Lesion position determination and medical image intelligent processing method and system

Abstract

The disclosure provides a lesion position determination and medical image intelligent processing method and system, and relates to the technical field of lesion position determination and medical image intelligent processing. The lesion position determination method comprises the following steps: determining a lesion mask image and an arterial vessel mask image corresponding to a chemotherapy lesion or a surgical lesion by using a preset part medical image; and determining whether the lesion position corresponding to the chemotherapy lesion or the surgical lesion is close to an arterial vessel according to the lesion mask image and the arterial vessel mask image. The embodiment of the disclosure can realize lesion position determination and medical image intelligent processing.

Description

Technical Field

[0001] This disclosure relates to the fields of lesion location determination and intelligent medical image processing technology, and in particular to a method and system for lesion location determination and intelligent medical image processing. Background Technology

[0002] The location of the lesion and whether it is adjacent to an artery are of great clinical significance in indicating the type of disease, assisting in differential diagnosis, and in subsequent chemotherapy and surgery.

[0003] Some diseases have specific vascular proximity characteristics. For example, pulmonary artery sarcoma often originates from the pulmonary artery intima and is closely related to the main pulmonary artery or its branches; while pulmonary metastases may spread hematogenously, are often located in the periphery of the lung, and have complex relationships with blood vessels. Furthermore, lesions adjacent to arteries need to be differentiated from vascular diseases (such as aneurysms and vascular embolisms).

[0004] If the lesion is close to an artery (distance <5mm), the operating space is limited during surgery. The blood vessel wall is thin and easily ruptured. Even a slight deviation may cause fatal massive bleeding, or lead to blood vessel rupture, organ ischemia and necrosis. The postoperative complication rate is as high as 40%. Some lesions near large blood vessels may be considered surgical contraindications because they cannot be safely removed, and other treatment methods need to be considered.

[0005] For lesions near arteries, tumor cells may be affected by blood flow, leading to dilution of chemotherapy drugs at the local tumor concentration. This can reduce the effectiveness of treatment and increase the risk of residual cancer cells and recurrence. For lesions near blood vessels, arterial infusion chemotherapy (such as hepatic artery infusion chemotherapy-IC) may be more effective, as it allows chemotherapy drugs to be directly injected into the artery supplying the tumor, increasing local drug concentration and reducing systemic toxicity. The dosage, timing, and duration of chemotherapy should be adjusted based on the relationship between the lesion and blood vessels to balance efficacy and safety.

[0006] Based on the above analysis, it is necessary to propose a technical solution for lesion location determination and intelligent medical image processing system to solve at least one of the following technical problems: the current need for manual determination of lesion location and whether it is adjacent to arteries is too subjective; the lack of intelligent determination of lesion location and whether it is adjacent to arteries causes a huge workload for doctors; and it makes it impossible to further effectively evaluate the prediction of radiotherapy efficacy. Summary of the Invention

[0007] This disclosure proposes a technical solution for lesion location determination and intelligent medical image processing and system.

[0008] According to one aspect of this disclosure, a method for determining the location of a lesion is provided, comprising: Using medical images of preset locations, determine the lesion mask image and arterial vessel mask image corresponding to the lesion to be treated or operated on; based on the lesion mask image and the arterial vessel mask image, determine whether the location of the lesion to be treated or operated on is adjacent to an artery; or, Using medical images of preset locations, determine the lesion mask and arterial mask images corresponding to the lesions to be treated or operated on; based on the lesion mask and arterial mask images, determine whether the location of the lesion to be treated or operated on is close to an artery.

[0009] Preferably, the step of determining the lesion mask image and arterial mask image corresponding to the lesion to be treated or the lesion to be operated on using medical images of preset locations includes: training a first preset deep learning segmentation network using a preset number of preset location medical training images and their corresponding lesion mask training images to obtain a preset lesion segmentation model; training a second preset deep learning segmentation network using a preset number of preset location medical training images and their corresponding arterial mask training images to obtain a preset arterial segmentation model; segmenting the lesion to be treated or the lesion to be operated on in the preset location medical images using the preset lesion segmentation model to obtain the corresponding lesion mask image; and segmenting the arteries using the preset arterial segmentation model to obtain the corresponding arterial mask image.

[0010] Preferably, the step of determining the lesion mask and arterial mask images corresponding to the lesion to be treated or the lesion to be operated on using medical images of preset locations includes: training a preset deep learning segmentation network using a preset number of preset location medical training images and their corresponding lesion mask and arterial mask training images to obtain a preset lesion and arterial segmentation model; and segmenting the lesion to be treated or the lesion to be operated on and the arterial vessels using the preset lesion and arterial segmentation model to obtain the corresponding lesion mask and arterial mask images.

[0011] Preferably, determining whether the location of the lesion to be treated or operated on is adjacent to an artery based on the lesion mask image and the artery mask image includes: calculating multiple positional distances between the artery edge mask position coordinates corresponding to the artery mask image and the lesion edge mask position coordinates corresponding to the lesion mask image; if the minimum positional distance among the multiple positional distances is less than or equal to a preset positional distance, then the lesion location is determined to be adjacent to an artery; otherwise, the lesion location is determined to be far from an artery.

[0012] Preferably, determining whether the location of the lesion to be treated or operated on is adjacent to an artery based on the lesion mask and artery mask images includes: calculating multiple distances between the coordinates of the artery edge mask and the coordinates of the lesion edge mask corresponding to the lesion mask and artery mask images; if the minimum distance among the multiple distances is less than or equal to a preset distance, the lesion is determined to be adjacent to an artery; otherwise, the lesion is determined to be far from an artery.

[0013] Preferably, before determining the lesion mask image and arterial mask image corresponding to the lesion to be treated or the lesion to be operated on using the medical image of the preset location, the method includes: detecting whether the lesion boundary in the lesion mask image corresponding to the medical image of the preset location is blurred and whether there is a spike sign at the edge of the lesion; if the lesion boundary in the lesion mask image is blurred and there is a spike sign at the edge of the lesion, then the lesion is configured as a lesion to be treated or a lesion to be operated on.

