Information processing device, method, and program

Abstract

To detect a feature point appropriately when setting an imaging range in information processing device, method, and program.SOLUTION: A processor acquires a camera image generated by imaging a subject on a bed with a camera by performing moving image capturing; selects one detection model from a plurality of detection models including a first detection model putting priority on a frame rate at the time of detection of a feature point and a second detection model putting priority on the accuracy at the time of detection of the feature point, which are constructed to detect a plurality of feature points on the subject included in the camera image; detects a plurality of feature points on the subject included in the camera image by the selected detection models; and specifies the imaging range of the subject on the basis of the plurality of feature points.SELECTED DRAWING: Figure 4

Description

Technical Field

[0001] The present disclosure relates to an information processing apparatus, method, and program.

Background Art

[0002] In recent years, due to the progress of medical devices such as CT (Computed Tomography) devices and MRI (Magnetic Resonance Imaging) devices, higher-quality high-resolution three-dimensional images have come to be used for image diagnosis.

[0003] When performing imaging of a subject using an imaging device such as a CT device or an MRI device, in order to determine the imaging range, a scout scan is performed prior to the main scan for acquiring a three-dimensional image, and a two-dimensional positioning image (scout image) is acquired. An operator (such as a technician) of the imaging device sets the imaging range during the main scan while looking at the scout image.

[0004] Prior to the scout scan, the operator sets the imaging range of the scout scan for the subject on the examination table. For example, the subject is irradiated with a cross-shaped laser, the scan start position of the scout scan is set, and the scan end position of the scout scan is set so as to be an imaging range corresponding to the imaging site. During the scout scan, when the examination table moves from the initial position of the table to the scan start position, the scan of the scout scan starts, and when the examination table moves to the scan end position, the scan of the scout scan ends. After setting the imaging range of the main scan based on the scout image acquired by the scout scan, the operator performs the main scan to acquire a three-dimensional image.

[0005] Here, when setting the imaging range during the scout scan, the subject is imaged by a camera provided above the examination table, feature points such as both ankles, both waists, both elbows, and both shoulders of the subject are detected, and the imaging range is specified based on the detected feature points. At this time, a learned detection model constructed by machine learning a neural network is used for the detection of the feature points.

[0006] On the other hand, when applying a pre-trained model to medical images, a method has been proposed for efficiently processing medical images by preparing a pre-trained model that performs a plurality of different processes and selecting which pre-trained model to use according to the situation (see, for example, Patent Document 1).

Prior Art Documents

Patent Documents

[0007]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0008] As described above, when photographing a subject with a camera to position the photographing range, the subject may move on the bed. In particular, the head of the subject is likely to move. When detecting feature points from the image captured by the camera using a detection model, if the subject moves, it may become impossible to accurately detect the feature points. In addition, in some cases, the subject may be photographed with a blanket, but in this case, since a part of the subject is covered, it may become impossible to detect the feature points.

[0009] The present disclosure has been made in view of the above circumstances, and an object thereof is to be able to appropriately detect feature points when setting a photographing range.

Means for Solving the Problems

[0010] The information processing apparatus according to the present disclosure includes at least one processor, the processor acquires a camera image generated by video-photographing a subject on a bed with a camera, and selects one detection model from a plurality of detection models including a first detection model that emphasizes the frame rate at the time of detecting feature points and a second detection model that emphasizes the accuracy at the time of detecting feature points, which are constructed to detect a plurality of feature points on the subject included in the camera image. Detect a plurality of feature points on the subject included in the camera image using the selected detection model, and identify the shooting range of the subject based on the plurality of feature points.

[0011] Note that in the information processing apparatus according to the present disclosure, the processor may select a detection model according to the shooting part of the subject.

[0012] Further, in the information processing apparatus according to the present disclosure, the processor may determine the detection accuracy of the feature points, and if the detection accuracy is equal to or higher than the standard, identify the shooting range based on the feature points, and if the detection accuracy is lower than the standard, issue a warning.

[0013] Further, in the information processing apparatus according to the present disclosure, the processor may detect the movement of the subject based on the camera image, and if the movement of the subject is equal to or greater than the standard, identify the shooting range based on the feature points detected using the first detection model, and if the movement of the subject is smaller than the standard, identify the shooting range based on the feature points detected using the second detection model.

[0014] Further, in the information processing apparatus according to the present disclosure, the processor may select the first detection model to detect feature points from the camera image, and detect the movement of the subject based on the feature points.

[0015] Further, in the information processing apparatus according to the present disclosure, the processor may determine the detection accuracy of the feature points, and if the detection accuracy is equal to or higher than the standard, identify the shooting range based on the feature points, and if the detection accuracy is lower than the standard, issue a warning.

[0016] Also, in the information processing apparatus according to the present disclosure, the processor selects a first detection model, detects feature points from the camera image, determines the detection accuracy of the feature points, and when the detection accuracy is equal to or higher than a first standard, specifies the shooting range based on the feature points detected using the first detection model, and when the detection accuracy is lower than the first standard, selects a second detection model and specifies the shooting range based on the feature points detected using the second detection model.

[0017] Also, in the information processing apparatus according to the present disclosure, the processor determines the detection accuracy of the feature points detected using the second detection model, and when the detection accuracy is equal to or higher than a second standard, specifies the shooting range based on the feature points, and when the detection accuracy is lower than the second standard, may issue a warning.

[0018] Also, in the information processing apparatus according to the present disclosure, the processor may derive the moving distance of the bed based on the shooting range.

