is a trade-off between saving storage capacity of the storage unit and improving the reproduction speed of the image processing flow, and the circumstances differ for each medical institution.
  • the storage policy of the image processing flow needs to be changed with an environmental change such as an increase in storage capacity of the storage unit. Therefore, a flexible mechanism that optimizes the storage format of the image processing flow considering the circumstances of each medical institution and can be customized is required. This is implemented by the optimization execution unit 110.
  • FIG. 4 is a flowchart illustrating an example of image processing flow optimization performed by the optimization execution unit 110.
  • optimization is performed with an emphasis on improving the reproduction speed of the image processing flow.
  • the total calculation time can be calculated by adding all the individual image processing calculation times among the data items shown in FIG.
  • the present invention may be configured to delete several pieces of intermediate image data together without deleting them one by one.
  • the calculation time can be shortened and the server capacity can be reduced.
  • a support system can be provided.
  • FIG. 5 is a flowchart showing an example of image processing flow optimization performed by the optimization execution unit 110 as in FIG.
  • optimization is performed with an emphasis on saving storage capacity by reducing the data size of the image processing flow.
  • intermediate image data is deleted one by one until the total file size falls within the specified file size.
  • conditions may be defined for both the total calculation time and the total file size as branching conditions of the optimization flowchart.
  • reproduction of the image processing flow is executed by presenting intermediate images of the image processing flow to be reproduced in chronological order.
  • the image processing execution unit 107 may execute image processing again.
  • the effect of the present invention in the present embodiment is that the image processing flow can be comfortably reproduced by storing the image processing flow for the three-dimensional image data in an appropriate format. It is effective in a case study case at a conference or in a scene where it is desired to confirm image processing performed by another doctor.
  • Image data input / output unit 102
  • Network 103
  • Image data display unit 105
  • Image processing command input unit 106
  • Image processing flow playback unit 107
  • Image processing execution unit 108
  • Image processing flow recording unit 109
  • Optimization execution unit 201
  • 3D Image data reading processing 202
  • Registration processing 203
  • Difference processing 204
  • Filtering processing 205
  • Segmentation processing 206
  • Quantitative value calculation processing 301
  • Intermediate image data immediately before execution of image processing 302
  • Intermediate image data immediately after execution of image processing 303
  • Image processing parameters 304

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Abstract

Disclosed is a diagnosis support system for storing an image processing flow in a customized format for each medical institution, and conveniently reproducing the recorded image processing flow. As a storage method of an image processing flow to be reproduced by an image processing flow reproduction unit, intermediate images corresponding to individual image processing are deleted one by one with computing time or data size as an index, and at a state of satisfying stipulated conditions, the image processing flow is stored.

Description

診断支援システムおよびプログラムDiagnosis support system and program
　本発明は、診断支援システムおよびプログラムに関するものである。 The present invention relates to a diagnosis support system and a program.
　医療分野において、CTやMRI、PETに代表される画像診断装置の高性能化が急激に進んでいる。感度や画像解像度の向上、撮像時間の短縮といったハード面での改良に加え、造影剤をはじめとする新規薬剤の研究開発成果との相乗効果で、今後もさらなる進歩が見込まれている。それと同時に病院情報システム、殊に画像診断に係るシステムも、この急激な進歩に追随して進歩していく必要がある。データの入出力に関してはDICOMやHL7といった標準的な規格が既に存在するため、例えば新規の画像診断装置が開発されて医療現場で活用されることになっても、画像診断装置側が対応することにより大きな問題が生じることはないが、新規の画像診断装置から出力される画像をどのように扱ってどのように解釈すべきかが大きな課題となる。 In the medical field, the performance of diagnostic imaging apparatuses represented by CT, MRI, and PET is rapidly increasing. In addition to hardware improvements such as improved sensitivity and image resolution and shortened imaging time, further progress is expected in the future due to synergistic effects with research and development results of new drugs such as contrast media. At the same time, hospital information systems, especially systems related to diagnostic imaging, need to follow this rapid progress. Standards such as DICOM and HL7 already exist for data input / output. For example, even if a new diagnostic imaging device is developed and used in the medical field, the diagnostic imaging device will respond. Although no major problem occurs, how to handle and interpret an image output from a new diagnostic imaging apparatus is a major issue.