[0014] Preferably, before determining the lesion mask and arterial mask images corresponding to the lesion to be treated or the lesion to be operated on using the medical image of the preset location, the method includes: detecting whether the lesion boundary in the lesion and arterial mask image corresponding to the medical image of the preset location is blurred and whether there is a spike sign at the edge of the lesion; if the lesion boundary in the lesion and arterial mask image is blurred and there is a spike sign at the edge of the lesion, then the lesion is configured as a lesion to be treated or a lesion to be operated on.

[0015] Preferably, determining whether the lesion boundary is blurred includes: mapping the lesion location coordinates in the lesion masking image to a preset site medical image; calculating multiple gradients corresponding to the lesion location coordinates in the preset site medical image; if the gradient magnitude value corresponding to the multiple gradients is less than a preset gradient magnitude value, then the lesion boundary where the lesion location corresponding to the lesion location is less than the preset gradient magnitude value is determined to be blurred.

[0016] Preferably, if the gradient magnitude corresponding to the plurality of gradients is greater than or equal to a preset gradient magnitude, then the boundary of the lesion corresponding to the lesion location that is greater than or equal to the preset gradient magnitude is clearly defined.

[0017] Preferably, before mapping the lesion location coordinates in the lesion masking image to the preset site medical image, the method includes: performing an erosion operation on the lesion mask in the lesion masking image corresponding to the preset site medical image to obtain a lesion mask erosion image; and subtracting the lesion mask erosion image from the lesion masking image to obtain the lesion masking image.

[0018] Preferably, determining whether a spiky sign exists at the edge of the lesion includes: determining whether the medical image of the preset location is a two-dimensional or three-dimensional medical image; if the medical image of the preset location is a two-dimensional medical image, then calculating the ratio of the long axis to the short axis of each lesion based on the long axis and short axis of each lesion in the lesion mask image corresponding to the medical image of the preset location; if the ratio of the long axis to the short axis is greater than or equal to a first preset ratio of the long axis to the short axis, then determining that the corresponding lesion has a spiky sign; otherwise, determining that the corresponding lesion does not have a spiky sign; if the medical image of the preset location is a three-dimensional medical image, then calculating multiple ratios of the long axis to the short axis of each three-dimensional lesion based on the long axis and short axis of each three-dimensional lesion in the lesion mask image corresponding to the medical image of the preset location; if any one of the ratios of the long axis to the short axis is greater than or equal to the first preset ratio of the long axis to the short axis, then determining that the corresponding lesion has a spiky sign.

[0019] Preferably, if any one of the major-to-minor-diameter ratios is less than a first preset major-to-minor-diameter ratio, then the corresponding lesion does not have a spiky sign.

[0020] According to one aspect of this disclosure, a medical image intelligent processing method is provided, comprising: determining whether the location of the lesion to be treated or the lesion to be operated on is adjacent to an artery using the lesion location determination method described above; if the location of the lesion to be treated or the lesion to be operated on is adjacent to an artery, determining whether the artery passes through the lesion to be treated or the lesion to be operated on based on the lesion mask area corresponding to the lesion to be treated or the lesion to be operated on and the artery mask area corresponding to the artery.

[0021] Preferably, determining whether the artery passes through the lesion to be treated or operated on, based on the lesion mask region corresponding to the lesion to be treated or operated on and the artery mask region corresponding to the artery, includes: extracting the lesion mask position coordinates corresponding to the lesion mask region of the lesion to be treated or operated on and the artery mask region of the artery to be treated, respectively; if the artery mask position coordinates overlap with the lesion mask position coordinates, then it is determined that the artery passes through the lesion to be treated or operated on; otherwise, it is determined that the artery does not pass through the lesion to be treated or operated on.

[0022] Preferably, the method further includes: using one or more imaging features extracted from the medical image of the preset site, such as the average gray value corresponding to the lesion, the ratio of the long diameter to the short diameter corresponding to the lesion, whether the lesion is near an artery, and whether there is a spike sign at the edge of the lesion, to predict the efficacy of radiotherapy.

[0023] According to one aspect of this disclosure, a lesion location determination system is provided, comprising: The first determining unit is used to determine the lesion mask image and artery mask image corresponding to the lesion to be treated or the lesion to be operated on using a preset medical image of the site; the second determining unit is used to determine whether the location of the lesion to be treated or the lesion to be operated on is close to an artery based on the lesion mask image and the artery mask image.

[0024] According to one aspect of this disclosure, a lesion location determination system is provided, comprising: The third determining unit is used to determine the lesion mask and arterial mask image corresponding to the lesion to be treated or the lesion to be operated on using a preset medical image of the site; the fourth determining unit is used to determine whether the location of the lesion to be treated or the lesion to be operated on is close to an artery based on the lesion mask and the arterial mask image.

[0025] According to one aspect of this disclosure, a lesion location determination system is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the instructions stored in the memory to execute the lesion location determination method described above.

[0026] According to one aspect of this disclosure, a lesion location determination system is provided, comprising: a computer-readable storage medium storing a computer program / instructions and a bit stream thereon, wherein the computer program / instructions, when executed by a processor, implement the above-described lesion location determination method to generate the bit stream.

[0027] According to one aspect of this disclosure, a medical image intelligent processing system is provided, comprising: a computer program product configured with a computer program / instruction, which, when executed by a processor, implements the lesion location determination method described above.

