Medical image analysis support system and method for providing medical image analysis results

Abstract

A medical image analysis support system according to an embodiment of the present invention includes a lesion reading unit that reads medical image information including a plurality of medical image images of a patient's body, estimates the presence of a lesion from the plurality of medical image images, and detects the position and information of the estimated lesion, and an image reading information generation unit that generates image reading information in which a marking related to the lesion is displayed on a scout image including at least one of the medical image images in which the lesion is present and a medical image generated based on the plurality of medical image images to represent the lesion, wherein the marking is displayed to correspond to the position of the lesion in the scout image, and is displayed in a different diagram depending on the type of the lesion.

Description

[Technical field]

[0001] The present invention relates to a medical image analysis support system and a method for providing medical image analysis results, and more particularly, to a medical image analysis support system for supporting diagnosis of lesions in medical images and a method for providing medical image analysis results using the same. [Background technology]

[0002] Many hospitals use picture archiving and communications systems (PACS) that work with equipment that captures medical images such as X-ray, CT, and MRI images.

[0003] The medical image information system digitally stores the captured medical images and transmits them to the clinician's PC, allowing the clinician to view the medical images on the PC and diagnose the patient's condition.

[0004] In particular, CT and MRI images are composed of 300 consecutive cross-sectional images, placing a burden on clinicians to check all 300 images to ensure that no lesions are missed and to make an accurate diagnosis. Summary of the Invention [Problem to be solved by the invention]

[0005] An object of the present invention is to provide a medical image analysis support system and a method for providing medical image analysis results that allow a user to more conveniently and accurately diagnose lesions.

[0006] The object of the present invention is not limited to the above-mentioned object, and other object not mentioned will be clearly understood by a person skilled in the art from the following description. [Means for solving the problem]

[0007] In order to solve the above problems, a medical video analysis support system according to an embodiment of the present invention includes a lesion reading unit that reads medical video information including a plurality of medical video images of a patient's body, estimates the presence of a lesion from the plurality of medical video images, and detects the position and information of the estimated lesion, and an interpretation information generation unit that generates interpretation information in which a marking related to the lesion is displayed on a scout image that includes at least one of the medical video images in which the lesion is present and medical video images generated based on the plurality of medical video images to represent the lesion, wherein the marking is displayed to correspond to the position of the lesion in the scout image, and is displayed in different diagrams depending on the type of the lesion.

[0008] In order to solve the above problems, a method for providing medical image analysis results according to an embodiment of the present invention includes interpreting medical image information including a plurality of medical image images of a patient's body, estimating the presence of a lesion from the plurality of medical image images, detecting the position and information of the estimated lesion, and generating interpretation information in which a marking related to the lesion is displayed on a scout image including at least one of the medical image images in which the lesion is present and a medical image generated based on the plurality of medical image images to represent the lesion, wherein the marking is displayed to correspond to the position of the lesion in the scout image and is displayed in different diagrams depending on the type of the lesion.

[0009] Further details of the invention are included in the detailed description and drawings. Effect of the Invention

[0010] According to the embodiment of the present invention, at least the following effects are obtained.

[0011] Users can diagnose lesions more conveniently and accurately.

[0012] The effects of the present invention are not limited to the above-mentioned examples, and various other effects are included in this specification. [Brief description of the drawings]

[0013] [Figure 1] 1 is a diagram illustrating a system in which a medical image analysis support system according to an embodiment of the present invention is used. [Diagram 2] 4 is a flowchart illustrating a method for providing a medical image analysis result according to an embodiment of the present invention. [Diagram 3] 1 is a schematic diagram of medical imaging information including a plurality of medical imaging images according to one embodiment; [Figure 4] FIG. 2 shows a first medical video image in which a first lesion is detected. [Diagram 5] FIG. 13 shows a second medical imaging image in which a second lesion is detected. [Figure 6] FIG. 13 is a diagram for explaining a method for generating a scout image. [Figure 7] FIG. 13 shows a first scout image in which a marking is displayed on a first lesion. [Figure 8] FIG. 13 shows a second scout image in which a marking is displayed on a second lesion. [Figure 9] FIG. 13 is a diagram showing an example of a scout image in which markings are displayed on multiple lesions. [Figure 10] 3 is a flowchart for explaining step S30 in FIG. 2. [Figure 11] FIG. 2 is a diagram illustrating a schematic configuration of DICOM format data. [Figure 12] 11A and 11B are diagrams for explaining a method of utilizing a scout image according to the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0014] The advantages and features of the present invention, as well as the methods for achieving them, will become apparent from the following detailed description of the embodiments in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, and can be realized in various different forms. The present embodiments are provided solely for the purpose of making the disclosure of the present invention complete and fully conveying the scope of the invention to those skilled in the art to which the present invention pertains, and the present invention is defined only by the claims.

