WO2023032437A1 - 造影状態判別装置、造影状態判別方法、及びプログラム - Google Patents

造影状態判別装置、造影状態判別方法、及びプログラム Download PDF

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WO2023032437A1
WO2023032437A1 PCT/JP2022/025287 JP2022025287W WO2023032437A1 WO 2023032437 A1 WO2023032437 A1 WO 2023032437A1 JP 2022025287 W JP2022025287 W JP 2022025287W WO 2023032437 A1 WO2023032437 A1 WO 2023032437A1
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contrast
image
state determination
images
elapsed time
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French (fr)
Japanese (ja)
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圭太 尾谷
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Fujifilm Corp
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Fujifilm Corp
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06T7/0014—Biomedical image inspection using an image reference approach
    • G06T7/0016—Biomedical image inspection using an image reference approach involving temporal comparison
    • 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]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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 OR CALCULATING; 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 OR CALCULATING; 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 OR CALCULATING; 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
    • G06T2207/30056—Liver; Hepatic
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06T2207/30084—Kidney; Renal

Definitions

  • the present invention relates to a contrast state determination device, a contrast state determination method, and a program for determining a contrast state from a contrast-enhanced image, and more particularly to a technique for determining a contrast state from a two-dimensional image.
  • Dynamic contrast-enhanced CT images taken by CT (computed tomography) examinations using a contrast agent differ greatly in appearance depending on the contrast-enhanced time phase. is necessary.
  • DICOM Digital Imaging and COmmunications in Medicine
  • tags sometimes contain contrast information, but they do not always contain it, and sometimes they are wrong. Therefore, it is required to grasp the contrast-enhanced time phase from the image.
  • Contrast time phase information is required for later processing that depends on the contrast time phase, and high-speed operation is required.
  • the technique disclosed in Non-Patent Document 1 has a problem that it takes time because the contrast-enhanced image is processed as it is in 3D (Dimension). Further, the technique disclosed in Patent Document 1 has a problem that the contrast-enhanced time phase cannot be determined when a specific region is not shown in the contrast-enhanced image.
  • the present invention has been made in view of such circumstances. It aims at providing a method and a program.
  • the contrast state determination device provides a first image containing information of at least a first slice image of a first position of the subject from a first image series captured before or after injection of a contrast agent into the subject. obtaining a second two-dimensional image including information of the two-dimensional image and a second slice image at a second location different from the first location, and obtaining a first indication of an imaging condition from the first two-dimensional image; estimating a value, estimating a second indicator value for the contrast condition from the second two-dimensional image, and determining the contrast condition of the first image series based on at least the first indicator value and the second indicator value.
  • the contrast-enhanced state determination device further generates a third two-dimensional image including information of a third slice image at a third position different from the first position and the second position of the subject from the first image series. obtained, and estimating a third index value for the contrast condition from the third two-dimensional image, and determining the contrast condition of the first image series based on the first to third index values.
  • the index value is a likelihood of belonging to each imaging state among a plurality of imaging states, and at least one processor derives an index value by integrating the plurality of index values, and calculates a first index value based on the integrated index value. image series to determine the imaging status.
  • a first learning model that, when a two-dimensional image based on a series of images taken before or after injection of a contrast medium into a subject is input, outputs a likelihood of belonging to each contrast enhancement state among a plurality of contrast enhancement states. and the at least one processor preferably inputs the plurality of two-dimensional images into the first learning model to estimate the plurality of index values.
  • the index value is the elapsed time after injection of the contrast agent into the subject, and at least one processor derives the elapsed time by integrating the plurality of elapsed times that are the plurality of index values, and calculates the integrated elapsed time
  • the imaging status of the first image series is determined based on.
  • the index value is the elapsed time and the likelihood of the elapsed time after the injection of the contrast agent into the subject, and at least one processor calculates the index value based on the plurality of likelihoods of the plurality of index values. It is preferable to derive an aggregated elapsed time from a plurality of elapsed times and to determine the imaging status of the first image series based on the aggregated elapsed time.
  • At least one processor preferably derives the integrated elapsed time based on the product of a plurality of probability distribution models with elapsed time and probability as parameters.
  • the elapsed time after the injection of the contrast medium into the subject and the likelihood of the elapsed time are output.
  • a conversion table in which the elapsed time after injection of the contrast agent into the subject and the contrast enhancement state are associated with each other, and at least one processor uses a plurality of index values after the injection of the contrast agent into the subject. estimating the elapsed time; determining a plurality of contrast conditions for the first image series based on the plurality of elapsed times and the conversion table; and determining a contrast condition for the first image series based on the plurality of contrast conditions. preferable.
  • the two-dimensional image is preferably at least one of a slice image, a MIP (Maximum Intensity Projection) image of a plurality of slice images, and an average image of the plurality of slice images.
  • MIP Maximum Intensity Projection
  • the first image series are three-dimensional images including the subject's liver, and the contrast-enhanced state includes at least one of non-enhanced, arterial phase, portal vein phase, and equilibrium phase.
  • the first image series is a three-dimensional image including the subject's kidney, and the contrast-enhancing state preferably includes at least one of non-enhancement, corticomedullary phase, parenchymal phase, and excretory phase.
  • One aspect of the contrast enhancement state determination method for achieving the above object is to extract information on slice images at different positions of the subject from a first image series captured before or after injection of a contrast agent into the subject. obtaining a plurality of two-dimensional images including each of the plurality of two-dimensional images; estimating an index value relating to a contrast-enhanced state from each of the plurality of two-dimensional images; and determining the contrast state of the series.
  • One aspect of the program for achieving the above object is a program for causing a computer to execute the above contrast enhancement state determination method.