　医用画像データが血液・生化学検査結果などの数値データと異なる点は、診断装置の出力データそのものから医学的な意味を持つ数値データが直接得られるとは限らない点である。放射線科医は、画像の拡大・縮小や色調変更、フィルタリングといった基本的な画像処理から、時系列データ間や別モダリティデータ間の位置合わせ（レジストレーション）、領域抽出（セグメンテーション）などの高度な画像処理まで、多種多様の画像処理機能を組み合わせて連続的に画像処理を行うことが可能である。しかし、画像処理一つを取り上げてみても多くのパラメータが存在するため、画像処理機能を組み合わせて連続的に行われる一連の画像処理すなわち画像処理フローのパターンは無数である。一方で放射線科医が読影作業に費やすことのできる時間には限りがあるため、効率的で安全性の高い診断支援技術が求められている。 The difference between medical image data and numerical data such as blood and biochemical test results is that numerical data having medical meaning is not always obtained directly from the output data of the diagnostic apparatus. Radiologists perform advanced image processing such as registration (registration) and region extraction (segmentation) between time-series data and other modality data from basic image processing such as image enlargement / reduction, color change, and filtering. Up to processing, it is possible to perform image processing continuously by combining various image processing functions. However, since there are many parameters even if one image processing is taken up, there are innumerable patterns of a series of image processing, that is, image processing flows that are continuously performed by combining image processing functions. On the other hand, there is a limit to the amount of time that a radiologist can spend on interpretation work, so there is a need for efficient and safe diagnosis support technology.
　放射線科医が日々の読影業務の中で実行する画像処理フローを適切に記録して活用することは、医療現場の様々なシーンにおける効果が期待できる。例えば、カンファレンスにおいて臨床医と放射線科医、放射線科技師などの医療従事者が集まってある症例について議論をする場面では、CT検査結果の原画像を提示したりそこから病変部位の定量評価を行った結果を示したりしながらその症例に関する情報を共有し合うが、原画像から定量値算出に至るまでの画像処理フローを記録しておくことによって、放射線科医の作業手順を確認しながら議論をすることが可能となる。あるいは、ある患者の画像データに対して記録しておいた画像処理フローを記録しておき、次回検査の画像データに対してその画像処理フローを適用することで、時点の異なるデータ間の比較を効率的かつ精度良く行うことができる。 適 切 Properly recording and utilizing the image processing flow performed by the radiologist in daily interpretation work can be expected to have an effect in various scenes in the medical field. For example, in a conference where a clinician, a radiologist, or a medical professional such as a radiologist is discussing a case, an original image of a CT examination result is presented and a lesion is quantitatively evaluated from there. While sharing the information about the case while showing the results, we recorded the image processing flow from the original image to the calculation of the quantitative value, so that we could discuss while confirming the work procedure of the radiologist. It becomes possible to do. Alternatively, by recording the image processing flow recorded for the image data of a certain patient and applying the image processing flow to the image data of the next examination, a comparison between data at different time points can be performed. It can be performed efficiently and accurately.
　上記のような画像処理フローをはじめとする、医療現場における作業履歴を活用する技術としてはこれまでにも以下のような技術が開示されている。 The following techniques have been disclosed so far as techniques for utilizing the work history in the medical field including the image processing flow as described above.
　特許文献１では、情報処理装置及び情報処理方法について記載されている。診断の対象となった医用画像に関するリンク情報と画像処理履歴情報と診断レポートとを対応付けた電子カルテを好適に作成することに関する特許文献である。 Patent Document 1 describes an information processing apparatus and an information processing method. This is a patent document relating to suitably creating an electronic medical record in which link information relating to a medical image to be diagnosed, image processing history information and a diagnostic report are associated with each other.