[0015] In addition, the embodiments described herein are described with reference to cross-sectional views and / or schematic diagrams that are ideal exemplary views of the present invention. Therefore, the shapes of the exemplary views may be modified due to manufacturing techniques and / or tolerances. In addition, in each drawing shown in the present invention, each component may be illustrated somewhat enlarged or reduced in size for convenience of explanation. The same reference numerals refer to the same components throughout the specification.

[0016] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, the present invention will be described with reference to the drawings illustrating a medical image analysis support system and a method for providing a medical image analysis result using the same according to an embodiment of the present invention.

[0017] FIG. 1 is a schematic diagram illustrating a system in which a medical image analysis support system according to an embodiment of the present invention is used, and FIG. 2 is a flowchart illustrating a method for providing medical image analysis results according to an embodiment of the present invention.

[0018] 1, a medical image analysis support system 1 according to an embodiment of the present invention is communicably connected to a PACS (Picture Archiving Communications System, 2). The PACS is a medical image information system that is widely used in hospitals today.

[0019] In this embodiment, the medical image analysis support system 1 will be described based on PACS2 as an example of a medical image information system to which it is connected. However, the medical image analysis support system 1 according to one embodiment of the present invention can also be connected and used in a communicative manner with other medical image information systems other than PACS2.

[0020] The medical image analysis support system 1 can be located in a remote location outside a hospital, and in this case, can be communicatively connected to the PACS 2 via a wired or wireless remote communication network. For example, the medical image analysis support system 1 and the PACS 2 can be connected via a wide area network such as the Internet.

[0021] Alternatively, the medical image analysis support system 1 may be located in a hospital that operates the PACS 2. In this case, the medical image analysis support system 1 may be communicably connected to the PACS 2 via a wired or wireless communication network in the hospital.

[0022] In addition, the medical image analysis support system 1 can be communicatively connected to a terminal having an input device and a screen. The terminal can be an electronic device such as a desktop, a notebook computer, a tablet, or a smartphone.

[0023] The medical image analysis support system 1 is located at a remote location from the terminal and can be communicatively connected to the terminal via a wired or wireless remote communication network. For example, the medical image analysis support system 1 can be connected to the terminal in a cloud-based manner to provide services.

[0024] Alternatively, the medical image analysis support system 1 can be provided as software installed on a terminal.

[0025] Alternatively, some components of the medical image analysis support system 1 may be provided as software installed on a terminal, and other components may be provided in a remote location and communicatively connected to the terminal via a wired or wireless remote communication network.

[0026] Referring to FIG. 1, a medical image analysis support system 1 according to an embodiment of the present invention may include a medical image information receiving unit 10, a lesion image interpretation unit 20, an image interpretation information generating unit 30, and an image interpretation information transmitting unit 40.

[0027] Also, referring to FIG. 2, a method for providing medical image analysis results according to one embodiment of the present invention may include step S10 of acquiring medical image information, step S20 of interpreting the medical image information to detect lesions, step S30 of generating interpretation information, and step S40 of transmitting the generated interpretation information.

[0028] In step S10 of acquiring medical image information, the medical image information receiving unit 11 receives medical image information stored in the PACS 2 from the PACS 2 or generated by a medical imaging device linked to the PACS 2. The medical image information may be data in a DICOM (Digital Imaging Communication in Medicine) format, which is a standardized medical image information that is currently widely used. In this embodiment, the description will be based on medical image information including medical images captured by a medical imaging device such as a CT (Computer Tomography) or an MRI (Magnetic Resonance Imaging), in which a plurality of medical image images of consecutively captured cross sections of a patient's body are configured as one set.

[0029] In step S20 of interpreting the medical image information to detect a lesion, the lesion interpretation unit 20 interprets a plurality of medical image images included in the medical image information acquired by the medical image information receiving unit 10 to estimate the presence of a lesion and detects position information of the estimated lesion within the image, etc. The lesion interpretation unit 20 can be configured to interpret the medical image based on artificial intelligence (AI) and detect a lesion estimated to be present within the image.