  • a computer-readable non-transitory storage medium in which this program is recorded may also be included in this embodiment.
  • the present invention it is possible to determine the contrast-enhanced state at high speed, with high accuracy, and robustness, even if there are organs not included in the image.
  • FIG. 1 is a conceptual diagram showing an outline of processing by the contrast enhancement state determination device according to the first embodiment.
  • FIG. 2 is a flow chart showing a contrast state determination method by the contrast state determination device.
  • FIG. 3 is a block diagram schematically showing an example of the hardware configuration of the imaging state determination apparatus according to the first embodiment;
  • FIG. 4 is a functional block diagram showing an overview of processing functions of the contrast enhancement state determination apparatus according to the first embodiment.
  • FIG. 5 is a conceptual diagram showing an overview of processing by the contrast enhancement state determination device according to the second embodiment.
  • FIG. 6 is a functional block diagram showing an overview of the processing functions of the contrast enhancement state determination device according to the second embodiment.
  • FIG. 7 is a conceptual diagram showing an overview of processing by the contrast enhancement state determination device according to the third embodiment.
  • FIG. 8 is an explanatory diagram showing an example of processing in the number-of-seconds distribution estimation unit.
  • FIG. 9 is a graph of a function used for variable transformation in Equation (2).
  • FIG. 10 is a graph of the number-of-seconds distribution estimated by the parameters estimated by the number-of-seconds distribution estimator.
  • FIG. 11 is an explanatory diagram of an example of processing in the integrating unit and the maximum point specifying unit.
  • FIG. 12 is a schematic illustration of an example of a machine learning method for generating a regression model applied to the seconds distribution estimator.
  • FIG. 13 is an explanatory diagram of a loss function used during training.
  • FIG. 14 is a functional block diagram showing an overview of the processing functions of the imaging state determination device 10 according to the third embodiment.
  • FIG. 15 is an explanatory diagram showing an example of processing in the second distribution estimating unit of the imaging state determination apparatus according to the fourth embodiment.
  • FIG. 16 is a graph of the number-of-seconds distribution estimated by the parameters estimated by the number-of-seconds distribution estimator.
  • FIG. 17 is an explanatory diagram showing an example of processing in the integration unit and the maximum point specifying unit of the contrast enhancement state determination apparatus according to the fourth embodiment.
  • FIG. 18 is an explanatory diagram schematically showing an example of a machine learning method for generating a regression model applied to the number-of-seconds distribution estimator in the fourth embodiment.
  • FIG. 19 is an explanatory diagram showing Modified Example 1 of data used for input to the contrast enhancement state determination device.
  • FIG. 20 is an explanatory diagram showing Modified Example 2 of data used for input to the contrast enhancement state determination device.
  • FIG. 21 is a block diagram showing a configuration example of a medical information system to which the imaging state determination device is applied.
  • FIG. 1 is a conceptual diagram showing an outline of processing by the contrast enhancement state determination device 10 according to the first embodiment.
  • the contrast enhancement state determination device 10 for estimating the contrast enhancement state (contrast enhancement time phase) of an image series based on a plurality of slice images of the same image series contrast-enhanced using a CT (Computed Tomography) device will be described. do.
  • CT Computerputed Tomography
  • a plurality of slice images in the same series are used as inputs to estimate the contrast-enhanced state by image analysis.
  • “By image analysis” means by processing based on pixel values that constitute image data.
  • the imaging state of liver imaging includes at least one of non-imaging, arterial phase (early arterial phase/late arterial phase), portal vein phase, and equilibrium phase.
  • the imaging state of renal imaging includes at least one of non-imaging, corticomedullary phase, parenchymal phase, and excretion phase.
  • the contrast enhancement state determination device 10 can be realized using computer hardware and software. As shown in FIG. 1 , the contrast enhancement state determination device 10 includes a time phase estimation section 30 and an integration section 32 .
  • a plurality of slice images are input to the contrast enhancement state determination device 10 .
  • the plurality of slice images are images sampled at equal intervals from the 3D CT data of the same image series taken before or after the injection of the contrast agent into the patient SB (an example of the “subject”).
  • the contrast enhancement state determination device 10 has three images IM1, IM2, and IM3 at different positions SBP1, SBP2, and SBP3 of the patient SB (an example of "a plurality of slice images", An example of "a plurality of two-dimensional images" is input.
  • the slice image may also be called a tomographic image.
  • a slice image may be understood as a substantially two-dimensional image (cross-sectional image).
  • the time phase estimating unit 30 estimates an index value (an example of an “index value relating to contrast-enhanced state”) that is the likelihood that an image series of input images belongs to each contrast-enhanced time phase among a plurality of contrast-enhanced time phases. .
  • the numerical range of the probability of belonging to each contrast-enhanced time phase output from the time phase estimation unit 30 may be “0% to 100%”.
  • three phase estimating units 30 are illustrated in order to show the flow of processing when three different images IM1, IM2, and IM3 are input.
  • the temporal estimator 30 used is the same (single) processor.
  • the phase estimation unit 30 includes a learned model 30A (an example of a "first learning model") trained by machine learning.
  • the trained model 30A determines whether the image series of the input two-dimensional image belongs to each contrast-enhanced time phase among a plurality of contrast-enhanced time phases. It is a multi-class classification model that outputs likelihood.
  • the trained model 30A is configured using, for example, a convolutional neural network (CNN).
  • CNN convolutional neural network
  • the trained model 30A is trained by a known method using pairs of two-dimensional images based on a series of images taken before or after injection of a contrast agent into the subject and the contrast-enhanced time phases of the series of images as learning data. there is
  • the temporal estimation unit 30 inputs the input two-dimensional images to the trained model 30A and estimates the index value of each image.