　特許文献２では、診断支援方法、診断支援装置、診断支援システム、及び診断支援プログラムについて記載されている。操作指示を入力するための複数のアイコンを表示し、統計情報を用いて表示するアイコンを抽出しユーザに提示することに関する特許文献である。 Patent Document 2 describes a diagnosis support method, a diagnosis support apparatus, a diagnosis support system, and a diagnosis support program. This is a patent document relating to displaying a plurality of icons for inputting operation instructions, extracting icons to be displayed using statistical information, and presenting them to a user.
特開２００４－３３４７８９号公報JP 2004-334789 A 特開２００７－３３０３７４号公報JP 2007-330374 A
　しかし、上記の既に開示されている技術では、記録された画像処理フローを再生する場面において画像処理前の原画像からもう一度画像処理フローを全て演算し直す必要があるので、演算時間のかかる高度な画像処理フローの再生に対しては適切な技術であるとはいえない。また、医療機関によってネットワークやサーバの容量を含めたシステム構成が異なるため、医療機関ごとにカスタマイズした形式で画像処理フローの保存が容易に行えることも重要な課題である。 However, in the above-described technology already disclosed, it is necessary to recalculate the entire image processing flow from the original image before image processing in the scene where the recorded image processing flow is reproduced. It cannot be said that this is an appropriate technique for reproducing an image processing flow. In addition, since the system configuration including the capacity of the network and the server differs depending on the medical institution, it is also an important issue that the image processing flow can be easily saved in a format customized for each medical institution.
　上記課題を解決するために、本発明の診断支援システムおよびプログラムは、
画像データを入出力する画像データ入出力部と、
前記画像データ入出力部から入力された画像を表示する画像データ表示部と、
前記画像データ表示部に表示された画像データに対する画像処理命令を入力する画像処理命令入力部と、
前記画像処理命令入力部から入力された画像処理を実行する画像処理実行部と、
前記画像処理実行部によって実行される一連の画像処理を画像処理フローとして記録する画像処理フロー記録部と、
前記画像処理フローを記憶する記憶部と、
前記記憶部から前記画像処理フローを受け取り再生する画像処理フロー再生部と、
を備えることを特徴とする。 In order to solve the above problems, the diagnosis support system and program of the present invention include:
An image data input / output unit for inputting / outputting image data;
An image data display unit for displaying an image input from the image data input / output unit;
An image processing command input unit for inputting an image processing command for the image data displayed on the image data display unit;
An image processing execution unit for executing image processing input from the image processing command input unit;
An image processing flow recording unit that records a series of image processing executed by the image processing execution unit as an image processing flow;
A storage unit for storing the image processing flow;
An image processing flow reproduction unit that receives and reproduces the image processing flow from the storage unit;
It is characterized by providing.
　また、前記画像処理フローは、個々の画像処理を実行する直前の中間画像あるいは直後の中間画像あるいは画像処理パラメータの少なくともいずれか1以上を含む個々の画像処理履歴を順列に保持することが好ましい。 In the image processing flow, it is preferable that individual image processing histories including at least one of an intermediate image immediately before executing individual image processing, an intermediate image immediately after execution of each image processing, or an image processing parameter are stored in a permutation.
　さらに、前記記憶部に記憶された前記画像処理フローに対し、前記画像処理フローの最適化を実行する最適化実行部をさらに備え、前記最適化実行部は前記中間画像を１ずつ削除することで最適化を実行することが好ましい。 Further, the image processing flow stored in the storage unit further includes an optimization execution unit that performs optimization of the image processing flow, and the optimization execution unit deletes the intermediate image one by one. It is preferable to perform optimization.
　また、前記最適化実行部は、前記画像処理フローの総演算時間を算出し、前記総演算時間が規定時間内に収まるように、あるいは前記画像処理フローのデータサイズを算出し前記データサイズが規定データサイズ内に収まるように、前記画像処理フローの中間画像を１ずつ削除することが好ましい。 Further, the optimization execution unit calculates a total calculation time of the image processing flow and calculates the data size of the image processing flow so that the total calculation time is within a specified time or the data size is specified. It is preferable to delete the intermediate images of the image processing flow one by one so as to fit within the data size.