[0019] Also, in the information processing apparatus according to the present disclosure, the processor may display a human body image simulating a human body on the display and draw a shooting start line and a shooting end line based on the shooting range on the human body image.

[0020] Also, in the information processing apparatus according to the present disclosure, the shooting range may be the shooting range when shooting a positioning image acquired before actual shooting of the subject.

[0021] Also, in the information processing apparatus according to the present disclosure, the first detection model has a smaller amount of calculation than the second detection model and a faster processing speed for detecting feature points, and the second detection model has a larger amount of calculation than the first detection model and a slower processing speed for detecting feature points but a higher detection accuracy.

[0022] The information processing method according to the present disclosure includes a computer acquiring a camera image generated by video-shooting a subject on a bed with a camera. A plurality of detection models including a first detection model that emphasizes the frame rate at the time of detecting feature points and a second detection model that emphasizes the accuracy at the time of detecting feature points, which are constructed to detect a plurality of feature points on a subject included in a camera image, select one detection model from among them, detect a plurality of feature points on the subject included in the camera image by the selected detection model, specify the shooting range of the subject based on the plurality of feature points.

[0023] An information processing program according to the present disclosure includes a procedure for acquiring a camera image generated by video-shooting a subject on a bed with a camera, a procedure for selecting one detection model from a plurality of detection models including a first detection model that emphasizes the frame rate at the time of detecting feature points and a second detection model that emphasizes the accuracy at the time of detecting feature points, which are constructed to detect a plurality of feature points on a subject included in the camera image, a procedure for detecting a plurality of feature points on the subject included in the camera image by the selected detection model, a procedure for specifying the shooting range of the subject based on the plurality of feature points, and causing a computer to execute them.

Advantages of the Invention

[0024] According to the present disclosure, when setting the shooting range, feature points can be appropriately detected according to the situation of the subject.

Brief Description of the Drawings

[0025]

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Fig. 10

Mode for Carrying Out the Invention

[0026] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. FIG. 1 is a perspective view showing an overview of a CT apparatus to which the information processing apparatus according to the first embodiment of the present disclosure is applied, and FIG. 2 is a view of the CT apparatus to which the information processing apparatus according to the first embodiment of the present disclosure is applied as seen from the side. As shown in FIGS. 1 and 2, the CT apparatus 1 according to the present embodiment includes a gantry 2, a bed 3, and a console 4.

[0027] The gantry 2 has a tunnel-shaped structure having an opening 5 at its center. Inside the gantry 2, an X-ray source unit that emits X-rays and a detection unit that detects X-rays and generates a radiation image are provided (both not shown). The X-ray source unit and the detection unit can each rotate along the annular shape of the gantry 2 while maintaining a position relationship facing each other. Further, a control unit that controls the operation of the CT apparatus 1 is provided inside the gantry 2.

[0028] The bed 3 has a bed portion 3A on which the subject lies, a base portion 3B that supports the bed portion 3A, and a drive unit 3C that reciprocates the bed portion 3A in the direction of arrow A. The bed portion 3A is slidable in the direction of arrow A with respect to the base portion 3B by the drive unit 3C. When taking a CT image, the subject H lying on the bed portion 3A is transported into the opening 5 of the gantry 2 by the sliding of the bed portion 3A.

[0029] Note that a camera 7 is installed above the bed 3. The camera 7 is a camera capable of taking an RGB color image by detecting the reflected light of the subject H. The camera 7 has an imaging element such as a lens and a CCD (Charge Coupled Device), and obtains a camera image, which is a moving image, by photographing the subject H on the bed 3 at a predetermined frame rate, and outputs it to the console 4. Note that the camera 7 may be a camera in which an RGB camera and an NIR (Near InfraRed) are integrated. In this case, the NIR camera is a stereo camera, and information on the depth direction of the subject H can be obtained by parallax. By using the NIR camera of the camera 7 as a stereo camera, information on the depth direction of the subject H on the bed 3 can be obtained. Note that the NIR camera can perform photographing even when the amount of light is insufficient. Therefore, when the brightness of the examination room is insufficient for photographing with the RGB camera, the subject H may be photographed with the NIR camera.

[0030] The driving of the gantry 2, the driving of the bed 3, and the photographing of the subject by the camera 7 are performed by an operator's input from the console 4. The console 4 incorporates the information processing apparatus according to the present embodiment.

[0031] Next, an information processing apparatus according to the first embodiment included in the console 4 will be described. First, with reference to FIG. 3, the hardware configuration of the information processing apparatus according to the first embodiment will be described. As shown in FIG. 3, the information processing apparatus 10 is a computer such as a workstation, a server computer, and a personal computer, and includes a CPU (Central Processing Unit) 11, a non-volatile storage 13, and a memory 16 as a temporary storage area. Further, the information processing apparatus 10 includes a display 14 such as a liquid crystal display, an input device 15 such as a keyboard and a mouse, and an interface 17 such as a network I / F (InterFace) connected to the CT apparatus 1. The CPU 11, the storage 13, the display 14, the input device 15, the memory 16, and the network I / F 17 are connected to a bus 18. Note that the CPU 11 is an example of a processor in the present disclosure. The display 14 and the input device 15 are also illustrated in FIGS. 1 and 2.

[0032] The storage 13 is realized by an HDD (Hard Disk Drive), an SSD (Solid State Drive), a flash memory, or the like. The information processing program 12 installed in the information processing apparatus 10 is stored in the storage 13 as a storage medium. The CPU 11 reads the information processing program 12 from the storage 13, expands it in the memory 16, and executes the expanded information processing program 12.