[0028] According to one aspect of this disclosure, a medical image intelligent processing system is provided, comprising: a first determining unit, configured to determine, using a preset site medical image, a lesion mask image and an arterial vessel mask image corresponding to a lesion to be treated or a lesion to be operated on; a second determining unit, configured to determine, based on the lesion mask image and the arterial vessel mask image, whether the location of the lesion to be treated or the lesion to be operated on is adjacent to an arterial vessel; a third determining unit, configured to, if the location of the lesion to be treated or the lesion to be operated on is adjacent to an arterial vessel, determine, based on the lesion mask area corresponding to the lesion to be treated or the lesion to be operated on and the arterial vessel mask area corresponding to the arterial vessel, whether the arterial vessel passes through the lesion to be treated or the lesion to be operated on; and a first radiotherapy unit, configured to, if the arterial vessel passes through the lesion to be treated or the lesion to be operated on, perform radiotherapy on the arterial vessel passing through the lesion to be treated or the lesion to be operated on under ultrasound image guidance with a first radiotherapy dose, and perform radiotherapy on the lesion outside the arterial vessel under ultrasound image guidance with a second radiotherapy dose less than the first radiotherapy dose.

[0029] According to one aspect of this disclosure, a medical image intelligent processing system is provided, comprising: a fourth determining unit, configured to determine, using a preset site medical image, a lesion mask and an arterial vessel mask image corresponding to a lesion to be treated or a lesion to be operated on; a fifth determining unit, configured to determine, based on the lesion mask and the arterial vessel mask image, whether the location of the lesion to be treated or the lesion to be operated on is adjacent to an arterial vessel; a sixth determining unit, configured to, if the location of the lesion to be treated or the lesion to be operated on is adjacent to an arterial vessel, determine, based on the lesion mask area corresponding to the lesion to be treated or the lesion to be operated on and the arterial vessel mask area corresponding to the arterial vessel, whether the arterial vessel passes through the lesion to be treated or the lesion to be operated on; and a second radiotherapy unit, configured to, if the arterial vessel passes through the lesion to be treated or the lesion to be operated on, perform radiotherapy on the arterial vessel passing through the lesion to be treated or the lesion to be operated on under ultrasound image guidance with a first radiotherapy dose, and perform radiotherapy on the lesion outside the arterial vessel under ultrasound image guidance with a second radiotherapy dose less than the first radiotherapy dose.

[0030] According to one aspect of this disclosure, a medical image intelligent processing system is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the instructions stored in the memory to execute the above-described medical image intelligent processing method.

[0031] According to one aspect of this disclosure, a medical image intelligent processing system is provided, comprising: a computer-readable storage medium storing a computer program / instructions and a bit stream thereon, wherein the computer program / instructions, when executed by a processor, implement the above-described medical image intelligent processing method to generate the bit stream.

[0032] According to one aspect of this disclosure, a medical image intelligent processing system is provided, comprising: a computer program product configured with a computer program / instruction, which, when executed by a processor, implements the above-described medical image intelligent processing method.

[0033] In this embodiment of the disclosure, a technical solution for lesion location determination and intelligent medical image processing and system is proposed to solve at least one of the following technical problems: the need for manual determination of lesion location and whether it is adjacent to arteries is too subjective; the lack of intelligent determination of lesion location and whether it is adjacent to arteries causes a huge workload for doctors; and it makes it impossible to further effectively evaluate the prediction of radiotherapy efficacy.

[0034] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure.

[0035] Other features and aspects of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0036] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the specification, serve to illustrate the technical solutions of this disclosure.

[0037] Figure 1 A flowchart illustrating a method for determining the location of a lesion according to an embodiment of the present disclosure is shown; Figure 2 A flowchart illustrating a method for determining the location of a lesion according to an embodiment of the present disclosure is shown; Figure 3 A flowchart illustrating a medical image intelligent processing method according to an embodiment of the present disclosure is shown. Figure 4 A block diagram of a lesion location determination system according to an embodiment of the present disclosure is shown; Figure 5 A block diagram of a lesion location determination system according to an embodiment of the present disclosure is shown; Figure 6 A block diagram of a medical image intelligent processing system according to an embodiment of the present disclosure is shown. Detailed Implementation

[0038] Various exemplary embodiments, features, and aspects of this disclosure will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0039] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0040] In this document, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0041] Furthermore, to better illustrate this disclosure, numerous specific details are set forth in the following detailed description. Those skilled in the art will understand that this disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main points of this disclosure.

[0042] It is understood that the various method embodiments mentioned above in this disclosure can be combined with each other to form combined embodiments without violating the principle and logic. Due to space limitations, this disclosure will not elaborate further.

[0043] In addition, this disclosure also provides a lesion location determination system and a medical image intelligent processing system. The above can be used to implement any of the lesion location determination methods and medical image intelligent processing methods provided in this disclosure. The corresponding technical solutions and descriptions are described in the relevant sections on lesion location determination methods and medical image intelligent processing methods, and will not be repeated here.

[0044] Figure 1 A flowchart illustrating a method for determining the location of a lesion according to an embodiment of this disclosure is shown. Figure 1 As shown, the lesion location determination method includes: Step S101: using a preset medical image of the site, determining the lesion mask image and arterial vessel mask image corresponding to the lesion to be treated or the lesion to be operated on; Step S102: based on the lesion mask image and the arterial vessel mask image, determining whether the lesion location corresponding to the lesion to be treated or the lesion to be operated on is adjacent to an artery. This addresses at least one of the technical problems currently present, such as the need for manual determination of lesion location and whether it is adjacent to an artery, which is too subjective, lacks intelligent determination of lesion location and whether it is adjacent to an artery, resulting in a huge workload for doctors, and prevents further effective evaluation of radiotherapy efficacy prediction.

[0045] In this embodiment, determining the lesion mask image and artery mask image corresponding to the lesion to be treated or the lesion to be operated on using medical images of preset locations includes: training a first preset deep learning segmentation network using a preset number of preset location medical training images and their corresponding lesion mask training images to obtain a preset lesion segmentation model; training a second preset deep learning segmentation network using a preset number of preset location medical training images and their corresponding artery mask training images to obtain a preset artery segmentation model; segmenting the lesion to be treated or the lesion to be operated on in the preset location medical images using the preset lesion segmentation model to obtain the corresponding lesion mask image; and segmenting the arteries using the preset artery segmentation model to obtain the corresponding artery mask image.