　医療従事者が行った画像処理フローを記録・保存し、必要な場面で保存しておいた画像処理フローを高速に再生することが容易となる。また、医療機関ごとに画像処理フローの保存方法をカスタマイズし、個々の医療機関に適した方法で保存することが容易となる。 It is easy to record and save the image processing flow performed by the medical staff and to play back the image processing flow stored in the necessary scene at high speed. In addition, it is easy to customize the storage method of the image processing flow for each medical institution and to store it by a method suitable for each medical institution.
本発明の実施例１におけるシステム構成図である。It is a system configuration diagram in Example 1 of the present invention. 腫瘍サイズの3次元的定量化における画像処理フローチャートの例である。It is an example of the image processing flowchart in three-dimensional quantification of tumor size. 画像処理履歴のデータ構造を示す図である。It is a figure which shows the data structure of an image processing log | history. 画像処理フローの最適化フローチャートである。It is an optimization flowchart of an image processing flow. 画像処理フローの最適化フローチャートである。It is an optimization flowchart of an image processing flow.
　以下、本発明の実施形態について、図面を用いて詳細に説明する。 Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.
　図１は、本発明の実施形態のシステム構成を表すシステム構成図である。図１において、画像データ入出力部101は、CT, MRI, PET, SPECT, US等、画像診断機器との通信、あるいは画像サーバとの通信により画像データを入出力する。上記通信はネットワーク102を介して行われる。システムのクライアント領域103には、入力された画像を表示する画像データ表示部104、画像処理命令を入力する画像処理命令入力部105、画像処理フロー再生部106が含まれる。画像データ表示部104は、US断層像やX線透過画像などの2次元画像、CT, MRIなどの3次元画像、さらには時間軸を加えた4次元画像を表示する。また、同一患者の時相が異なるデータ、日時が異なるデータ、あるいは類似症例を並べて表示する場合には、複数の画像データを縦または横に並べて表示する。 FIG. 1 is a system configuration diagram showing a system configuration of an embodiment of the present invention. In FIG. 1, an image data input / output unit 101 inputs / outputs image data by communication with an image diagnostic device such as CT, MRI, PET, SPECT, US, or communication with an image server. The communication is performed via the network 102. The client area 103 of the system includes an image data display unit 104 that displays an input image, an image processing command input unit 105 that inputs an image processing command, and an image processing flow reproduction unit 106. The image data display unit 104 displays a two-dimensional image such as a US tomographic image or an X-ray transmission image, a three-dimensional image such as CT or MRI, and a four-dimensional image added with a time axis. In addition, when data of the same patient with different time phases, data with different dates and times, or similar cases are displayed side by side, a plurality of image data are displayed side by side vertically or horizontally.
　以降の説明は、癌症例における腫瘍の3次元的定量化を例に挙げて実施形態を詳細に説明するが、本例は本実施形態の範囲を限定するものではない。 In the following description, the embodiment will be described in detail by taking a three-dimensional quantification of a tumor in a cancer case as an example, but this example does not limit the scope of this embodiment.
　癌の腫瘍サイズは、腫瘍の性状や位置情報とともに病期の判定や治療方針決定に関わる重要な要素の一つである。検査画像データを3次元的なボリュームデータとして扱い一連の画像処理を実行することで、腫瘍の体積を算出したり、腫瘍の最大長径、最大短径を3次元的に算出したりすることが可能となる。しかし、これらの画像定量値を算出する方法においては、実行する画像処理機能の種類、画像処理パラメータのみならず、画像処理の順序や、造影CT画像に対する処理であれば動脈相や遅延相など複数の時相が存在するためどのデータに対して画像処理を実行するか、などが選択肢になるため、放射線科医をはじめとするユーザは各自が最適と判断した方法やパラメータを用いて定量値を算出することになる。 The tumor size of cancer is one of the important factors related to determination of stage of disease and decision of treatment policy as well as tumor properties and position information. By processing examination image data as 3D volume data and executing a series of image processing, it is possible to calculate the volume of the tumor and to calculate the maximum major axis and the maximum minor axis of the tumor in three dimensions It becomes. However, in these image quantification value calculation methods, not only the type of image processing function to be executed and the image processing parameters, but also the order of image processing, and the arterial phase and delay phase, etc., can be used for contrast CT images. if time phase executes image processing for any data to present, since the like becomes an option, a quantitative value users using the method and parameters each determines the optimum, including the radiologist Will be calculated.