[0033] Note that the information processing program 12 is stored in a storage device of a server computer connected to the network or a network storage in a state accessible from the outside, and is downloaded and installed in the computer constituting the information processing apparatus 10 in response to a request. Alternatively, it is recorded and distributed on a recording medium such as a DVD (Digital Versatile Disc) or a CD-ROM (Compact Disc Read Only Memory), and is installed in the computer constituting the information processing apparatus 10 from the recording medium.

[0034] Next, the functional configuration of the information processing apparatus according to the first embodiment will be described. FIG. 4 is a diagram showing the functional configuration of the information processing apparatus according to the first embodiment. As shown in FIG. 4, the information processing apparatus 10 includes a shooting control unit 20, a camera control unit 21, a selection unit 22, a feature point detection unit 23, and a shooting range specifying unit 24. Then, when the CPU 11 executes the information processing program 12, the CPU 11 functions as the shooting control unit 20, the camera control unit 21, the selection unit 22, the feature point detection unit 23, and the shooting range specifying unit 24.

[0035] The shooting control unit 20 controls the shooting unit, the detection unit, and the control unit provided in the gantry 2 to shoot the subject H according to an instruction from the input device 15. Note that, at the time of CT shooting, in order to determine the shooting range, scout shooting is performed prior to the main shooting for acquiring a three-dimensional CT image. The scout shooting is performed by shooting the subject H with the shooting unit and the detection unit fixed.

[0036] At the time of scout shooting, as will be described later, the shooting range of the subject H is set, and the bed 3 is moved to the opening 5 of the gantry 2 so that the set shooting range is shot, and the scout shooting is performed. The scout image acquired by the scout shooting is a two-dimensional image including the shooting range set for the subject H. The scout image is displayed on the display 14. The operator views the scout image displayed on the display 14 and sets the shooting range for the main shooting. After setting the shooting range, when the operator gives an instruction for the main shooting from the input device 15, the main shooting is performed, and a three-dimensional CT image of the subject H is acquired. The acquired scout image and CT image are stored in the storage 13.

[0037] The camera control unit 21 controls the photographing of the subject H on the bed 3 by the camera 7. The photographing of the subject H by the camera 7 is performed to set the photographing range when performing scout photographing. The photographing of the subject H by the camera 7 is performed from the preparation stage before photographing. That is, the camera control unit 21 starts photographing by the camera 7 from the time before the subject H lies supine on the bed 3, and causes the camera 7 to acquire a camera image. Then, when an instruction to start scout photographing is given by the operator, the camera control unit 21 stops the photographing by the camera 7. The acquired camera image is stored in the memory 16 to specify the photographing range described later.

[0038] The selection unit 22 selects one detection model from the first detection model 22A and the second detection model 22B constructed to detect a plurality of feature points on the subject H included in the camera image acquired by the camera 7. The first detection model 22A is a detection model that emphasizes the frame rate at the time of detecting feature points. The second detection model 22B is a model that emphasizes the accuracy at the time of detecting feature points. The first detection model 22A and the second detection model 22B are constructed by machine learning a neural network.

[0039] "Emphasizing the frame rate" means, for example, reducing the amount of data to be processed, omitting calculations, etc., so as to improve the processing speed for detecting feature points. Since the first detection model 22A emphasizes the frame rate, a model with a small amount of calculation and a high processing speed for detecting feature points is used.

[0040] "Emphasizing the accuracy" means improving the accuracy of detecting feature points, which takes time for calculations, without reducing the amount of data or omitting calculations. Since the second detection model 22B emphasizes the accuracy, a model with a large amount of calculation, a slower processing speed than the first detection model 22A, but a higher accuracy for detecting feature points than the first detection model 22A is used.

[0041] The first detection model 22A uses a neural network with a structure that has a small amount of computation and can perform the processing of detecting feature points at high speed. The second detection model 22B uses a neural network with a structure that, although having a large amount of computation, can detect feature points with higher accuracy than the first detection model 22A. For both neural networks, a teacher image that includes the whole body of a human and for which 17 feature points are known, obtained by photographing a human body with the camera 7, is used for learning. Note that the teacher image is obtained, for example, by photographing a person wearing an examination gown with the camera 7, in the same manner as in an actual inspection.

[0042] Note that neural networks with the same structure may be used for the first detection model 22A and the second detection model 22B. In this case, the neural network is trained using different teacher images for the first detection model 22A and the second detection model 22B. For example, for the first teacher image used for training the first detection model 22A, one with a relatively low resolution is used. On the other hand, for the second teacher image used for training the second detection model 22B, one with a higher resolution than the first teacher image is used.

[0043] In the first embodiment, the selection unit 22 selects either the first detection model 22A or the second detection model 22B according to the imaging part of the subject H. Here, the imaging parts of the subject H include the head, chest, abdomen, lower limbs, and the whole body, etc. The head is likely to move during imaging, while the chest or abdomen is less likely to move during imaging. For this reason, when the imaging part is the head or the whole body including the head, the selection unit 22 selects the first detection model 22A that emphasizes the frame rate. On the other hand, when the imaging part is the abdomen or the lower limbs, since imaging is often performed with a blanket covering, the selection unit 22 selects the second detection model 22B that emphasizes accuracy and is easier to detect more feature points.