  • the temporal phase estimating unit 30 determines that the index value of the image IM1 has a probability of 10% belonging to the arterial phase among the plurality of imaging states, and a probability of belonging to the portal vein phase of 50%. , and outputs an estimation result PS1 with a probability of belonging to the equilibrium phase of 40%. Further, the temporal phase estimating unit 30 determines that the index values of the image IM2 are 5% for the probability of belonging to the arterial phase, 80% for the probability of belonging to the portal phase, and 15% for the probability of belonging to the equilibrium phase. is output as the estimation result PS2. In addition, the temporal phase estimating unit 30 determines that the index values of the image IM3 are 30% likely to belong to the arterial phase, 35% likely to belong to the portal vein phase, and 35% likely to belong to the equilibrium phase. is output as the estimation result PS3.
  • the integration unit 32 derives an index value by integrating a plurality of input index values, and determines the contrast enhancement state of the image series.
  • the integrating unit 32 may integrate the plurality of index values by using the maximum value, may integrate the plurality of index values by majority rule, or may integrate the plurality of index values by probabilistic sum.
  • the index value integrated by the integration unit 32 is output as the final estimation result.
  • the integration unit 32 integrates the estimation result PS1, the estimation result PS2, and the estimation result PS3, and outputs the portal vein phase as the final estimation result.
  • ⁇ Description of medical images used for input In the DICOM (Digital Imaging and COmmunications in Medicine) standard that defines the format and communication protocol of medical images, a unit called a study ID, which is an identification code (ID) for specifying the type of examination, , the series ID is defined.
  • DICOM Digital Imaging and COmmunications in Medicine
  • CT imaging of a range including the liver is performed a plurality of times (here, four times) at different imaging timings as described below.
  • CT data is three-dimensional data composed of a plurality of continuous slice images (tomographic images), and is an aggregate of a plurality of slice images (continuous slices) that constitute the three-dimensional data.
  • a set of images) is called an “image series”.
  • CT data is an example of a "three-dimensional image" in this disclosure.
  • “study 1” is given as a study ID for a specific patient's liver contrast imaging examination
  • “series 1” is given as the series ID of CT data obtained by imaging before contrast medium injection
  • “series 1” is given as a series ID for CT data obtained by imaging before "Series 2" for CT data obtained by imaging 35 seconds after injection
  • "Series 3" for CT data obtained by imaging 70 seconds after injection of contrast agent, 180 seconds after injection of contrast injection
  • a unique ID is assigned to each series, such as "series 4", to the CT data obtained by imaging. Therefore, CT data can be identified by a combination of study ID and series ID.
  • CT data can be identified by a combination of study ID and series ID.
  • the correspondence relationship between the series ID and the imaging timing elapsed time after injection of the contrast medium
  • the three-dimensional CT data has a large data size, it may be difficult to use the CT data as it is as input data to perform processing for determining the contrast enhancement state.
  • FIG. 2 is a flow chart showing each step of the contrast state determination method by the contrast state determination device 10. As shown in FIG. Here, an example of determining the contrast-enhanced state of a series of liver contrast-enhanced images will be described.
  • step S1 the contrast state determining apparatus 10 selects a position SBP1 of the patient SB (a "first An image IM1 (an example of a 'first slice image', an example of a 'first two-dimensional image') is acquired at a position).
  • step S2 the phase estimating unit 30 inputs the image IM1 acquired in step S1 to the learned model 30A to estimate the first index value regarding the contrast enhancement state.
  • the time phase estimating unit 30 uses the probability of non-contrast enhancement (likelihood of non-enhancement), the probability of arterial phase (likelihood of artery phase), the probability of portal vein phase (likelihood of portal vein phase) as the first index value. , and the probability of being in the equilibrium phase (likelihood of equilibrium phase) are obtained and output as the estimation result PS1.
  • step S3 the contrast-enhanced state determination apparatus 10 extracts an image IM2 (an example of a “second slice image”, a “second (an example of a two-dimensional image of ).
  • the contrast enhancement state determination device 10 acquires the image IM3, and the phase estimation unit 30 outputs an estimation result PS3 as an index value related to the contrast enhancement state from the image IM3.
  • step S5 the integration unit 32 determines the contrast enhancement state of the image series based on the estimation results PS1 to PS3, and outputs the determined contrast enhancement state as the final estimation result.
  • the contrast time phase is determined based on a plurality of two-dimensional images, so the contrast time phase is determined based on one two-dimensional image. It is more accurate and robust than the case. Also, the inference is faster than when determining the contrast-enhanced time phase based on the three-dimensional image. Furthermore, since the estimation is not performed based on a specific region, estimation is possible even if there are organs not shown in the image.
  • the contrast-enhanced condition determining apparatus 10 determines the contrast-enhanced time phase based on the three images IM1, IM2, and IM3, but may determine the contrast-enhanced time phase based on two images.
  • the contrast-enhanced time phase may be determined based on more than one image. The smaller the number of images, the faster the processing, and the larger the number of images, the higher the precision and robustness of the determination result.
  • FIG. 3 is a block diagram schematically showing an example of the hardware configuration of the imaging state determination device 10 according to the first embodiment.
  • the imaging state determination apparatus 10 can be implemented by a computer system configured using one or several computers. Here, an example in which one computer executes a program to realize various functions of the imaging state determination apparatus 10 will be described.
  • the form of the computer functioning as the imaging state determination apparatus 10 is not particularly limited, and may be a server computer, a workstation, a personal computer, a tablet terminal, or the like.
  • the imaging state determination device 10 includes a processor 102 , a non-transitory tangible computer-readable medium 104 , a communication interface 106 , an input/output interface 108 and a bus 110 .