　上述の一連の画像処理は、まず画像処理命令入力部105から画像処理命令が入力され、次に画像処理実行部107において画像処理が実行され、さらに画像処理実行部107によって実行された画像処理結果は画像データ表示部104に表示され、再び画像処理命令入力部105から画像処理命令が入力され画像処理が実行されることによって行われる。腫瘍の3次元的定量化においては腫瘍の体積、最大長径、最大短径などを算出することが画像処理フローの目的かつ出力データであるが、これらの定量値算出を以って画像処理フローが完了する。 In the series of image processing described above, first, an image processing command is input from the image processing command input unit 105, then image processing is executed in the image processing execution unit 107, and the image processing result executed by the image processing execution unit 107 Is displayed on the image data display unit 104, and an image processing command is input from the image processing command input unit 105 again to execute image processing. In the three-dimensional quantification of tumors, calculating the tumor volume, maximum major axis, maximum minor axis, etc. is the purpose and output data of the image processing flow. Complete.
　図２のフローチャートは、複数の画像処理が連続的に実行される画像処理フローの一例である。古典的肝細胞癌の腫瘍は造影CTの動脈相で高濃度を呈し、遅延相で低濃度となる。ここで示す画像処理フローは腫瘍の定量化を目的として、この2つの時相データを入力画像としてシステムに読み込み（201）、レジストレーション（202）を実行後、差分画像を生成し（203）、ある閾値で二値化するフィルタリング（204）を実行後、肝腫瘍領域を抽出（205）し、最後に腫瘍の体積、最大長径、最大短径などの定量値を算出（206）する、という過程を経る一連の画像処理である。ここで挙げた画像処理フローは個々の画像処理機能の組み合わせの一例であり、ユーザは自由自在に画像処理機能を組み合わせたり、あるいは個々の画像処理のパラメータを設定したりすることが可能である。 The flowchart in FIG. 2 is an example of an image processing flow in which a plurality of image processes are continuously executed. Classic hepatocellular carcinoma tumors show high concentrations in the arterial phase of contrast-enhanced CT and low concentrations in the delayed phase. The image processing flow shown here reads these two temporal data into the system as input images for the purpose of tumor quantification (201), performs registration (202), generates a difference image (203), After performing filtering (204) to binarize at a certain threshold, extract the liver tumor area (205), and finally calculate (206) quantitative values such as tumor volume, maximum major axis, and maximum minor axis It is a series of image processing that goes through. The image processing flow described here is an example of a combination of individual image processing functions, and the user can freely combine the image processing functions or set parameters of individual image processing.
　図３に、図２の画像処理フローと対応した個々の画像処理履歴のデータ構造を示す。個々の画像処理履歴には画像処理実行直前の画像データ301、画像処理実行直後の画像データ302、画像処理パラメータ303、画像処理演算時間304、自身のデータサイズ305などがデータ項目として含まれ得る。画像処理演算時間304は、代わりに画像処理演算開始時刻と終了時刻を記録しておいてもよい。この例では、図３のNo.1～5のレコードの集合と、最後の画像処理である定量値算出の結果得られる画像定量値を合わせたデータが画像処理フローのデータとなる。 FIG. 3 shows the data structure of each image processing history corresponding to the image processing flow of FIG. Each image processing history may include image data 301 immediately before execution of image processing, image data 302 immediately after execution of image processing, image processing parameters 303, image processing calculation time 304, own data size 305, and the like as data items. As the image processing calculation time 304, the image processing calculation start time and end time may be recorded instead. In this example, data obtained by combining the set of records No. 1 to No. 5 in FIG. 3 and the image quantitative value obtained as a result of the quantitative value calculation which is the last image processing becomes the data of the image processing flow.