[0030] For example, the lesion interpretation unit 20 can detect nodules in the lungs by using a CAD (Computer Aided Detection) algorithm for images of the lungs in a plurality of medical images (e.g., CT images) included in the medical image information. The lesion interpretation unit 20 can interpret the position, size, volume, type, Hounsfield, category, etc. of the detected nodules.

[0031] The types can be classified as non-solid nodules, part-solid nodules, solid nodules, calcification nodules, and unknown. The categories can be classified as 1, 2, 3, 4A, and 4B according to Lung-RADs. The categories can be classified based on the size, volume, type, and Hounsfield information of the detected nodules.

[0032] FIG. 3 is a schematic diagram of medical imaging information including a plurality of medical imaging images according to one embodiment.

[0033] 3, medical image information 100 may include a plurality of medical image images 101. For example, the medical image information 100 may include 360 ​​axial slice images 101.

[0034] The lesion interpretation unit 20 interprets each medical image 101, estimates the presence of a lesion in each medical image 101, and detects the position and information of the estimated lesion.

[0035] For ease of explanation, the following description will be based on the case where the lesion interpretation unit 20 estimates the presence of a lesion in two medical video images 102 and 103.

[0036] FIG. 4 shows a first medical image in which a first lesion is detected, and FIG. 5 shows a second medical image in which a second lesion is detected.

[0037] Referring to FIG. 3, each pixel in each medical video image 101 can be represented by three-axis coordinates (x, y, z).

[0038] 4, a first lesion N1 found in a first medical image 102 is located on the coordinates (X1, Y1, Z1). The z-axis coordinates of all pixels in the first medical image 102 are the same as Z1.

[0039] 5, a second lesion N2 found in the second medical image 103 is located on the coordinates (X2, Y2, Z2). The z-axis coordinates of all pixels in the second medical image 103 are the same as Z2.

[0040] The lesion reading unit 20 can read not only the positional information of the first lesion N1 and the second lesion N2, but also the size, volume, type, Hounsfield, category, etc. of the first lesion N1 and the second lesion N2, as described above.

[0041] In step S30 of generating image interpretation information, the image interpretation information generating section 30 generates image interpretation information using information related to the lesion detected by the lesion image interpretation section 20.

[0042] The image interpretation information generating unit 30 can generate a scout image as image interpretation information.

[0043] FIG. 6 is a diagram for explaining a method of generating a scout image, FIG. 7 is a diagram showing a first scout image in which a marking is displayed on a first lesion, and FIG. 8 is a diagram showing a second scout image in which a marking is displayed on a second lesion.

[0044] 3 to 6, a first lesion N1 is located on a first Y plane 201, and a second lesion N2 is located on a second Y plane 202. As shown in FIG.

[0045] The image interpretation information generating unit 30 can generate a coronal image in which the first lesion N1 is depicted as a scout image in which the first lesion N1 is depicted. In this case, the image interpretation information generating unit 30 generates a coronal image in which the first lesion N1 is depicted as a scout image in which the first lesion N1 is depicted in the multiple medical video images 101. -1 ) can be combined to generate a coronal image depicting the first lesion N1 as a first scout image 300, as shown in FIG.

[0046] The image interpretation information generating unit 30 can generate a coronal image in which the second lesion N2 is depicted as a scout image in which the second lesion N2 is depicted. In this case, the image interpretation information generating unit 30 generates a coronal image in which the second lesion N2 is depicted as a scout image in which the second lesion N2 is depicted as a scout image in which the second lesion N2 is depicted. In this case, the image interpretation information generating unit 30 generates a scout image in which the second lesion N2 is depicted as .... -2 ) can be combined to generate a coronal image depicting the second lesion N2 as a second scout image 400, as shown in the drawing.

[0047] When the interpretation information generation unit 30 generates a sagittal image as a scout image, the interpretation information generation unit 30 can generate the sagittal image as a scout image by combining images of pixels in multiple medical video images 101 whose x-axis coordinate values ​​are the same as the x-axis coordinate of the lesion.

[0048] Alternatively, when the image interpretation information generating unit 30 uses an axial image as a scout image, the image interpretation information generating unit 30 can use the medical video image 101 as the scout image without generating a new scout image.

[0049] Meanwhile, referring to Fig. 7, the image interpretation information generating unit 30 can generate image interpretation information by displaying a first marking 301 for a first lesion N1 on a first scout image 300. Also, referring to Fig. 8, the image interpretation information generating unit 30 can generate image interpretation information by displaying a second marking 401 for a second lesion N2 on a second scout image 400.