  • the processor 102 includes a CPU (Central Processing Unit). Processor 102 may include a GPU (Graphics Processing Unit). Processor 102 is coupled to computer-readable media 104 , communication interface 106 , and input/output interface 108 via bus 110 . The processor 102 reads various programs and data stored in the computer-readable medium 104 and executes various processes.
  • CPU Central Processing Unit
  • GPU Graphics Processing Unit
  • the computer-readable medium 104 includes, for example, a memory 104A that is a main storage device and a storage 104B that is an auxiliary storage device.
  • the storage 104B is configured using, for example, a hard disk drive (HDD) device, a solid state drive (SSD) device, an optical disk, a magneto-optical disk, or a semiconductor memory, or an appropriate combination thereof. .
  • HDD hard disk drive
  • SSD solid state drive
  • Various programs, data, and the like are stored in the storage 104B.
  • Computer-readable medium 104 is an example of a "storage device" in this disclosure.
  • the memory 104A is used as a work area for the processor 102, and is used as a storage unit that temporarily stores programs and various data read from the storage 104B.
  • a program stored in the storage 104B is loaded into the memory 104A, and the processor 102 executes the instructions of the program, whereby the processor 102 functions as means for performing various processes defined by the program.
  • the memory 104A stores an imaging state determination program 130 executed by the processor 102, various data, and the like.
  • the contrast enhancement state determination program 130 includes a trained model 30A (see FIG. 1) trained by machine learning, and causes the processor 102 to execute the processes described with reference to FIGS.
  • the communication interface 106 performs wired or wireless communication processing with an external device, and exchanges information with the external device.
  • the imaging state determination device 10 is connected to a communication line (not shown) via a communication interface 106 .
  • the communication line may be a local area network or a wide area network.
  • the communication interface 106 can serve as a data acquisition unit that receives input of data such as images.
  • the imaging state determination device 10 may further include an input device 114 and a display device 116 .
  • Input device 114 and display device 116 are connected to bus 110 via input/output interface 108 .
  • Input device 114 may be, for example, a keyboard, mouse, multi-touch panel, or other pointing device, voice input device, or any suitable combination thereof.
  • the display device 116 is an output interface that displays various information.
  • the display device 116 may be, for example, a liquid crystal display, an organic electro-luminescence (OEL) display, a projector, or an appropriate combination thereof.
  • OEL organic electro-luminescence
  • FIG. 4 is a functional block diagram showing an overview of the processing functions of the imaging state determination device 10 according to the first embodiment.
  • the processor 102 of the contrast state determination device 10 functions as the data acquisition unit 12, the phase estimation unit 30, the integration unit 32, and the output unit 19 by executing the contrast state determination program 130 stored in the memory 104A.
  • the data acquisition unit 12 accepts input of data to be processed.
  • the data acquisition unit 12 acquires an image IMi, which is a slice image sampled from CT data.
  • the data acquisition unit 12 may perform processing for cutting out slice images from CT data at regular intervals, or may acquire slice images sampled in advance by a processing unit (not shown) or the like.
  • the image IMi captured via the data acquisition unit 12 is input to the phase estimation unit 30 .
  • the output unit 19 is an output interface for displaying the contrast enhancement state estimated by the integration unit 32 and providing it to other processing units.
  • the output unit 19 may include a processing unit such as processing for generating data for display and/or data conversion processing for transmitting data to the outside.
  • the contrast time phase estimated by the contrast state determination device 10 may be displayed on a display device (not shown) or the like.
  • the contrast enhancement state determination device 10 may be incorporated in a medical image processing device for processing medical images acquired in medical institutions such as hospitals. Further, the processing functions of the imaging state determination apparatus 10 may be provided as a cloud service.
  • the method of contrast-enhanced state determination processing executed by the processor 102 is an example of the “contrast-enhanced state determination method” in the present disclosure.
  • the probability of belonging to each contrast phase out of a plurality of contrast phases is used as the index value related to the contrast enhancement state.
  • An example using the elapsed time after the time will be described.
  • the hardware configuration of the imaging state determination device 10 according to the second embodiment may be the same as that of the first embodiment. Regarding the second embodiment, points different from the first embodiment will be described.
  • FIG. 5 is a conceptual diagram showing an outline of processing by the contrast enhancement state determination device 10 according to the second embodiment.
  • FIG. 6 is a functional block diagram showing an overview of the processing functions of the imaging state determination device 10 according to the second embodiment.
  • a plurality of slice images sampled at equal intervals from three-dimensional CT data of a patient whose liver has been contrast-enhanced using a CT apparatus is used as an input, and from the image series of the input plurality of slice images, a contrast agent is injected.
  • An example of the contrast enhancement state determination device 10 that estimates the number of seconds and determines the contrast enhancement state based on the estimated number of seconds will be described.
  • the term "seconds" in this specification includes the number of seconds indicating the elapsed time from the injection of the contrast medium, unless explicitly stated otherwise.
  • the contrast-enhanced state determination apparatus 10 receives an input of an image IM, and estimates the number of seconds as the elapsed time from the injection of the contrast agent in the image series of the images IM. , an integrating unit 36 for deriving an integrated elapsed time from a plurality of input elapsed times, and a discriminating unit 38 for discriminating a contrast-enhanced time phase from the derived elapsed time.
  • the contrast-enhanced time phase determined by the determination unit 38 is output from the output unit 19 as a final result.
  • Unit 34 is the same (single) processing unit.
  • Seconds estimator 34 estimates the number of seconds from contrast agent injection of the image series of the input images.
  • the number-of-seconds estimation unit 34 includes a learned model 34A trained by machine learning.
  • the trained model 34A When the trained model 34A is input with a two-dimensional image based on a contrast-enhanced three-dimensional image, it outputs the number of seconds from the injection of the contrast agent in the image series of the input two-dimensional image.