[0046] In the embodiments disclosed herein and other possible embodiments, the mask value corresponding to each lesion region in the lesion mask image and the lesion mask training image is configured as 1, and the mask value corresponding to the non-lesion region is configured as 0; similarly, the mask value corresponding to the artery in the artery mask image and the artery mask training image is configured as 1, and the mask value corresponding to the non-artery region is configured as 0.

[0047] In the embodiments of this disclosure and other possible embodiments, before training the first preset deep learning segmentation network using a preset number of preset site medical training images and their corresponding lesion mask training images, the method includes: using segmentation annotation software CasiaLabeler or Labelme to delineate lesions on the preset number of preset site medical training images to obtain corresponding lesion mask training images.

[0048] In the embodiments of this disclosure and other possible embodiments, before training the second preset deep learning segmentation network using a preset number of preset site medical training images and their corresponding arterial blood vessel mask training images, the method includes: using segmentation annotation software CasiaLabeler or Labelme to delineate arteries and blood vessels in the preset number of preset site medical training images to obtain corresponding arterial blood vessel mask training images.

[0049] In this embodiment, determining whether the location of the lesion to be treated or operated on is adjacent to an artery based on the lesion mask image and the artery mask image includes: calculating multiple positional distances between the artery edge mask position coordinates corresponding to the artery mask image and the lesion edge mask position coordinates corresponding to the lesion mask image; if the minimum positional distance among the multiple positional distances is less than or equal to a preset positional distance, then the lesion location is determined to be adjacent to an artery; otherwise, the lesion location is determined to be far from an artery. The artery edge mask position coordinates and the lesion edge mask position coordinates corresponding to the lesion are respectively configured as the position coordinates of the artery edge in the artery edge mask image corresponding to the artery mask image and the position coordinates of the lesion edge mask in the lesion edge mask image corresponding to the lesion mask image.

[0050] Figure 2 A flowchart illustrating a method for determining the location of a lesion according to an embodiment of this disclosure is shown. Figure 2 As shown, the lesion location determination method includes: Step S103: using a preset medical image of the site, determining the lesion mask and arterial mask image corresponding to the lesion to be treated or the lesion to be operated on; Step S104: based on the lesion mask and arterial mask image, determining whether the lesion location corresponding to the lesion to be treated or the lesion to be operated on is adjacent to an artery. This addresses at least one of the technical problems currently present, such as the need for manual determination of lesion location and proximity to arteries, which is highly subjective, lacks intelligent determination of lesion location and proximity to arteries, resulting in a huge workload for doctors and hindering subsequent effective evaluation of radiotherapy efficacy prediction.

[0051] In this embodiment, determining the lesion mask and arterial mask images corresponding to the lesion to be treated or the lesion to be operated on using preset medical images of preset locations includes: training a preset deep learning segmentation network using a preset number of preset medical training images of preset locations and their corresponding lesion mask and arterial mask training images to obtain a preset lesion and arterial segmentation model; and segmenting the lesion to be treated or the lesion to be operated on and the arterial vessels using the preset lesion and arterial segmentation model to obtain the corresponding lesion mask and arterial mask images.

[0052] In the embodiments disclosed herein and other possible embodiments, the mask value corresponding to each lesion region in the lesion mask and artery mask image and the lesion mask and artery mask training image is configured as 1, the mask value corresponding to the artery is configured as 2, and the mask value corresponding to the non-lesion region and non-artery region is configured as 0.

[0053] In the embodiments of this disclosure and other possible embodiments, before training the preset deep learning segmentation network using a preset number of preset site medical training images and their corresponding lesion masks and artery mask training images, the method includes: using segmentation annotation software CasiaLabeler or Labelme to simultaneously delineate lesions and arteries on the preset number of preset site medical training images to obtain corresponding lesion mask and artery mask training images.

[0054] In the embodiments disclosed herein and other possible embodiments, the preset deep learning segmentation network, the first preset deep learning segmentation network and the second preset deep learning segmentation network are configured as one or more deep learning segmentation networks such as Fully Convolutional Network (FCN), U-Net, DeepLab, Pyramid Spatial Pooling Network (PSPNet).

[0055] In the embodiments disclosed herein and other possible embodiments, the preset site medical training images and the preset site medical images are of the same type. For example, the preset site medical training images and the preset site medical images are configured as one or more of the following medical images: chest X-ray / CT / PET / MRI images, knee joint X-ray / CT / PET / MRI images, spine X-ray / CT / PET / MRI images, breast X-ray / CT / PET / MRI images, abdominal X-ray / CT / PET / MRI images, vertebral X-ray / CT / PET / MRI images, breast ultrasound images, and abdominal ultrasound images; and / or, the lesions corresponding to the preset site medical images are configured as one or more of the following medical images: pneumonia, tuberculosis, encephalitis, lung cancer, liver cancer, breast cancer, lipoma, fibroma, hamartoma, cavitary tuberculosis, cavitary lung abscess, nodule, fracture, cerebral contusion and laceration, and soft tissue injury.

[0056] In this embodiment, determining whether the location of the lesion to be treated or operated on is adjacent to an artery based on the lesion mask and artery mask images includes: calculating multiple positional distances between the artery edge mask position coordinates corresponding to the lesion mask and artery mask images and the lesion edge mask position coordinates; if the minimum positional distance among the multiple positional distances is less than or equal to a preset positional distance, then the lesion location is determined to be adjacent to an artery; otherwise, the lesion location is determined to be far from an artery. The artery edge mask position coordinates and the lesion edge mask position coordinates corresponding to the lesion are respectively configured as the position coordinates of the artery edge in the artery edge mask and the position coordinates of the lesion edge mask in the lesion mask and artery mask images.

[0057] In the embodiments disclosed herein and other possible embodiments, those skilled in the art can configure the preset position distance according to actual needs. For example, the preset position distance can be any value between 0 and 10 pixels or other values.