[0050] 7 and 8, the markings 301, 401 can be displayed to correspond to the location of the lesion in the scout image. For example, the markings 301, 401 can be displayed to overlap the location of the lesion in the scout image, to indicate the location of the lesion, or to surround at least a portion of the lesion.

[0051] The interpretation information generating unit 30 can display the markings 301, 401 in different diagrams depending on the type of lesion, so that a user checking the scout image can easily recognize the type of lesion for which the marking is displayed simply by checking the marking.

[0052] For example, if the lesion is a nodule, the nodule may be classified into types such as non-solid nodule, part-solid nodule, solid nodule, calcification nodule, and unknown.

[0053] The image interpretation information generating unit 30 may, for example, display a dotted line diagram for a non-solid nodule, a solid line diagram for a part-solid nodule, a double solid line diagram for a solid nodule, a filled diagram for a calcification nodule, and an X-shaped diagram for an unknown.

[0054] (See Figure 9.) Markings can be shown in diagrams with differences not only in line type but also in line thickness, line color, etc.

[0055] Meanwhile, the image interpretation information generating unit 30 may display the size of the diagram in relation to the size of the lesion. That is, the image interpretation information generating unit 30 may display a diagram with larger markings as the size of the lesion increases. Thus, a user checking the scout image can easily recognize the absolute size or relative size of the lesion on which the markings are displayed by simply checking the markings.

[0056] On the other hand, the image interpretation information generating unit 30 may display markings 302, 303, 402, and 403 related to lesions located on different Y planes on each scout image 300, 400, as shown in Fig. 7 and Fig. 8. In this case, the markings 301, 401 related to lesions located on the same plane as the scout images 300, 400 can be displayed to be distinguished from the markings 302, 303, 402, and 403 related to lesions located on a plane different from the scout images 300, 400. For example, the markings 301, 401 related to lesions located on the same plane as the scout images 300, 400 can be displayed darker, bolder, or in a different color than the markings 302, 303, 402, and 403 related to lesions located on a plane different from the scout images 300, 400.

[0057] FIG. 9 is a diagram showing an example of a scout image in which markings are displayed on each of a plurality of lesions.

[0058] Referring to FIG. 9, the interpretation information generating unit 30 can also generate interpretation information in which markings 501, 502, 503, 504, 505, and 506 relating to all lesions interpreted in the medical image information 100 are displayed on one scout image 500.

[0059] In step S40 of transmitting the generated interpretation information, the interpretation information transmitting unit 40 can transmit the interpretation information generated through step S30 to the PACS2.

[0060] The image interpretation information transmitted by the image interpretation information transmission unit 40 to the PACS 2 may be data in DICOM format. Therefore, the hospital receiving the image interpretation information can check the scout image and markings included in the image interpretation information using the PACS 2.

[0061] The interpretation information may include at least one of a scout image generated for each lesion and displaying markings, and a single scout image displaying markings for all lesions, as shown in Figures 7 and 8.

[0062] FIG. 10 is a flowchart for explaining step S30 in FIG.

[0063] Referring to Figure 10, step S30 of generating interpretation information may include step S31 of displaying markings related to the lesion on a scout image, step S32 of determining coordinate information of the lesion, and step S33 of determining header information of the interpretation information.

[0064] Step S31 of displaying markings relating to a lesion on a scout image has already been described with reference to FIGS. 6 to 8, and therefore will not be described further.

[0065] The medical image analysis support system and the method for providing a medical image analysis result according to an embodiment of the present invention may further include step S32 and step S33.

[0066] In step S32 of determining coordinate information of a lesion, the image interpretation information generating unit 30 calculates coordinate information of the detected lesion.

[0067] FIG. 11 is a diagram showing an outline of the structure of data in the DICOM format.

[0068] A plurality of slice image information 110 constituting the DICOM format medical image information 100 includes DICOM header information 111 and DICOM image information 112. The DICOM image information 112 includes information for constituting the slice image 101 described above.

[0069] The DICOM header information 111 records various information related to the DICOM image information 112. For example, the DICOM header information 111 includes patient information, imaging device information, image position patient information 111a, pixel spacing information 111b, and the like.

[0070] In step S32, the image interpretation information generating unit 30 calculates coordinate information of the detected lesion using the image position patient information 111a and the pixel interval information 111b.

[0071] Step S32 will be described in more detail with reference to FIG.

[0072] The image interpretation information generating unit 30 can confirm image position patient information 111a and pixel interval information 111b from the DICOM header information 111 of the first medical video image 102 in which the first lesion N1 is detected.