  • the trained model 34A is constructed using, for example, a convolutional neural network.
  • the trained model 34A acquires pairs of two-dimensional images based on a series of images taken before or after injection of a contrast agent into the subject and the number of seconds from the injection of the contrast agent in the image series as learning data by a known method. being learned.
  • the number-of-seconds estimator 34 inputs the input two-dimensional image to the trained model 34A and estimates the elapsed time after injection of the contrast agent in the image series of the two-dimensional image.
  • the number-of-seconds estimating unit 34 outputs an estimation result PS11 (an example of the "first elapsed time") of 70 seconds as the index value of the image IM1, and as the index value of the image IM2,
  • An estimation result PS12 (an example of the “second elapsed time”) of 75 seconds is output, and an estimation result PS13 of 80 seconds is output as the index value of the image IM3.
  • the integration unit 36 integrates a plurality of input estimation results using any statistical method, and estimates the number of seconds from the injection of the contrast agent in the image series.
  • the integration unit 36 may integrate the estimation results by, for example, simple averaging, may integrate the estimation results by weighted averaging, or select one of a plurality of estimation results and integrate the estimation results. You may In the example shown in FIG. 5, the integration unit 36 integrates the estimation result PS11, the estimation result PS12, and the estimation result PS13, and outputs 72 seconds as the final estimation result PS14.
  • the determination unit 38 determines the contrast enhancement state from the value of the number of seconds integrated by the integration unit 36 .
  • the determination unit 38 includes a conversion table 38A.
  • Conversion table 38A may be stored on computer readable medium 104 .
  • the conversion table 38A associates the elapsed time from the start of injection of the contrast medium with the contrast time phase. For example, in the case of liver contrast imaging, the conversion table 38A is associated with the arterial phase for less than 50 seconds from the start of injection of the contrast agent, the portal vein phase for 50 seconds to less than 120 seconds, and the equilibrium phase for 120 seconds or longer. .
  • the determination unit 38 determines the contrast enhancement state using the conversion table 38A. In the example shown in FIG. 5, the final estimation result PS14 is 72 seconds, so the contrast enhancement state is determined to be the portal vein phase.
  • the determination unit 38 determines the contrast enhancement state of the image series of images IM1 (an example of a “first contrast enhancement state”), the contrast enhancement state of the image series of images IM2 (an example of a “second contrast enhancement state”), and The contrast enhancement state of the image series IM3 is determined, and the determined contrast enhancement state of the image series IM1, the determined contrast enhancement state of the image series IM2, and the contrast enhancement state of the image series IM3 are integrated in the integration unit 36. A final imaging state may be determined.
  • ⁇ Third embodiment>> an example will be described in which the probability distribution of the elapsed time after injection of the contrast agent is used as the index value for the contrast enhancement state.
  • the hardware configuration of the imaging state determination apparatus 10 according to the third embodiment may be the same as that of the first embodiment.
  • FIG. 7 is a conceptual diagram showing an outline of processing by the contrast state determination device 10 according to the third embodiment.
  • the contrast enhancement state determination device 10 that estimates the distribution of the number of seconds after contrast agent injection based on a plurality of input slice images and determines the contrast enhancement state based on the estimated distribution of the number of seconds will be described.
  • the imaging state determination apparatus 10 includes a seconds distribution estimating unit 14 that receives an input of an image IM and estimates a probability distribution of seconds (hereinafter referred to as “seconds distribution”);
  • a maximum It includes a point specifying unit 18 and a discriminating unit 38 that discriminates the contrast-enhanced time phase by associating the specified number of seconds with the contrast-enhanced time phase.
  • the contrast-enhanced time phase converted by the determination unit 38 is output as a final result.
  • FIG. 7 shows three seconds distribution estimating units 14 in order to show the flow of processing when three different images IM are input.
  • the distribution estimator 14 is the same (single) processor.
  • the function of formula (2) is an example of a mapping that converts the likelihood score value Ob to a value b in the positive region.
  • Parameter ⁇ is an example of a “first parameter” in the present disclosure.
  • Parameter b is an example of a "second parameter" in the present disclosure.
  • the Laplace distribution is applied as the probability distribution model of the number-of-seconds distribution.
  • Laplacian distribution is represented by the function of the following equation (3).
  • the reason for converting the likelihood score value Ob to a positive value b is related to applying the Laplace distribution as a probability distribution model for the number of seconds distribution. This is because if the parameter b is a negative value (b ⁇ 0), the Laplace distribution does not hold as a probability distribution, so it is necessary to ensure that the parameter b is a positive value (b>0). .
  • FIG. 10 shows an example of a graph of the number-of-seconds distribution estimated by the parameters ⁇ and b estimated by the number-of-seconds distribution estimation unit 14 .
  • the position indicated by the dashed line GT in the drawing corresponds to the correct number of seconds (correct number of seconds).
  • Estimating a set of the estimated value Oa and the probability score Ob from the input image IM substantially corresponds to estimating the number-of-seconds distribution.
  • the estimated value Oa of the number of seconds is an example of a "random variable" in this disclosure.
  • FIG. 11 is an explanatory diagram showing an example of processing in the integrating section 16 and the maximum point specifying section 18.
  • FIG. 11 To simplify the explanation, an example of integrating two distributions of seconds estimated by the distribution of seconds estimating unit 14 is shown here, but the same applies to the case of integrating three or more distributions of seconds.
  • Graph GL1 in FIG. 11 is an example of logarithmic probability density logP1 obtained by taking the logarithm of probability distribution P1.
  • a graph GD2 shown in the lower left of FIG. 11 is a distribution of seconds (probability distribution P2 ) is an example.