[0058] In this embodiment, before determining the lesion mask image and arterial mask image corresponding to the lesion to be treated or the lesion to be operated on using the medical image of the preset location, the method includes: detecting whether the lesion boundary in the lesion mask image corresponding to the medical image of the preset location is blurred and whether there is a spike sign at the edge of the lesion; if the lesion boundary in the lesion mask image is blurred and there is a spike sign at the edge of the lesion, then the lesion is configured as a lesion to be treated or a lesion to be operated on.

[0059] In this embodiment, before determining the lesion mask and arterial mask image corresponding to the lesion to be treated or the lesion to be operated on using the medical image of the preset location, the method includes: detecting whether the lesion boundary in the lesion and arterial mask image corresponding to the medical image of the preset location is blurred and whether there is a spike sign at the edge of the lesion; if the lesion boundary in the lesion and arterial mask image is blurred and there is a spike sign at the edge of the lesion, then the lesion is configured as a lesion to be treated or a lesion to be operated on.

[0060] In this embodiment, determining whether the lesion boundary is blurred includes: mapping the lesion location coordinates in the lesion masking image to a preset site medical image; calculating multiple gradients corresponding to the lesion location coordinates in the preset site medical image; if the gradient magnitude value corresponding to the multiple gradients is less than a preset gradient magnitude value, then the lesion boundary where the lesion location is located, which is larger than the preset gradient magnitude value, is determined to be blurred.

[0061] In this embodiment, if the gradient magnitude value corresponding to the plurality of gradients is greater than or equal to a preset gradient magnitude value, then the boundary of the lesion location corresponding to the lesion location that is greater than or equal to the preset gradient magnitude value is clearly determined.

[0062] In this embodiment, before mapping the lesion location coordinates in the lesion masking image to a preset site medical image, the method includes: performing an erosion operation on the lesion mask in the lesion masking image corresponding to the preset site medical image to obtain a lesion mask erosion image; and subtracting the lesion mask erosion image from the lesion masking image to obtain a lesion masking image.

[0063] In the embodiments disclosed herein and other possible embodiments, determining the gradient magnitude corresponding to the plurality of gradients includes: squaring the plurality of x-direction gradients corresponding to the lesion location coordinates to obtain a plurality of x-direction squared gradients; squaring the plurality of y-direction gradients corresponding to the lesion location coordinates to obtain a plurality of y-direction squared gradients; summing the plurality of x-direction squared gradients with their corresponding y-direction squared gradients to obtain a gradient sum corresponding to the plurality of lesion location coordinates; and taking the square root of the gradient sum corresponding to the plurality of lesion location coordinates to obtain the gradient magnitude corresponding to the plurality of gradients.

[0064] In the embodiments of this disclosure and other possible embodiments, the step of calculating multiple gradients corresponding to the lesion location coordinates in the medical image of the preset location includes: determining multiple first position coordinates and multiple second position coordinates corresponding to both sides of the lesion location coordinates in the x-direction, and multiple third position coordinates and multiple fourth position coordinates corresponding to the y-direction, based on the lesion location coordinates; calculating the differences between the first pixel values ​​corresponding to the multiple first position coordinates and the second pixel values ​​corresponding to the multiple second position coordinates to obtain multiple x-direction gradients corresponding to the lesion location coordinates; calculating the differences between the third pixel values ​​corresponding to the multiple third position coordinates and the fourth pixel values ​​corresponding to the multiple fourth position coordinates to obtain multiple y-direction gradients corresponding to the lesion location coordinates; and configuring the multiple x-direction gradients and their corresponding multiple y-direction gradients as multiple gradients corresponding to the lesion location coordinates.

[0065] In the embodiments disclosed herein and other possible embodiments, determining multiple first position coordinates and multiple second position coordinates corresponding to both sides of the lesion position coordinates in the x-direction of the medical image of the preset site based on the lesion position coordinates includes: using the lesion position coordinates, determining multiple first position coordinates and multiple second position coordinates corresponding to both sides in the x-direction according to a preset distance; and / or, determining multiple third position coordinates and multiple fourth position coordinates corresponding to both sides of the lesion position coordinates in the y-direction of the medical image of the preset site based on the lesion position coordinates includes: using the lesion position coordinates, determining multiple third position coordinates and multiple fourth position coordinates corresponding to both sides in the x-direction according to a preset distance.

[0066] In the embodiments disclosed herein and other possible embodiments, those skilled in the art can configure the preset distance according to actual needs. For example, the lesion location coordinates can be configured as (x1, y1), (x2, y2), ..., (x... n ,y n ), respectively determine the coordinates of multiple first positions (x1+1, y1), (x2+1, y2), ..., (x2+1, y2) in the x-direction on both sides according to the 1 pixel value corresponding to the preset distance.n +1,y n ) and multiple second position coordinates (x1-1, y1), (x2-1, y2), ..., (x n -1,y n For example, the lesion location coordinates are configured as (x1, y1), (x2, y2), ..., (x... n ,y n ), respectively determine the coordinates of multiple first positions (x1, y1+1), (x2, y2+1), ..., (x2, y2+1) in the x-direction on both sides according to the 1 pixel value corresponding to the preset distance. n ,y n +1) and multiple second position coordinates (x1, y1-1), (x2, y2-1), ..., (x n ,y n -1).

[0067] In the embodiments disclosed herein and other possible embodiments, those skilled in the art can configure the preset gradient magnitude value according to actual needs. For example, the preset gradient magnitude value can be configured as any value between 2 and 80 or other values.