[0044] Note that in this embodiment, a table associating the imaging part with the detection model to be selected is stored in the storage 13. The selection unit 22 refers to this table and selects the detection model according to the imaging part.

[0045] Note that the imaging site of the subject H is included in the examination order provided by the doctor at the time of imaging. The operator refers to the examination order and sets the imaging site using the input device 15.

[0046] The feature point detection unit 23 detects feature points from the camera image using the detection model selected by the selection unit 22. FIG. 5 is a diagram for explaining the feature points. As shown in FIG. 5, in the present embodiment, the first and second detection models 22A and 22B are constructed to detect 17 feature points on the subject H included in the camera image. The 17 feature points are both eyes, nose, both ears, both shoulders, both elbows, both hands, both waists, both knees, and both feet. Note that the midpoint of the collarbone may be added to these 17 feature points to use 18 feature points.

[0047] The first detection model 22A and the second detection model 22B derive the probability representing the possibility of each of the 17 feature points at each pixel of the camera image. Then, the feature point detection unit 23 detects, for each of the 17 feature points, the pixel for which the highest probability is derived as the feature point. For example, when detecting the right eye as a feature point, the feature point detection unit 23 compares the probabilities of the right eye for all the pixels of the camera image derived by the first detection model 22A and the second detection model 22B, and detects the pixel with the highest probability as the feature point of the right eye.

[0048] The imaging range specifying unit 24 specifies the imaging range of the subject H when performing scouting imaging based on the 17 feature points detected by the feature point detection unit 23. For this purpose, the imaging range specifying unit 24 first determines the detection accuracy of the feature points detected by the feature point detection unit 23. As described above, the feature point detection unit 23 detects, as feature points, the pixels for which the highest probability is derived for each of the 17 feature points. The higher the probability output by the detection model for the detected feature points, the better the detection accuracy. Therefore, the imaging range specifying unit 24 compares the representative value of the probabilities derived by the detection model for the 17 feature points with a predetermined threshold, and determines that the detection accuracy is good when the representative value is greater than or equal to the threshold, and that the detection accuracy is poor when the representative value is less than the threshold.

[0049] As the representative value, an average value, a median value, a weighted average value according to the imaging part, etc. can be used, but it is not limited to these. Also, in the first detection model 22A that emphasizes the frame rate and the second detection model 22B that emphasizes accuracy, even when the same camera image is input, the probability derived for the feature points is smaller for the first detection model 22A. Therefore, when the first detection model 22A is selected, a smaller threshold may be used than when the second detection model 22B is selected.

[0050] When the imaging range specifying unit 24 determines that the detection accuracy is good, it performs the process of specifying the imaging range. On the other hand, when it determines that the detection accuracy is poor, the imaging range specifying unit 24 performs a warning display on the display 14. Here, if the subject H is moving too much, a thick blanket is placed on the subject H, or the entire body of the subject H is not included in the imaging range of the camera 7, the feature points cannot be accurately detected using any detection model, and the detection accuracy deteriorates. In such a case, the imaging range specifying unit 24 determines that the detection accuracy is poor.

[0051] When a warning is displayed, the operator manually sets the shooting range for scout shooting. That is, the operator measures the distance from the initial position of the bed to the scan start line, further measures the distance between the scan start line and the scan end line, and inputs the measured distances from the input device 15. Based on the input distances, the movement of the bed 3 during scout shooting is controlled. The scan start line is an example of the start line of shooting, and the scan end line is an example of the end line of shooting.

[0052] Hereinafter, the process of specifying the shooting range by the shooting range specifying unit 24 will be described. For example, when the shooting part is the head, the scout image has a shooting range from the top of the head to the tip of the chin. Therefore, the shooting range specifying unit 24 sets a line connecting both eyes or both ears among the feature points detected by the feature point detecting unit 23, and further sets a scan start line at the top of the head and a scan end line between the chin and both shoulders. Then, based on the distance relationship between the line connecting both eyes or both ears and the scan start line and the scan end line, the distance D1 between the scan start line and the scan end line is derived. The range of the distance D1 between the scan start line and the scan end line is the shooting range.

[0053] Here, before scout shooting, the bed 3 is in the initial position and the subject H is lying supine on the bed 3, so the distance from the end of the bed 3 to the top of the head of the subject H can be known from the camera image. Therefore, the shooting range specifying unit 24 calculates the distance D2 from the end of the bed 3 to the top of the head of the subject H as the movement amount of the bed from the initial position to the scan start line, that is, the movement amount of the bed 3 until the top of the head of the subject H reaches the scan position in the CT apparatus 1.

[0054] When the imaging range is specified, the imaging range specifying unit 24 displays the scan start line and the scan end line on the schema, which is a human body diagram displayed on the display 14. FIG. 6 is a diagram showing the schema on which the start line and the end line are displayed. As shown in FIG. 6, the schema 30 shows a scan start line 31 and a scan end line 32. The operator checks the scan start line and the scan end line displayed on the display 14. After the check, if it is OK, the operator uses the input device 15 to give an instruction to start the scout scan.

[0055] In response to the instruction to start the scout scan, information on distances D1 and D2 is output to the CT apparatus 1. The imaging control unit 20 controls the CT apparatus 1 such that after the driving unit 3C moves the bed 3 by the distance D2, the scout scan is started, and when the driving unit 3C moves the bed 3 by the distance D1, the scout scan is ended.