　個々の画像処理が実行されると、後述するデータ構造をもつ画像処理履歴として画像処理フロー記録部108に記録される。一連の画像処理が完了すると、画像処理フロー記録部108に記録されていた画像処理履歴は画像定量値とともに画像処理フローとして記憶部109に記憶される。 When individual image processing is executed, it is recorded in the image processing flow recording unit 108 as an image processing history having a data structure to be described later. When a series of image processing is completed, the image processing history recorded in the image processing flow recording unit 108 is stored in the storage unit 109 as an image processing flow together with the image quantitative value.
　画像処理フローのデータサイズをなるべく小さくすることを考慮すると、上述のデータ項目のうち中間画像データは保存をしない方が好ましいが、中間画像データを保存しておかないと画像処理フロー再生時に画像処理フロー再生部106にてその画像処理を再度実行する必要が生じるため再生に要する時間は長くなる。逆に中間画像データを保存しておけば画像処理を再度実行する必要はなく、画像処理フローの再生を速やかに行うことが可能であるがデータサイズは大きくなってしまう。 Considering to reduce the data size of the image processing flow as much as possible, it is preferable not to save the intermediate image data among the above-mentioned data items. However, if the intermediate image data is not saved, image processing will not be performed during playback. Since it is necessary to execute the image processing again in the flow reproduction unit 106, the time required for reproduction becomes long. Conversely, if the intermediate image data is stored, it is not necessary to execute the image processing again, and the image processing flow can be reproduced promptly, but the data size becomes large.
　そこで、最適化実行部110は、画像処理フローから必要に応じて中間画像を間引き、記憶部109に記憶する。前述したように画像データを画像処理フローのデータとして保存するか否かは記憶部のストレージ容量節約と画像処理フローの再生速度向上とのトレードオフであり、医療機関ごとに事情が異なる。さらに、記憶部のストレージ容量増設等の環境変化に伴い画像処理フローの保存方針の変更が必要となることも十分に想定される。したがって、医療機関ごとの事情を考慮した画像処理フローの保存形式を最適化し、またカスタマイズ可能な柔軟な機構が必要となる。これを実現しているのが最適化実行部110である。 Therefore, the optimization execution unit 110 thins out intermediate images as necessary from the image processing flow, and stores them in the storage unit 109. As described above, whether or not the image data is stored as image processing flow data is a trade-off between saving storage capacity of the storage unit and improving the reproduction speed of the image processing flow, and the circumstances differ for each medical institution. Furthermore, it is sufficiently assumed that the storage policy of the image processing flow needs to be changed with an environmental change such as an increase in storage capacity of the storage unit. Therefore, a flexible mechanism that optimizes the storage format of the image processing flow considering the circumstances of each medical institution and can be customized is required. This is implemented by the optimization execution unit 110.