[0068] In this embodiment, determining whether a spiky sign exists at the edge of the lesion includes: determining whether the medical image of the preset location is a two-dimensional or three-dimensional medical image; if the medical image of the preset location is a two-dimensional medical image, then calculating the ratio of the long axis to the short axis of each lesion based on the long axis and short axis of each lesion in the lesion mask image corresponding to the medical image of the preset location; if the ratio of the long axis to the short axis is greater than or equal to a first preset ratio of the long axis to the short axis, then determining that the corresponding lesion has a spiky sign; otherwise, determining that the corresponding lesion does not have a spiky sign. The spiky sign is determined as follows: If the medical image of the preset location is a three-dimensional medical image, then based on the major and minor axes of each three-dimensional lesion in the lesion mask image corresponding to the medical image of the preset location, multiple major-minor axis ratios corresponding to each three-dimensional lesion are calculated; if any one of the major-minor axis ratios is greater than or equal to a first preset major-minor axis ratio, then the corresponding lesion is determined to have a spiky sign; and / or, if any one of the major-minor axis ratios is less than the first preset major-minor axis ratio, then the corresponding lesion does not have a spiky sign.

[0069] In the embodiments of this disclosure and other possible embodiments, determining the major axis corresponding to each three-dimensional lesion includes: performing three-dimensional reconstruction on each lesion mask in the first lesion mask image to obtain the three-dimensional mask lesion corresponding to each lesion mask; performing edge detection on the three-dimensional mask lesion of each lesion mask to obtain the three-dimensional lesion mask edge; selecting any three-dimensional edge pixel in the three-dimensional lesion mask edge; calculating multiple three-dimensional edge pixel distances between the arbitrary three-dimensional edge pixel and other three-dimensional edge pixel; selecting the maximum three-dimensional edge pixel distance from the multiple three-dimensional edge pixel distances; and configuring the maximum three-dimensional edge pixel distance as the major axis corresponding to each three-dimensional lesion.

[0070] In the embodiments of this disclosure and other possible embodiments, determining the short path corresponding to each lesion includes: performing three-dimensional reconstruction on each lesion mask in the first lesion mask image to obtain a three-dimensional mask lesion corresponding to each lesion mask; performing edge detection on the three-dimensional mask lesions of each lesion mask to obtain the three-dimensional lesion mask edges; selecting any three-dimensional edge pixel in the three-dimensional lesion mask edges; calculating multiple three-dimensional edge pixel distances between the arbitrary three-dimensional edge pixel and other three-dimensional edge pixel points; selecting the minimum three-dimensional edge pixel distance from the multiple three-dimensional edge pixel distances; and configuring the minimum three-dimensional edge pixel distance as the long path corresponding to each three-dimensional lesion.

[0071] Figure 3 A flowchart illustrating a medical image intelligent processing method according to an embodiment of this disclosure is shown. Figure 3 As shown, the intelligent medical image processing method includes: Step 105: Determine whether the location of the lesion to be treated or the lesion to be operated on is adjacent to an artery using the lesion location determination method described above; Step 106: If the location of the lesion to be treated or the lesion to be operated on is adjacent to an artery, determine whether the artery passes through the lesion to be treated or the lesion to be operated on based on the lesion mask area corresponding to the lesion to be treated or the lesion to be operated on and the artery mask area corresponding to the artery.

[0072] In this embodiment, determining whether the artery passes through the lesion to be treated or operated on, based on the lesion mask region corresponding to the lesion to be treated or operated on and the artery mask region corresponding to the artery, includes: extracting the lesion mask position coordinates corresponding to the lesion mask region of the lesion to be treated or operated on and the artery mask region of the artery to be treated, respectively; if the artery mask position coordinates and the lesion mask position coordinates overlap, it is determined that the artery passes through the lesion to be treated or operated on; otherwise, it is determined that the artery does not pass through the lesion to be treated or operated on.

[0073] In this embodiment, the method further includes: using one or more imaging features extracted from the medical image of the preset site, such as the average gray value corresponding to the lesion, the ratio of the long diameter to the short diameter of the lesion, whether the lesion is adjacent to an artery, and whether there is a spiky sign at the edge of the lesion, to predict the efficacy of radiotherapy. This addresses the technical problem in the prior art of effectively predicting the grading of radiotherapy efficacy for osteoblastic bone metastases.

[0074] In the embodiments disclosed herein and other possible embodiments, if the artery passes through the lesion to be treated with chemotherapy or the lesion to be operated on, radiotherapy is performed on the artery passing through the lesion under ultrasound image guidance at a first radiotherapy dose, and radiotherapy is performed on the lesion outside the artery under ultrasound image guidance at a second radiotherapy dose lower than the first radiotherapy dose. This addresses the issue that tumor cells corresponding to the lesion may be affected by blood flow in the blood vessels, and the concentration of chemotherapy drugs in the local tumor area may be easily diluted, leading to reduced efficacy and increased risk of residual cancer cells and recurrence.

[0075] In the embodiments of this disclosure and other possible embodiments, the step of using one or more imaging features extracted from the medical image of the preset location—such as the average gray value corresponding to the lesion, the ratio of the major axis to the minor axis corresponding to the lesion, whether the lesion is adjacent to an artery, and whether there is a spike sign at the edge of the lesion—to predict the radiotherapy efficacy includes: determining whether there is a similar lesion type on the medical image of the preset location; if so, determining the imaging features used to distinguish the lesion type from the similar lesion type; and using the preset radiotherapy efficacy prediction model and the imaging features to predict the radiotherapy efficacy of the medical image of the preset location.

[0076] In the embodiments of this disclosure and other possible embodiments, determining the imaging features used to distinguish the lesion type from the similar lesion type includes: extracting multiple first imaging parameters to be distinguished from lesions in multiple first preset locations to be distinguished corresponding to the lesion type, and multiple second imaging parameters to be distinguished from lesions in multiple second preset locations to be distinguished corresponding to the similar lesion type; determining corresponding quantitative imaging parameters and count imaging parameters based on the multiple first imaging parameters to be distinguished and the multiple second imaging parameters to be distinguished; and using a feature filtering algorithm to filter the risk factor imaging parameters constructed from the quantitative imaging parameters and the count imaging parameters to obtain imaging features used to distinguish the lesion type from the similar lesion type.