  • the parameter ⁇ 2 is an example of the "second elapsed time”
  • the parameter b2 is an example of the "second likelihood of the second elapsed time”
  • the probability distribution P2 is an example of the "second probability distribution model”.
  • parameters ⁇ 1 and ⁇ 2 are examples of "multiple elapsed times”
  • parameters b1 and b2 are examples of “multiple likelihoods”
  • probability distributions P1 and P2 are examples of "multiple probability distribution models”.
  • a graph GL2 in FIG. 11 is an example of the logarithmic probability density obtained by taking the logarithm of the probability distribution P2.
  • the graph GLS shown on the far right in FIG. 11 is an example of the joint logarithmic probability density that integrates the logarithmic probability density logP1 and the logarithmic probability density logP2.
  • the distribution shown in graph GLS is an example of "integrated distribution" in the present disclosure.
  • the target function of argmin shown on the right side of the equal sign in the second row of Equation (4) corresponds to the loss function during training in machine learning, which will be described later.
  • the right side of the equal sign described in the third row corresponds to the weighted median formula.
  • the parameter bi corresponding to the weight for integration dynamically changes according to the output of the regression estimator 22 .
  • the integrated distribution takes the form of a weighted median.
  • a highly accurate estimated value can be obtained by suppressing the influence of the outlier.
  • FIG. 12 is a schematic explanatory diagram of an example of a machine learning method for generating a regression model applied to the number-of-seconds distribution estimator 14.
  • Training data used for machine learning includes an image TIM as input data and correct data (teacher signal t) corresponding to the input.
  • the image TIM may be a slice image that constitutes an image series of three-dimensional CT data
  • the teacher signal t is a value that indicates the number of seconds (ground truth) from the injection of the contrast medium when the series to which the slice image belongs is captured. It's okay.
  • a plurality of training data are generated by linking the corresponding teacher signal t.
  • "Binding” may also be referred to as correspondence or association.
  • "Training” is synonymous with "learning.”
  • the same teacher signal t may be associated with slices of the same image series. That is, the teacher signal t may be associated with each image series.
  • each slice is associated with a corresponding teacher signal t to generate multiple training data.
  • a set of training data thus generated is used as a training data set.
  • the learning model 20 (an example of the "second learning model") is configured using CNN.
  • the learning model 20 is used in combination with the variable conversion section 24 .
  • the variable conversion unit 24 may be integrally incorporated into the learning model 20 .
  • the learning model 20 When the image TIM read from the training data set is input to the learning model 20, the learning model 20 outputs the estimated value Oa of the number of seconds and the likelihood score Ob.
  • the estimated value Oa and the score value Ob are variable-transformed into the parameter ⁇ and the parameter b of the probability distribution model by the variable transformation unit 24 .
  • the loss function L used during training is defined by the following equation (5).
  • the subscript i is an index that identifies each slice.
  • FIG. 13 is an explanatory diagram of the loss function used during training.
  • the loss function is a negative log-likelihood, which directly optimizes the formula used for regression estimation by learning. Learning maximizes the log-likelihood of the teacher signal t in seconds.
  • a graph for the parameter ⁇ of the loss function shown in Equation (5) is the graph GR ⁇ in FIG.
  • the graph GR ⁇ has a stable slope with respect to the parameter ⁇ .
  • the graph for the parameter b of the loss function shown in Equation (5) is the graph GRb in FIG.
  • Graph GRb has an unstable slope with respect to parameter b. In regions where the value of b is small, 1/b is dominant, and in regions where the value of b is large, logb is dominant.
  • the function used for variable transformation of the parameter b is a function that asymptotically approaches -1/x when x ⁇ - ⁇ and exp(x) when x ⁇ . can be canceled.
  • the machine learning method of the learning model 20 described with reference to FIGS. 12 and 13 is an example of the "learned model generation method" in the present disclosure.
  • FIG. 14 is a functional block diagram showing an overview of the processing functions of the imaging state determination device 10 according to the third embodiment.
  • the processor 102 of the contrast state determination apparatus 10 executes the contrast state determination program 130 stored in the memory 104A to obtain the data acquisition unit 12, the seconds distribution estimation unit 14, the integration unit 16, the maximum point identification unit 18, the determination It functions as a unit 38 and an output unit 19 .
  • the data acquisition unit 12 accepts input of data to be processed.
  • the data acquisition unit 12 acquires an image IMi, which is a slice image sampled from CT data.
  • n may be an integer of 2 or more.
  • the data acquisition unit 12 may perform processing for cutting out slice images from CT data at regular intervals, or may acquire slice images sampled in advance by a processing unit (not shown) or the like.
  • the image IMi captured via the data acquisition unit 12 is input to the regression estimation unit 22 of the seconds distribution estimation unit 14 .
  • the regression estimator 22 outputs a set of an estimated value Oa of the number of seconds and a score value Ob indicating the likelihood of the estimated value Oa from each of the input images IMi.
  • the estimated value Oa output from the regression estimating unit 22 is converted into the parameter ⁇ i of the probability distribution model in the variable transforming unit 24, and the likelihood score Ob output from the regression estimating unit 22 is converted to probability in the variable transforming unit 24. It is converted into parameters bi of the distribution model. These two parameters ⁇ i, bi estimate the probability distribution Pi of the seconds.
  • the integration unit 16 performs processing to integrate multiple probability distributions Pi obtained by inputting multiple images IMi.
  • the logarithm of the probability distribution Pi is taken in the logarithmic conversion unit 26 and converted into the logarithmic probability density logPi, and the integrated distribution is obtained by calculating the sum of the logarithmic probability densities logPi in the integrated distribution generation unit 28. .