　図４は、最適化実行部110で行われる画像処理フロー最適化の一例を示すフローチャートである。このフローチャートでは画像処理フローを快適に再生するため、画像処理フローの再生速度向上に重点を置いた最適化を実行している。まず、最適化すべき画像処理フローの総演算時間を算出する。総演算時間は、図３で示したデータ項目のうち、個々の画像処理演算時間を全て加算することで算出することができる。次に、算出された総演算時間が、あらかじめ規定しておいた規定時間を超過しているか否かの判定を行う。超過している場合には、総演算時間が規定時間内に収まるまで中間画像データを1つずつ削除していく。その時、画像演算時間がより短時間で完了した画像処理の中間画像データから優先的に削除していくことで、画像処理フロー再生時に再生時間を規定時間内に収め、かつ、データサイズを最小にすることができる。なお、本発明は、中間画像データを1つずつ削除せずに、数個一緒に削除する形態でもよい。本発明のように、画像演算時間が短い中間画像データから削除し、画像演算時間が長い中間画像データは残すようにすることにより、演算時間を短くし、かつ、サーバの容量も少なくて済む診断支援システムを提供可能となる。 FIG. 4 is a flowchart illustrating an example of image processing flow optimization performed by the optimization execution unit 110. In this flowchart, in order to comfortably reproduce the image processing flow, optimization is performed with an emphasis on improving the reproduction speed of the image processing flow. First, the total calculation time of the image processing flow to be optimized is calculated. The total calculation time can be calculated by adding all the individual image processing calculation times among the data items shown in FIG. Next, it is determined whether or not the calculated total calculation time exceeds a prescribed time that has been prescribed in advance. If it exceeds, the intermediate image data is deleted one by one until the total calculation time falls within the specified time. At that time, by preferentially deleting from the intermediate image data of the image processing that has completed the image calculation time in a shorter time, the reproduction time is kept within the specified time during the image processing flow reproduction, and the data size is minimized. can do. Note that the present invention may be configured to delete several pieces of intermediate image data together without deleting them one by one. As in the present invention, by deleting from the intermediate image data having a short image calculation time and leaving the intermediate image data having a long image calculation time, the calculation time can be shortened and the server capacity can be reduced. A support system can be provided.
　図５は、図４と同様に最適化実行部110で行われる画像処理フロー最適化の一例を示すフローチャートである。このフローチャートでは画像処理フローのデータサイズをより小さくすることで、ストレージ容量を節約することに重点を置いた最適化を実行している。この最適化においては、総ファイルサイズが規定ファイルサイズ内に収まるまで中間画像データを1つずつ削除していく。 FIG. 5 is a flowchart showing an example of image processing flow optimization performed by the optimization execution unit 110 as in FIG. In this flowchart, optimization is performed with an emphasis on saving storage capacity by reducing the data size of the image processing flow. In this optimization, intermediate image data is deleted one by one until the total file size falls within the specified file size.
　あるいは、記憶部のストレージ容量節約と画像処理フローの再生速度向上とのバランスを重視し、最適化フローチャートの分岐条件として総演算時間と総ファイルサイズの両方に条件を規定してもよい。 Alternatively, emphasizing the balance between the storage capacity saving of the storage unit and the improvement of the reproduction speed of the image processing flow, conditions may be defined for both the total calculation time and the total file size as branching conditions of the optimization flowchart.
　画像処理フロー再生部106において画像処理フローの再生は、再生する画像処理フローの中間画像を時系列順に提示することで実行される。最適化実行部110によって削除された中間画像に関しては、画像処理実行部107によって再度画像処理を実行すればよい。 In the image processing flow reproduction unit 106, reproduction of the image processing flow is executed by presenting intermediate images of the image processing flow to be reproduced in chronological order. For the intermediate image deleted by the optimization execution unit 110, the image processing execution unit 107 may execute image processing again.
　本実施例における本発明の効果は、3次元画像データに対する画像処理フローを適切な形式で保存しておくことによって、画像処理フローを快適に再生できることにある。カンファレンスにおける症例検討の場において、あるいは他の医師が行った画像処理について確認したい場面において効果的である。 The effect of the present invention in the present embodiment is that the image processing flow can be comfortably reproduced by storing the image processing flow for the three-dimensional image data in an appropriate format. It is effective in a case study case at a conference or in a scene where it is desired to confirm image processing performed by another doctor.