[0077] For example, if the lesion type in the CT scan of the vertebral body corresponding to the preset medical image is configured as osteoblastic bone metastasis, then the similar lesion type is configured as bone island. If the lesion type in the CT scan of the chest or X-ray of the chest corresponding to the preset medical image is configured as pneumonia (common pneumonia), then the similar lesion type is configured as pneumonia caused by coronavirus. If the lesion type in the CT scan of the chest or X-ray of the chest corresponding to the preset medical image is configured as ground-glass nodule, then the similar lesion type is configured as solid nodule or mixed ground-glass nodule. If the lesion type in the CT scan of the chest or X-ray of the chest corresponding to the preset medical image is ground-glass nodule corresponding to lung inflammation, then the similar lesion type is configured as ground-glass nodule corresponding to early lung cancer.

[0078] For example, the imaging features used to distinguish the type of lesion from the similar lesion types are configured as one or more imaging features corresponding to the imaging features used to differentiate osteogenic bone metastases from bone islands, such as the average effective atomic number value, the average CT value corresponding to the lesion, the ratio of the long axis to the short axis of the lesion with the largest cross-sectional area, whether the lesion boundary is clear, whether the lesion location is adjacent to the bone cortex, and whether there is a spinous process sign at the edge of the lesion.

[0079] Osteoblastic bone metastases commonly occur in tumors such as prostate cancer, breast cancer, and kidney cancer, accounting for 10% of all bone metastases. Bone metastases are classified into osteolytic metastases, osteoblastic metastases, and mixed metastases. Among these, diagnosing osteoblastic bone metastases is a challenge in current imaging diagnostics, as it is easily confused with diseases such as bone islands. Symptoms of bone metastases include persistent and severe bone pain, pathological fractures and limited mobility, severe hypercalcemia, and compression of adjacent spinal cords and nerves. Failure to make a clear diagnosis and treat bone metastases in a timely manner can pose a significant risk to the patient's life and reduce their quality of life. Currently, bone metastases are often diagnosed through a clear history of the primary tumor, commonly used imaging diagnostic techniques (DR, CT, MR), emission CT (ECT), or positron emission tomography (PET). The shortcomings of existing technologies are summarized as follows: (1) Osteolytic and mixed metastatic bone metastases both appear as bone destruction on DR (Digital Radiography) and CT (Computed Tomography). The bone destruction is shown as a low-density or mixed low-density lesion, and soft tissue masses may be present around the bone destruction; (2) Osteolytic and mixed metastatic bone metastases often appear as long T1 and long T2 signals on MR (Magnetic Resonance Imaging). Diffusion-restricted areas can be seen on diffusion sequences, and enhanced MR scans can show different degrees of enhancement of bone destruction and surrounding soft tissue masses. However, osteoblastic bone metastases and bone islands are very similar in imaging. These lesions are high-density on both DR and CT (CT has higher image resolution), while they are generally low-signal on various MR sequences. Therefore, it is often difficult to accurately diagnose and differentiate osteoblastic bone metastases and bone islands using MR.

[0080] Currently, the main treatment for bone metastases is symptomatic drug therapy. If a clear therapeutic effect is not achieved, radiotherapy, chemotherapy, and immunotherapy are required. Chemotherapy for bone metastases often fails to completely cure the tumor and can only be used to relieve symptoms and improve the patient's quality of life. For significant therapeutic effects on local bone metastases, local radiotherapy to the metastatic area is often necessary. When assessing the effectiveness of local radiotherapy, osteolytic and mixed metastatic bone metastases can be evaluated by changes in the size of the corresponding tumor lesion; however, this assessment method is not applicable to osteoblastic bone metastases. After radiotherapy, although the internal tumor tissue of osteoblastic bone metastases has undergone necrosis, changes in the size and density of the corresponding tumor lesion are often not significant due to ossification or bone hyperplasia. Therefore, current technology cannot accurately assess the radiotherapy effect on osteoblastic bone metastases.

[0081] Furthermore, while radionuclide imaging (CT) can diagnose all bone metastases, including osteoblastic bone metastases, it requires intravenous injection of radiolabeled phosphate compounds. PET scans also require the injection of radioactive drugs and are very expensive. Therefore, these techniques are not suitable for patients with bone metastases who are allergic to radiopharmaceuticals, pregnant or breastfeeding women, have severe cardiopulmonary or hepatic / renal insufficiency, or are financially disadvantaged. In addition, radioactive drugs pose a risk not only to the patient's own healthy tissues but also to the surrounding environment and the general population.

[0082] In the embodiments of this disclosure and other possible embodiments, determining the corresponding quantitative and count parameters based on the plurality of first and second radiographic parameters to be differentiated includes: grouping the plurality of first and second radiographic parameters to be differentiated according to quantitative and count principles to obtain a first group of quantitative radiographic parameters to be processed and a second group of count radiographic parameters to be processed; performing an independent samples t-test on the first group of quantitative radiographic parameters to be processed and the second group of count radiographic parameters to be processed, and selecting quantitative radiographic parameters with a significance level less than or equal to a first preset significance level from the first group of quantitative radiographic parameters to be processed or the second group of count radiographic parameters to be processed; performing a chi-square test on the first group of quantitative radiographic parameters to be processed and the second group of count radiographic parameters to be processed, and selecting count radiographic parameters with a significance level less than or equal to a second preset significance level from the first group of quantitative radiographic parameters to be processed or the second group of count radiographic parameters to be processed; wherein the feature selection algorithm is configured as a multi-factor logistic regression analysis algorithm or a LASSO (Least Absolute Shrinkage and Selection Operator) algorithm.

[0083] In the embodiments of this disclosure and other possible embodiments, the imaging features used to distinguish the lesion type from the similar lesion type include: one or more imaging features such as the average gray value corresponding to the lesion, the ratio of the long diameter to the short diameter corresponding to the lesion, whether the lesion is located near an artery, and whether there is a spike sign at the edge of the lesion.

[0084] In the embodiments of this disclosure and other possible embodiments, determining the average gray value corresponding to the lesion includes: performing a pixel-level multiplication operation on the medical image of the preset location and its corresponding lesion mask image to obtain a lesion medical image; calculating the average pixel value of the lesion medical image to obtain the average gray value corresponding to the lesion.