[0056] The scout image obtained by the scout scan is displayed on the display 14. The operator checks the scout image displayed on the display 14 and sets the imaging range of the main scan with respect to the scout image. Then, the main scan is performed by giving an imaging instruction of the main scan using the input device 15, and a three-dimensional CT image of the imaging site of the subject H is obtained within the set imaging range of the main scan.

[0057] Next, the processing performed in the first embodiment will be described. FIG. 7 is a flowchart showing the processing performed in the first embodiment. The processing is started when an instruction to start imaging is given from the input device 15, and the selection unit 22 determines the imaging site included in the inspection order (step ST1). Then, the selection unit 22 selects either the first detection model 22A or the second detection model 22B according to the determined imaging site (detection model selection; step ST2).

[0058] Subsequently, the camera control unit 21 starts shooting by the camera 7 to acquire a camera image (step ST3), and the feature point detection unit 23 detects feature points from the camera image using the detection model selected by the selection unit 22 (step ST4). Subsequently, the shooting range specifying unit 24 determines the detection accuracy of the feature points (step ST5). If it is determined that the detection accuracy is good, the shooting range specifying unit 24 specifies the shooting range during scouting shooting based on the feature points (step ST6), and based on the specified shooting range, draws the scan start line and the scan end line during scouting shooting on the schema displayed on the display 14 (line drawing; step ST7). On the other hand, if it is determined that the detection accuracy is bad, the shooting range specifying unit 24 performs a warning display (step ST8). When the warning display is performed, the information processing apparatus 10 ends the shooting range specifying process. In this case, as described above, the operator manually sets the shooting range of the scouting shooting.

[0059] Subsequently, it is determined whether or not an instruction to start scouting shooting has been given by the operator (step ST9). If step ST9 is negated, the process returns to step ST3, and the processes after step ST3 are repeated. If step ST9 is affirmed, the camera control unit 21 stops shooting by the camera 7 (step ST10), and the information processing apparatus 10 ends the shooting range specifying process.

[0060] Thereafter, scouting shooting is performed by the shooting control unit 20 to acquire a scouting image, which is displayed on the display 14. After the operator checks the scouting image, the operator sets the shooting range of the main shooting. Then, when the operator gives an instruction for the main shooting from the input device 15, the main shooting is performed, and a three-dimensional CT image of the subject H is acquired.

[0061] As described above, in the first embodiment, the detection model used for detecting feature points is selected according to the imaging site. Therefore, when the imaging site is the head or the like that often moves during imaging, the first detection model 22A that emphasizes the frame rate is selected. When the imaging site is the abdomen or the like that rarely moves during imaging but is often covered with a blanket or the like, the second detection model 22B that emphasizes accuracy can be selected. Therefore, when setting the imaging range, feature points can be appropriately detected according to the imaging site. As a result, the imaging range at the time of scouting imaging can be appropriately specified using the detected feature points.

[0062] Next, a second embodiment of the present disclosure will be described. Since the hardware configuration of the information processing apparatus according to the second embodiment is the same as that of the first embodiment, detailed description thereof will be omitted here. FIG. 8 is a diagram showing a functional configuration of the information processing apparatus according to the second embodiment. In FIG. 8, the same components as those in FIG. 4 are given the same reference numerals, and detailed description thereof will be omitted. The information processing apparatus 10A according to the second embodiment is different from the first embodiment in that it includes a motion detection unit 25 that detects the motion of the subject H.

[0063] The motion detection unit 25 detects the motion of the subject H. Specifically, the two-dimensional motion of the feature points detected by the feature point detection unit 23 is detected between frames that are temporally adjacent in the camera image. In the second embodiment, the selection unit 22 first selects the first detection model 22A that emphasizes the frame rate, and the feature point detection unit 23 detects feature points using the first detection model 22A. Feature points for detecting the motion of the subject H may be those corresponding to the imaging site. For example, when the imaging site is the head, the nose, both eyes, or both ears may be used. When the imaging site is the chest, both shoulders and the bases of the left and right feet may be used. Note that as the motion, a representative value of the motions of all 17 feature points may be obtained.

[0064] When the detected motion is equal to or greater than a predetermined threshold value, the motion detection unit 25 determines that the motion is large. When the detected motion is less than the threshold value, the motion detection unit 25 determines that the motion is small.

[0065] In the second embodiment, when the motion detection unit 25 determines that the motion of the subject H is small, the selection unit 22 selects the accuracy - focused second detection model 22B instead of the first detection model 22A. The feature point detection unit 23 detects feature points using the second detection model 22B selected by the selection unit 22, and the imaging range specifying unit 24 specifies the imaging range using the detected feature points. On the other hand, when the motion detection unit 25 determines that the motion of the subject H is large, the feature point detection unit 23 continues to use the first detection model 22A used to detect the feature points for detecting the motion to detect the feature points, and the imaging range specifying unit 24 specifies the imaging range using the detected feature points.

[0066] Next, the processing performed in the second embodiment will be described. FIG. 9 is a flowchart showing the processing performed in the second embodiment. For example, when an instruction to start imaging is given from the input device 15, the processing is started, and the camera control unit 21 starts imaging by the camera 7 to acquire a camera image (step ST21). Next, the selection unit 22 selects the first detection model 22A that emphasizes the frame rate (step ST22), and the feature point detection unit 23 detects feature points from the camera image using the detection model selected by the selection unit 22 (step ST23).