  • the maximum point identifying unit 18 identifies the value of the number of seconds (maximum point) with the maximum probability from the integrated distribution, and outputs the value of the identified number of seconds. Note that the maximum point identification unit 18 may be configured to be incorporated in the integration unit 16 .
  • the discriminating unit 38 discriminates the contrast enhancement state from the value of the number of seconds specified by the maximum point specifying unit 18 .
  • the determination unit 38 includes a conversion table 38A. Conversion table 38A is stored on computer readable medium 104 .
  • the conversion table 38A associates the elapsed time from the start of injection of the contrast medium with the contrast time phase. For example, in the case of liver contrast imaging, the conversion table 38A is associated with the arterial phase for less than 50 seconds from the start of injection of the contrast agent, the portal vein phase for 50 seconds to less than 120 seconds, and the equilibrium phase for 120 seconds or longer. .
  • the determination unit 38 determines the contrast enhancement state using the conversion table 38A.
  • the number of seconds output from images that are not suitable for estimation may deteriorate the result.
  • the estimation is less susceptible to the outliers and becomes robust.
  • FIG. 15 is an explanatory diagram showing Example 2 of processing in the number-of-seconds distribution estimating unit 14 of the imaging state determination apparatus 10 according to the fourth embodiment. Instead of the processing described with reference to FIG. 8, the processing of FIG. 15 is applied.
  • variable conversion unit 24 in the fourth embodiment converts the likelihood score value Ob into the parameter ⁇ 2 using the following equation (7) instead of equation (2).
  • ⁇ 2 1/log(1+exp( ⁇ Ob)) (7)
  • ⁇ 2 plays the role of certainty. ⁇ 2 corresponds to variance and ⁇ to standard deviation.
  • the Gaussian distribution is represented by the function of the following formula (8).
  • the reason for converting the score value Ob into a positive value ( ⁇ 2 ) is the same as in the third embodiment. This is because if the parameter ⁇ 2 is a negative value, the Gaussian distribution does not hold as a probability distribution, so it is necessary to ensure that the parameter ⁇ 2 is a positive value ( ⁇ 2 >0).
  • FIG. 16 shows an example of a graph of the number-of-seconds distribution estimated by the parameters ⁇ and ⁇ 2 estimated by the number-of-seconds distribution estimator 14 .
  • FIG. 17 is an explanatory diagram showing an example of processing in the integrating section 16 and the maximum point identifying section 18 of the contrast state determination apparatus 10 according to the fourth embodiment. Here, an example of integrating two number-of-seconds distributions estimated by the number-of-seconds distribution estimating unit 14 is shown.
  • a graph GL1g in FIG. 17 is an example of the logarithmic probability density logP1 obtained by taking the logarithm of the probability distribution P1.
  • a graph GD2g shown in the lower left of FIG. 17 is an example of the number of seconds distribution (probability distribution P2) represented by the parameters ⁇ 2 and ⁇ 2 2 estimated by the number of seconds distribution estimation unit 14 .
  • a graph GL2g in FIG. 17 is an example of the logarithmic probability density obtained by taking the logarithm of the probability distribution P2.
  • the rightmost graph GLSg in FIG. 17 is an example of the joint logarithmic probability density that integrates the logarithmic probability density logP1 and the logarithmic probability density logP2.
  • the maximum point identifying unit 18 identifies the value x that maximizes the logarithmic probability from the integrated joint logarithmic probability density.
  • the processing in the maximum point identification unit 18 can be represented by the following equation (9).
  • the target function of argmin shown on the right side of the equal sign in the second row of Equation (9) corresponds to the loss function during training in machine learning, which will be described later. Also, the right side of the equal sign described in the third row corresponds to the weighted average formula.
  • the input value (maximum point) x that maximizes the logarithmic probability is selected as the final estimation result (final result).
  • FIG. 18 is an explanatory diagram schematically showing an example of a machine learning method for generating a regression model applied to the number-of-seconds distribution estimator 14 in the fourth embodiment. Training data used for learning may be the same as in the third embodiment. Regarding FIG. 18, points different from FIG. 12 will be described.
  • the error backpropagation method is applied using the loss sum represented by Equation (11), and the learning model 20 is trained using the stochastic gradient descent method in the same way as in normal CNN learning.
  • the learning model 20 is trained using multiple training data comprising multiple image series, the parameters of the learning model 20 are optimized to obtain a trained model.
  • the learned model thus obtained is applied to the number-of-seconds distribution estimation unit 14 .
  • images IM1, IM2, and IM3 which are slice images (tomographic images) obtained by extracting slices from three-dimensional CT data at equal intervals, are used as inputs.
  • the image to be displayed is not limited to this.
  • a two-dimensional image IM11 (an example of a “first two-dimensional image”) including information of an image IM1 (an example of a “first slice image”)
  • an image IM2 (an example of a “second slice image”)
  • a two-dimensional image IM12 (an example of a "second two-dimensional image") including information of a "slice image”
  • a two-dimensional image IM13 including information of an image IM3.
  • the two-dimensional image may be a tomographic image TGimg itself, a MIP (Maximum Intensity Projection) image MIPimg configured at regular intervals, or an average image AVEimg generated from a plurality of slice images.
  • Inputs to the phase estimation unit 30, the number-of-seconds estimation unit 34, and the number-of-seconds distribution estimation unit 14 may be a combination of multiple types of data elements. For example, as shown in FIG. 20, at least one of slice images, MIP images, and average images, which are partial images of the same series of CT data, can be used as an input, and a combination of these image types can be used. It may be input to the phase estimator 30 , the number of seconds estimator 34 , and the number of seconds distribution estimator 14 . For example, the combination of the average image and the MIP image may be input to the seconds distribution estimation unit 14 to estimate the seconds distribution. MIP images and average images are examples of generated images generated from partial images of three-dimensional CT data.