[0073] X1 means the number of pixels from the upper left corner of the first medical video image 102 in the x direction (see FIG. 3) to the pixel where the first lesion N1 is detected.

[0074] Y1 means the number of pixels from the upper left corner of the first medical video image 102 in the y direction (see FIG. 3) to the pixel where the first lesion N1 is detected.

[0075] The pixel interval information 111b refers to information relating to the interval between pixels.

[0076] The image position patient information 111a includes information regarding the reference coordinates of the first medical image 102. Referring to Figure 3, the medical image information 100 includes a plurality of axial medical images 101, and the image position patient information 111a of each axial medical image 101 has the same x and y coordinates and is different only in z coordinate.

[0077] The coordinates indicated by the image position patient information 111 a may be the upper left corner point of each medical image 101 or the center point of each medical image 101 .

[0078] The image interpretation information generating unit 30 can calculate the coordinate information of the first lesion N1 using the following mathematical formula.

[0079] X real =u+S x X1 Y real =v+S y Y1

[0080] The coordinates indicated by the image position patient information 111a are (u, v, w), Sx is the x-axis pixel interval in the pixel interval information 111b, and Sy is the y-axis pixel interval in the pixel interval information 111b.

[0081] X real means the actual x-coordinate of the first lesion N1, and Y real means the actual y coordinate of the first lesion N1.

[0082] The actual z coordinate of the first lesion N1 is the same as w.

[0083] The image interpretation information generating unit 30 obtains coordinate information of the first lesion N1 as (X real ,Y real ,w) can be generated.

[0084] In step S33 of determining header information of the image interpretation information, the image interpretation information generating unit 30 determines image position patient information from the DICOM header information for the scout image as coordinate information of the lesion calculated in step S32.

[0085] That is, the image interpretation information generating unit 30 extracts image position patient information from the DICOM header information of the image interpretation information including the scout image for the first lesion N1, as follows: real ,Y real ,w).

[0086] Alternatively, the image interpretation information generating unit 30 may generate image position patient information from the DICOM header information of the image interpretation information including the scout image for the first lesion N1, as follows: real ,w).

[0087] In step S40 of transmitting the generated interpretation information, the interpretation information transmitting unit 40 can transmit the interpretation information generated through steps S31 to S32 to the PACS 2.

[0088] 12 is a diagram for explaining a method of using a scout image according to the present invention, which is a diagram for roughly illustrating an execution screen of software for checking medical image information in DICOM format using a PACS.

[0089] 12, a tool area A may be displayed on one side of the screen, a scout image area B may be displayed in the center of the screen, and a medical video image area C may be displayed on the other side of the screen. Although a screen configuration divided into three parts is shown for ease of explanation, the screen configuration may be modified in various ways. For example, the order of at least some of the tool area A, scout image area B, and medical video image area C may be changed, or at least some of the three areas (A, B, C) may be further divided to display additional information or images.

[0090] When the software is executed to open interpretation information including a scout image, etc., a scout image is displayed in the scout image area B with a marking 301 at the position of the lesion, as shown in FIG.

[0091] Among the software tools for checking medical image information in DICOM format, when a marking 301 or a lesion is clicked using a 3D cursor A1, a medical image in which the lesion is located is displayed in a medical image area C. The medical image is an original medical image included in the medical image information 100, and may be an image in which markings, etc. are not displayed.

[0092] The software uses the coordinates in the scout image clicked by the 3D cursor to cause the corresponding medical image to be displayed in the medical image area C.

[0093] However, as shown in FIG. 12, when the scout image is a coronal image, clicking on the marking 301 or lesion with the 3D cursor A1 identifies the x and z coordinates of the marking 301 or lesion, but not the y coordinate.

[0094] Therefore, even if the marking 301 or the lesion is clicked using the 3D cursor A1 in the scout image of the interpretation information in which steps S32 and S33 have not been performed, the axial medical image displayed in the medical image area C can be identified using the clicked x-coordinate and z-coordinate, but the position of the lesion N1 cannot be identified in the axial medical image. Therefore, the user needs to search for the position of the lesion N1 in the axial medical image again.

[0095] However, steps S32 and S33 result in the image position patient information being (X real ,Y real In the case of image interpretation information indicating (X, w), the x and z coordinates of the clicked position in the scout image using the 3D cursor A1 and the image position patient information are (X, real ,Y real , w) to identify the axial medical image displayed in the medical image area C, and at the same time, to determine the position of the lesion N1 in the axial medical image (X click ,Y real ,Z click ) or (X real ,Y real,Z click ) can be specified as X click means the x-coordinate where the 3D cursor A1 was clicked, and Z click means the x-coordinate clicked using 3D cursor A1.