[0067] Subsequently, the motion detection unit 25 detects the motion of the subject H using the feature points detected by the feature point detection unit 23 (step ST24), and determines whether the motion is large (step ST25). If the motion of the subject H is small and step ST25 is denied, the selection unit 22 selects the accuracy - focused second detection model 22B instead of the first detection model 22A (step ST26). Subsequently, the feature point detection unit 23 detects feature points from the camera image (step ST27), and the imaging range specifying unit 24 determines the detection accuracy of the feature points (step ST28). If step ST25 is affirmed, the process proceeds to step ST28, and the imaging range specifying unit 24 determines the detection accuracy for the feature points detected in step ST23.

[0068] When it is determined that the detection accuracy is good, the shooting range specifying unit 24 specifies the shooting range at the time of scouting shooting based on the feature points (step ST29), and based on the specified shooting range, the scan start line and the scan end line at the time of scouting shooting are drawn on the schema displayed on the display 14 (line drawing; step ST30). On the other hand, when it is determined that the detection accuracy is bad, the shooting range specifying unit 24 performs a warning display (step ST31). When the warning display is performed, the information processing apparatus 10A ends the shooting range specifying process. In this case, as described above, the operator manually sets the shooting range of the scouting shooting.

[0069] Subsequently, it is determined whether or not an instruction to start scouting shooting has been given by the operator (step ST32). If step ST32 is negated, the process returns to step ST21, and the processes after step ST21 are repeated. If step ST32 is affirmed, the camera control unit 21 stops the shooting by the camera 7 (step ST33), and the information processing apparatus 10A ends the shooting range specifying process.

[0070] As described above, in the second embodiment, the movement of the subject H is detected. When the movement is large, the first detection model 22A that emphasizes the frame rate is used. When the movement is small, the second detection model 22B that emphasizes accuracy is used to detect the feature points. Therefore, when setting the shooting range, the feature points can be appropriately detected according to the movement of the subject. As a result, the shooting range at the time of scouting shooting can be appropriately specified using the detected feature points.

[0071] Next, a third embodiment of the present disclosure will be described. Note that the hardware configuration and the functional configuration of the information processing apparatus according to the third embodiment are the same as those of the information processing apparatus according to the first embodiment described above, and thus detailed description thereof will be omitted here. In the third embodiment, first, feature points are detected by the first detection model 22A that emphasizes the frame rate, the detection accuracy of the feature points is determined, and when the detection accuracy is bad, the second detection model 22B that emphasizes accuracy is used instead of the first detection model 22A. This is different from the first embodiment.

[0072] Next, the processing performed in the third embodiment will be described. FIG. 10 is a flowchart showing the processing performed in the third embodiment. For example, when an instruction to start shooting is given from the input device 15, the processing is started, and the camera control unit 21 starts shooting with the camera 7 to acquire a camera image (step ST41). Next, the selection unit 22 selects the first detection model 22A that emphasizes the frame rate (step ST42), and the feature point detection unit 23 detects feature points from the camera image using the detection model selected by the selection unit 22 (step ST43).

[0073] Next, the shooting range specifying unit 24 determines the detection accuracy of the feature points (step ST44). If the shooting range specifying unit 24 determines that the detection accuracy is poor, the selection unit 22 selects the second detection model 22B that emphasizes accuracy instead of the first detection model 22A (step ST45). Subsequently, the feature point detection unit 23 detects feature points from the camera image (step ST46), and the shooting range specifying unit 24 determines the detection accuracy of the feature points (step ST47).

[0074] If it is determined that the detection accuracy is good, the shooting range specifying unit 24 specifies the shooting range at the time of scout shooting based on the feature points (step ST48), and based on the specified shooting range, draws the scan start line and the scan end line at the time of scout shooting on the schema displayed on the display 14 (line drawing; step ST49). On the other hand, if it is determined that the detection accuracy is poor, the shooting range specifying unit 24 performs a warning display (step ST50). When the warning display is performed, the information processing apparatus 10 ends the shooting range specifying process. In this case, as described above, the operator manually sets the shooting range of the scout shooting.

[0075] Note that if the shooting range specifying unit 24 determines in step ST44 that the detection accuracy is good, the process proceeds to step ST48, and the processes after step ST48 are performed.

[0076] Subsequently, it is determined whether or not an instruction to start scout imaging has been given by the operator (step ST51). If step ST51 is negative, the process returns to step ST41, and the processes after step ST41 are repeated. If step ST51 is positive, the camera control unit 21 stops the imaging by the camera 7 (step ST52), and the information processing apparatus 10 ends the imaging range specifying process.

[0077] As described above, in the third embodiment, first, the feature points are detected by the first detection model 22A that emphasizes the frame rate. When the detection accuracy of the feature points is good, the first detection model 22A that emphasizes the frame rate is continuously used. When the detection accuracy of the feature points is poor, the second detection model 22B that emphasizes the accuracy is used to detect the feature points. Therefore, when setting the imaging range, the feature points can be appropriately detected according to the detection accuracy of the feature points. As a result, the imaging range at the time of scout imaging can be appropriately specified using the detected feature points.

[0078] In each of the above embodiments, the information processing apparatus according to the present disclosure is applied to the CT apparatus, but the present disclosure is not limited thereto. The information processing apparatus according to the present disclosure may be applied to an MRI apparatus or the like as long as it is an imaging apparatus that acquires a scout image for setting an imaging range before the main imaging.

[0079] Also, in each of the above embodiments, the information processing apparatus is provided with the imaging control unit 20, but the present disclosure is not limited thereto. The imaging control unit 20 may be provided separately from the information processing apparatus.