　101　画像データ入出力部
　102　ネットワーク
　103　クライアント領域
　104　画像データ表示部
　105　画像処理命令入力部
　106　画像処理フロー再生部
　107　画像処理実行部
　108　画像処理フロー記録部
　109　記憶部
　110　最適化実行部
　201　3次元画像データ読み込み処理
　202　レジストレーション処理
　203　差分処理
　204　フィルタリング処理
　205　セグメンテーション処理
　206　定量値算出処理
　301　画像処理実行直前の中間画像データ
　302　画像処理実行直後の中間画像データ
　303　画像処理パラメータ
　304　画像処理演算時間
　305　データサイズ 101 Image data input / output unit 102 Network 103 Client area 104 Image data display unit 105 Image processing command input unit 106 Image processing flow playback unit 107 Image processing execution unit 108 Image processing flow recording unit 109 Storage unit 110 Optimization execution unit 201 3D Image data reading processing 202 Registration processing 203 Difference processing 204 Filtering processing 205 Segmentation processing 206 Quantitative value calculation processing 301 Intermediate image data immediately before execution of image processing 302 Intermediate image data immediately after execution of image processing 303 Image processing parameters 304 Image processing calculation time 305 Data size

Claims (7)

  1. 　画像データを入出力する画像データ入出力部と、
    前記画像データ入出力部から入力された画像を表示する画像データ表示部と、
    前記画像データ表示部に表示された画像データに対する画像処理命令を入力する画像処理命令入力部と、
    前記画像処理命令入力部から入力された画像処理を実行する画像処理実行部と、
    前記画像処理実行部によって実行される一連の画像処理を画像処理フローとして記録する画像処理フロー記録部と、
    前記画像処理フローを記憶する記憶部と、
    前記記憶部から前記画像処理フローを受け取り再生する画像処理フロー再生部と、
    を備えることを特徴とする診断支援システム。     An image data input / output unit for inputting / outputting image data;
    An image data display unit for displaying an image input from the image data input / output unit;
    An image processing command input unit for inputting an image processing command for the image data displayed on the image data display unit;
    An image processing execution unit for executing image processing input from the image processing command input unit;
    An image processing flow recording unit that records a series of image processing executed by the image processing execution unit as an image processing flow;
    A storage unit for storing the image processing flow;
    An image processing flow reproduction unit that receives and reproduces the image processing flow from the storage unit;
    A diagnostic support system comprising:
  2. 　前記画像処理フローは、個々の画像処理を実行する直前の中間画像あるいは直後の中間画像あるいは画像処理パラメータの少なくともいずれか１以上を含む個々の画像処理履歴を順列に保持することを特徴とする、請求項１記載の診断支援システム。 The image processing flow is characterized in that individual image processing histories including at least one of the immediately preceding intermediate image, the immediately following intermediate image, or the image processing parameters are executed in a permutation. The diagnosis support system according to claim 1.
  3. 　前記記憶部に記憶された前記画像処理フローに対し、前記画像処理フローの最適化を実行する最適化実行部をさらに備え、前記最適化実行部は前記中間画像を１ずつ削除することで最適化を実行することを特徴とする、請求項２記載の診断支援システム。 The image processing flow stored in the storage unit further includes an optimization execution unit that performs optimization of the image processing flow, and the optimization execution unit optimizes by deleting the intermediate image one by one The diagnosis support system according to claim 2, wherein:
  4. 　前記最適化実行部は、前記画像処理フローの総演算時間を算出し、前記総演算時間が規定時間内に収まるように、あるいは前記画像処理フローのデータサイズを算出し前記データサイズが規定データサイズ内に収まるように、前記画像処理フローの中間画像を１ずつ削除することを特徴とする、請求項３記載の診断支援システム。 The optimization execution unit calculates a total calculation time of the image processing flow, and calculates the data size of the image processing flow so that the total calculation time is within a specified time, or the data size is a specified data size. The diagnosis support system according to claim 3, wherein intermediate images of the image processing flow are deleted one by one so as to be within a range.
  5. 　前記画像処理パラメータとは、前記画像処理に要した時間である画像処理演算時間ことを特徴とする請求項２または３に記載の診断支援システム。 4. The diagnosis support system according to claim 2, wherein the image processing parameter is an image processing calculation time which is a time required for the image processing.
  6. 　前記最適化実行部は、前記画像処理演算時間が短い画像から、前記中間画像を削除することを特徴とする請求項５に記載の診断支援システム。 The diagnosis support system according to claim 5, wherein the optimization execution unit deletes the intermediate image from the image having a short image processing calculation time.
  7. 　請求項１から６に記載の診断支援システムを、動作するプログラム。 A program for operating the diagnosis support system according to claim 1.
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