  • FIG. 21 is a block diagram showing a configuration example of a medical information system 200 including a medical image processing device 220.
  • the contrast enhancement state determination device 10 described as the first to fourth embodiments is incorporated in a medical image processing device 220, for example.
  • a medical information system 200 is a computer network built in a medical institution such as a hospital.
  • the medical information system 200 includes a modality 230 that captures medical images, a DICOM server 240, a medical image processing device 220, an electronic chart system 244, and a viewer terminal 246. These elements are connected via a communication line 248. Connected.
  • Communication line 248 may be a local communication line within a medical institution. Also, part of the communication line 248 may be a wide area communication line.
  • the modality 230 include a CT device 231, an MRI (Magnetic Resonance Imaging) device 232, an ultrasonic diagnostic device 233, a PET (Positron Emission Tomography) device 234, an X-ray diagnostic device 235, an X-ray fluoroscopic diagnostic device 236, and An endoscope device 237 and the like are included.
  • CT device 231 an MRI (Magnetic Resonance Imaging) device 232, an ultrasonic diagnostic device 233, a PET (Positron Emission Tomography) device 234, an X-ray diagnostic device 235, an X-ray fluoroscopic diagnostic device 236, and An endoscope device 237 and the like are included.
  • the types of modalities 230 connected to the communication line 248 can be combined in various ways for each medical institution.
  • the DICOM server 240 is a server that operates according to the DICOM specifications.
  • the DICOM server 240 is a computer that stores and manages various data including images captured using the modality 230, and has a large-capacity external storage device and a database management program.
  • the DICOM server 240 communicates with other devices via a communication line 248 to transmit and receive various data including image data.
  • the DICOM server 240 receives image data generated by the modality 230 and other various data via a communication line 248, and stores and manages them in a recording medium such as a large-capacity external storage device.
  • the storage format of image data and communication between devices via the communication line 248 are based on the DICOM protocol.
  • the medical image processing apparatus 220 can acquire data from the DICOM server 240 or the like via the communication line 248.
  • the medical image processing apparatus 220 performs image analysis and various other processes on medical images captured by the modality 230 .
  • the medical image processing device 220 performs, for example, processing for recognizing a lesion area from an image, processing for identifying a classification such as a disease name, or segmentation processing for recognizing a region such as an organ. It may be configured to perform analysis processing such as various computer aided diagnosis (Computer Aided Diagnosis, Computer Aided Detection: CAD).
  • the medical image processing device 220 can also send processing results to the DICOM server 240 and the viewer terminal 246 . Note that the processing functions of the medical image processing apparatus 220 may be installed in the DICOM server 240 or the viewer terminal 246 .
  • Various data stored in the database of the DICOM server 240 and various information including the processing results generated by the medical image processing apparatus 220 can be displayed on the viewer terminal 246.
  • the viewer terminal 246 is a terminal for viewing images called a PACSCS (Picture Archiving and Communication Systems) viewer or a DICOM viewer.
  • a plurality of viewer terminals 246 can be connected to the communication line 248 .
  • the form of the viewer terminal 246 is not particularly limited, and may be a personal computer, a workstation, a tablet terminal, or the like.
  • a program that causes a computer to implement the processing functions of the imaging state determination apparatus 10 is recorded on a computer-readable medium that is a non-temporary information storage medium such as an optical disk, magnetic disk, semiconductor memory, or other tangible object, and through this information storage medium. It is possible to provide a program.
  • part or all of the processing functions in the imaging state determination apparatus 10 may be realized by cloud computing, and may be provided as a Sass (Software as a Service) service.
  • Sass Software as a Service
  • processors include CPUs, which are general-purpose processors that run programs and function as various processing units, GPUs, which are processors specialized for image processing, and FPGAs (Field Programmable Gate Arrays).
  • PLD Programmable Logic Device
  • ASIC Application Specific Integrated Circuit
  • a single processing unit may be composed of one of these various processors, or may be composed of two or more processors of the same type or different types.
  • one processing unit may be configured by a plurality of FPGAs, a combination of CPU and FPGA, or a combination of CPU and GPU.
  • a plurality of processing units may be configured by one processor.
  • configuring a plurality of processing units in a single processor first, as represented by computers such as clients and servers, a single processor is configured by combining one or more CPUs and software. There is a form in which a processor functions as multiple processing units.
  • SoC System On Chip
  • the various processing units are configured using one or more of the above various processors as a hardware structure.
  • the hardware structure of these various processors is, more specifically, an electrical circuit that combines circuit elements such as semiconductor elements.
  • Contrast state determination device 10
  • Data acquisition unit 14
  • Second distribution estimation unit 16
  • Integration unit 18
  • Maximum point identification unit 19
  • Output unit 20
  • Learning model 22
  • Regression estimation unit 24
  • Variable conversion unit 26
  • Logarithmic conversion unit 24
  • Variable conversion unit 26
  • Logarithmic conversion unit 28
  • Integrated distribution generation unit 30
  • Temporal estimation Unit 30A Trained model
  • Integration unit 34
  • Number of seconds estimation unit 34A Trained model
  • Integration unit 38
  • Discrimination unit 38
  • Conversion table 102
  • Processor 104
  • Computer-readable medium 104
  • Memory 104B
  • Communication interface 108
  • Input/output interface 110
  • Bus 114
  • Input device 116
  • Display Apparatus 130
  • Contrast state determination program 200
  • Medical information system 220
  • Medical image processing apparatus 230
  • Modality 21
  • CT apparatus 232
  • MRI apparatus 233
  • Ultrasound diagnostic apparatus 234
  • PET apparatus 235

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