[0080] Also, in each of the above embodiments, the detection model used for detecting the feature points is selected from the first detection model 22A and the second detection model 22B, but the present disclosure is not limited thereto. For example, regarding the detection model that emphasizes the frame rate, a plurality of detection models having different processing speeds for feature point detection may be used. Also, regarding the detection model that emphasizes the accuracy, a plurality of detection models having different accuracies for feature point detection may be used.

[0081] Also, in the above-described embodiment, for example, as the hardware structure of a processing unit (Processing Unit) that executes various processes such as a shooting control unit 20, a camera control unit 21, a selection unit 22, a feature point detection unit 23, a shooting range specifying unit 24, and a motion detection unit 25, the following various processors (Processor) can be used. As described above, in addition to a CPU, which is a general-purpose processor that executes software (program) and functions as various processing units, the above-mentioned various processors include a programmable logic device (Programmable Logic Device: PLD), which is a processor whose circuit configuration can be changed after manufacturing, such as an FPGA (Field Programmable Gate Array), and a dedicated electric circuit, which is a processor having a circuit configuration specifically designed to execute specific processes, such as an ASIC (Application Specific Integrated Circuit).

[0082] One processing unit may be composed of one of these various processors, or may be composed of a combination of two or more processors of the same type or different types (for example, a combination of multiple FPGAs or a combination of a CPU and an FPGA). Also, a plurality of processing units may be composed of one processor.

[0083] As an example of configuring a plurality of processing units with one processor, firstly, as represented by computers such as clients and servers, there is a form in which one processor is configured by a combination of one or more CPUs and software, and this processor functions as a plurality of processing units. Secondly, as represented by a system on chip (System On Chip: SoC), there is a form in which a processor that realizes the functions of an entire system including a plurality of processing units with one IC (Integrated Circuit) chip is used. Thus, as a hardware structure, the various processing units are configured using one or more of the above-mentioned various processors.

[0084] Furthermore, as the hardware structure of these various processors, more specifically, an electric circuit (Circuitry) combined with circuit elements such as semiconductor elements can be used.

[0085] Hereinafter, the appended claims of the present disclosure will be described. (Appended Claim 1) Comprising at least one processor, The processor, acquires a camera image generated by video-recording a subject on a bed with a camera, selects one detection model from a plurality of detection models including a first detection model that emphasizes the frame rate at the time of detecting the feature points and a second detection model that emphasizes the accuracy at the time of detecting the feature points, which are constructed to detect a plurality of feature points on the subject included in the camera image, detects the plurality of feature points on the subject included in the camera image by the selected detection model, An information processing apparatus that specifies the shooting range of the subject based on the plurality of feature points. (Appended Claim 2) The information processing apparatus according to appended claim 1, wherein the processor selects the detection model according to the shooting part of the subject. (Appended Claim 3) The information processing apparatus according to appended claim 1 or 2, wherein the processor determines the detection accuracy of the feature points, and when the detection accuracy is equal to or higher than a reference, specifies the shooting range based on the feature points, and when the detection accuracy is lower than the reference, issues a warning. (Appended Claim 4) The processor detects the movement of the subject based on the camera image, When the movement of the subject is equal to or higher than a reference, the shooting range is specified based on the feature points detected using the first detection model, and when the movement of the subject is smaller than the reference, the shooting range is specified based on the feature points detected using the second detection model. The information processing apparatus according to appended claim 1. (Appended Claim 5) The information processing apparatus according to claim 3, wherein the processor selects the first detection model, detects the feature points from the camera image, and detects the movement of the subject based on the feature points. (Claim 6) The information processing apparatus according to claim 4 or 5, wherein the processor determines the detection accuracy of the feature points, and when the detection accuracy is equal to or higher than a reference, specifies the shooting range based on the feature points, and when the detection accuracy is lower than the reference, issues a warning. (Claim 7) The information processing apparatus according to claim 1, wherein the processor selects the first detection model, detects the feature points from the camera image, determines the detection accuracy of the feature points, and when the detection accuracy is equal to or higher than a first reference, specifies the shooting range based on the feature points detected using the first detection model, and when the detection accuracy is lower than the first reference, selects the second detection model and specifies the shooting range based on the feature points detected using the second detection model. (Claim 8) The information processing apparatus according to claim 7, wherein the processor determines the detection accuracy of the feature points detected using the second detection model, and when the detection accuracy is equal to or higher than a second reference, specifies the shooting range based on the feature points, and when the detection accuracy is lower than the second reference, issues a warning. (Claim 9) The information processing apparatus according to any one of claims 1 to 8, wherein the processor derives the movement distance of the bed based on the shooting range. (Claim 10) The information processing apparatus according to any one of claims 1 to 9, wherein the processor displays a human body image simulating a human body on a display, and draws a shooting start line and a shooting end line based on the shooting range on the human body image. (Claim 11) The information processing apparatus according to any one of claims 1 to 10, wherein the shooting range is a shooting range when shooting a positioning image acquired before actual shooting of the subject. (Claim 12) The first detection model has a smaller amount of computation than the second detection model, and has a faster processing speed for detecting the feature points. The second detection model has a larger amount of computation than the first detection model, and has a slower processing speed for detecting the feature points but has a higher detection accuracy. The information processing apparatus according to any one of appendices 1 to 11. (Appendix 13) The computer acquires a camera image generated by video-capturing a subject on a bed using a camera. A plurality of detection models including a first detection model that emphasizes the frame rate at the time of detecting the feature points and a second detection model that emphasizes the accuracy at the time of detecting the feature points, which are constructed to detect a plurality of feature points on the subject included in the camera image, are provided. One detection model is selected from the plurality of detection models. The selected detection model is used to detect the plurality of feature points on the subject included in the camera image. An information processing method for specifying the imaging range of the subject based on the plurality of feature points. (Appendix 14) A procedure for acquiring a camera image generated by video-capturing a subject on a bed using a camera, A procedure for selecting one detection model from a plurality of detection models including a first detection model that emphasizes the frame rate at the time of detecting the feature points and a second detection model that emphasizes the accuracy at the time of detecting the feature points, which are constructed to detect a plurality of feature points on the subject included in the camera image, A procedure for detecting the plurality of feature points on the subject included in the camera image by the selected detection model, An information processing program that causes a computer to execute a procedure for specifying the imaging range of the subject based on the plurality of feature points.