[0096] Referring to FIG. 12, within the medical image area C, click or X real A vertical line corresponding to the value, Y real A horizontal line corresponding to the value can be displayed, and the intersection of the vertical and horizontal lines is the location of lesion N1, allowing the user to easily identify lesion N1 within the medical imaging image.

[0097] Or, in the medical image area C, X click or X real A vertical line and Y corresponding to the value real Alternatively, only one of the two lines corresponding to the values ​​may be displayed, and in this case, the user can find the lesion N1 on the vertical or horizontal line in the medical image, making it easier to identify the lesion N1 in the medical image.

[0098] Alternatively, steps S32 and S33 may be used to set the image position patient information in the DICOM header information to (u,Y real In the case of image interpretation information indicating (u, Y , w), the x and z coordinates clicked using the 3D cursor A1 in the scout image and the image position patient information are (u, Y real , w) to identify the axial medical image displayed in the medical image area C, and at the same time, to determine the position of the lesion in the axial medical image (X click ,Y real ,Z click ) can be identified.

[0099] It will be understood by those skilled in the art that the present invention can be embodied in other specific forms without changing the technical idea or essential features of the present invention. Therefore, it should be understood that the above-described embodiments are illustrative in all respects and are not limiting. The scope of the present invention is indicated by the claims below, not by the above detailed description, and all modifications or alterations derived from the meaning and scope of the claims and their equivalent concepts should be interpreted as being included in the scope of the present invention.

Claims

1. a lesion image reading unit that reads medical image information including a plurality of medical image images of a patient's body, estimates the presence of a lesion from the plurality of medical image images, and detects the position and information of the estimated lesion; and an image interpretation information generating unit that generates image interpretation information in which a marking related to the lesion is displayed on a scout image including at least one of the medical image images in which the lesion exists and a medical image generated based on the medical image images to represent the lesion, among the plurality of medical image images; The markings are displayed in the scout image to correspond to the location of the lesion, and are displayed in different diagrams depending on the type of the lesion.

2. The medical image analysis support system according to claim 1 , wherein the size of the diagram is displayed so as to be related to the size of the lesion.

3. The types of lesions include non-solid nodules, part-solid nodules, and solid nodules; 2. The medical image analysis support system according to claim 1, wherein the diagram is displayed using a dotted line, a solid line, or a double solid line depending on the type of the lesion.

4. The medical image information and the interpretation information include data in a Digital Imaging and Communications in Medicine (DICOM) format, The medical image analysis support system of claim 1, wherein the interpretation information generation unit determines image position patient coordinate information in the DICOM header information of the interpretation information based on image position patient coordinate information in the DICOM header information of the medical image in which the lesion is present and position information of the lesion within the medical image in which the lesion is present.

5. The medical image analysis support system of claim 4, wherein the interpretation information generation unit determines position information of the lesion based on pixel spacing information in DICOM header information of the medical image in which the lesion is present and the number of pixels between the lesion and a reference point corresponding to the coordinate information of the image position of the patient.

6. interpreting medical image information including a plurality of medical images of a patient's body, estimating the presence of a lesion from the plurality of medical images, and detecting a position and information of the estimated lesion; generating image interpretation information in which a marking relating to the lesion is displayed on a scout image including at least one of a medical image in which the lesion is present and a medical image generated based on the plurality of medical images so as to represent the lesion; The marking is displayed in the scout image to correspond to a position of the lesion, and is displayed in different diagrams depending on the type of the lesion.

7. The method of claim 6, wherein the size of the diagram is displayed in relation to the size of the lesion.

8. The types of lesions include non-solid nodules, part-solid nodules, and solid nodules; The method of claim 6, wherein the diagram is displayed with a dotted line, a solid line, or a double solid line depending on the type of the lesion.

9. The method for providing medical image analysis results according to claim 6, further comprising determining image position patient coordinate information in the DICOM header information of the medical image in which the lesion is present, and image position patient coordinate information in the DICOM header information of the interpretation information based on position information of the lesion within the medical image in which the lesion is present.

10. The method for providing medical image analysis results according to claim 9, further comprising determining position information of the lesion based on pixel interval information in DICOM header information of the medical image in which the lesion is present and the number of pixels between the lesion and a reference point corresponding to the coordinate information of the image position of the patient.

Images