Explanation of Signs

[0086] 1 CT apparatus 2 Gantry 3 Bed 3A Bed section 3B Base 3C Drive section 4 Console 5 Opening 7 Camera 10, 10A Information Processing Device 11 CPU 12 Information Processing Program 13 Storage 14 Display 15 Input Device 16 Memory 17 Network I / F 18 Bus 20 Shooting Control Unit 21 Camera Control Unit 22 Selection Unit 23 Feature Point Detection Unit 24 Shooting Range Specifying Unit 25 Motion Detection Unit H Subject

Claims

1. Comprising at least one processor, The processor is configured to: Obtain a camera image generated by video-recording a subject on a bed using a camera, Select one detection model from a plurality of detection models including a first detection model that emphasizes the frame rate at the time of detecting the feature points and a second detection model that emphasizes the accuracy at the time of detecting the feature points, which are constructed to detect a plurality of feature points on the subject included in the camera image, Detect the plurality of feature points on the subject included in the camera image using the selected detection model, An information processing apparatus that specifies the imaging range of the subject based on the plurality of feature points.

2. The information processing apparatus according to claim 1, wherein the processor selects the detection model according to the imaging part of the subject.

3. The information processing apparatus according to claim 1 or 2, wherein the processor determines the detection accuracy of the feature points, and when the detection accuracy is equal to or higher than a reference, specifies the imaging range based on the feature points, and when the detection accuracy is lower than the reference, issues a warning.

4. The processor is configured to detect the movement of the subject based on the camera image, When the movement of the subject is equal to or higher than a reference, specify the imaging range based on the feature points detected using the first detection model, and when the movement of the subject is smaller than the reference, specify the imaging range based on the feature points detected using the second detection model. The information processing apparatus according to claim 1.

5. The information processing apparatus according to claim 3, wherein the processor selects the first detection model to detect the feature points from the camera image, and detects the movement of the subject based on the feature points.

6. The information processing apparatus according to claim 4 or 5, wherein the processor determines the detection accuracy of the feature points, and when the detection accuracy is equal to or higher than a reference, specifies the imaging range based on the feature points, and when the detection accuracy is lower than the reference, issues a warning.

7. The processor selects the first detection model to detect the feature points from the camera image, determines the detection accuracy of the feature points, and when the detection accuracy is equal to or higher than a first criterion, specifies the shooting range based on the feature points detected using the first detection model. When the detection accuracy is lower than the first criterion, the processor selects the second detection model and specifies the shooting range based on the feature points detected using the second detection model. The information processing apparatus according to claim 1.

8. The processor determines the detection accuracy of the feature points detected using the second detection model, and when the detection accuracy is equal to or higher than a second criterion, specifies the shooting range based on the feature points. When the detection accuracy is lower than the second criterion, the processor issues a warning. The information processing apparatus according to claim 7.

9. The processor derives the moving distance of the bed based on the shooting range. The information processing apparatus according to claim 1.

10. The processor displays a human body image simulating a human body on a display and draws a start line and an end line of shooting based on the shooting range on the human body image. The information processing apparatus according to claim 1.

11. The shooting range is a shooting range when shooting a positioning image acquired before actual shooting of the subject. The information processing apparatus according to claim 1.

12. The first detection model has a smaller amount of computation and a faster processing speed for detecting the feature points than the second detection model. The second detection model has a larger amount of computation and a slower processing speed for detecting the feature points than the first detection model, but has a higher detection accuracy. The information processing apparatus according to claim 1.

13. A computer acquires a camera image generated by video shooting a subject on a bed with a camera, selects one detection model from a plurality of detection models including a first detection model that emphasizes the frame rate at the time of detecting the feature points and a second detection model that emphasizes the accuracy at the time of detecting the feature points, which are constructed to detect a plurality of feature points on the subject included in the camera image, detects the plurality of feature points on the subject included in the camera image using the selected detection model, and specifies the shooting range of the subject based on the plurality of feature points. An information processing method.

14. A procedure for acquiring a camera image generated by video shooting a subject on a bed with a camera, A procedure for selecting one detection model from a plurality of detection models including a first detection model that emphasizes the frame rate at the time of detecting the feature points and is constructed to detect a plurality of feature points on the subject included in the camera image, and a second detection model that emphasizes the accuracy at the time of detecting the feature points; A procedure for detecting the plurality of feature points on the subject included in the camera image by the selected detection model; An information processing program that causes a computer to execute a procedure for specifying a shooting range of the subject based on the plurality of feature points.

Images