[0085] In the embodiments of this disclosure and other possible embodiments, when the preset site medical image is configured as a preset site spectral CT scan image, the imaging features used to distinguish the lesion type from the similar lesion type further include: the average effective atomic number value.

[0086] In the embodiments of this disclosure and other possible embodiments, extracting the average effective atomic number value corresponding to the lesion includes: using an energy spectrum CT atomic number analysis module configured with energy spectrum CT images to extract the average effective atomic number value corresponding to the lesion based on the energy spectrum CT scan image of the preset location and the corresponding lesion mask image.

[0087] In the embodiments of this disclosure and other possible embodiments, the spectral CT atomic number analysis module configured using spectral CT images extracts the average effective atomic number value corresponding to the lesion based on the spectral CT scan image of the preset location and its corresponding lesion mask image. This includes: using the spectral CT atomic number analysis module configured using spectral CT images to extract quantitative bar charts of atomic number corresponding to each component within each lesion generated from the spectral CT scan image of the preset location and its corresponding lesion mask image; determining the effective atomic number value of each component within each lesion based on multiple peak values ​​corresponding to the quantitative bar charts of atomic number corresponding to each component within each lesion; and averaging the effective atomic number values ​​of all components within all lesions to determine the average effective atomic number value of all components within the lesion corresponding to the spectral CT scan image of the preset location.

[0088] In the embodiments of this disclosure and other possible embodiments, the step of extracting a quantitative bar chart of the atomic number corresponding to each component within each lesion generated from the spectral CT scan image of the preset location based on each first spectral CT scan vertebral body image and its corresponding lesion mask image in the spectral CT scan image of the preset location includes: performing pixel-level multiplication operations on the spectral CT scan image of the preset location and its corresponding lesion mask image to obtain the spectral CT scan lesion image; and obtaining a quantitative bar chart of the atomic number corresponding to each component within each lesion generated from the spectral CT scan vertebral body image based on the spectral CT scan lesion image.

[0089] In the embodiments of this disclosure and other possible embodiments, the average CT value corresponding to the lesion is extracted based on the preset site spectral CT scan image and the lesion mask image, including: performing pixel-level multiplication operations on the preset site spectral CT scan image and its corresponding lesion mask image to obtain a first spectral CT scan lesion image corresponding to the preset site spectral CT scan image; calculating the CT value of each lesion corresponding to the first spectral CT scan lesion image; and averaging the CT values ​​of each lesion to determine the average CT value corresponding to the lesion.

[0090] In the embodiments of this disclosure and other possible embodiments, determining the preset radiotherapy efficacy prediction model includes: obtaining a preset number of imaging features corresponding to a first time moment for distinguishing the lesion type from the similar lesion type and radiotherapy efficacy prediction labels corresponding to a second time moment after the first time moment; training a preset prediction network or a preset predictor to obtain the preset radiotherapy efficacy prediction model.

[0091] In the embodiments of this disclosure and other possible embodiments, the radiotherapy efficacy prediction label can be configured as a radiotherapy efficacy prediction grading label. For example, when predicting the grading of radiotherapy efficacy for osteoblastic bone metastases, the radiotherapy efficacy prediction label can be configured as one or more of complete remission (CR), partial remission (PR), stable disease (SD), and progressive disease (PD). Complete remission (CR) is characterized by X-ray, CT, or MRI showing complete sclerosis of the osteoblastic lesion, normalized bone density, normalized bone signal intensity, and normalized bone imaging tracer uptake; clinically, it is characterized by complete disappearance of pain, no need for analgesics, and basic functional recovery. Partial remission (PR) is characterized by X-ray, CT, or MRI showing sclerosis or partial sclerosis filling at the edge of the osteoblastic lesion, or a measurable lesion shrinkage of ≥50%, or a subjective shrinkage of ≥50% for unmeasurable lesions; a subjective reduction of ≥50% in bone imaging tracer uptake; and clinically, it is characterized by significant pain reduction, reduced use of analgesics, and partial functional recovery. Stable disease (SD) is characterized by an increase of <25% or decrease of <50% in measurable lesion size on imaging, an increase of ≤25% and a decrease of ≤50% in unmeasurable lesion size, and no new bone metastases. Clinically, pain is not significantly changed or slightly improved, and function is basically stable. Progressive disease (PD) is characterized by an increase of ≥25% in measurable lesion size on X-ray, CT, or MRI, a subjective increase of ≥25% in unmeasurable lesion size, or an increase of ≥25% in bone scintigraphy tracer uptake. New bone metastases may also be present. Clinically, pain may worsen, function may deteriorate, or new bone-related events (such as pathological fractures or spinal cord compression) may occur.

[0092] In the embodiments disclosed herein and other possible embodiments, the preset prediction network or preset predictor may be configured as one or more of the following: decision tree classifier, random forest classifier, fully connected neural network (FCN), recurrent neural network (RNN), support vector machine (SVM), K-nearest neighbor algorithm (KNN).

[0093] The executor of the lesion location determination method and the intelligent medical image processing method can be an image processing device. For example, the lesion location determination method and the intelligent medical image processing method can be executed by a terminal device, a server, or other processing equipment. The terminal device can be a user equipment (UE), mobile device, user terminal, terminal, cellular phone, cordless phone, personal digital assistant (PDA), handheld device, computing device, vehicle-mounted device, wearable device, etc. In some possible implementations, the lesion location determination method and the intelligent medical image processing method can be implemented by a processor calling computer-readable instructions stored in memory.

[0094] Those skilled in the art will understand that in the above-described method for determining the location of lesions and the intelligent processing method for medical images in specific embodiments, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0095] Figure 4 A block diagram of a lesion location determination system according to an embodiment of the present disclosure is shown. Figure 4 As shown, the lesion location determination system includes: a first determination unit 10, used to determine the lesion mask image and arterial mask image corresponding to the lesion to be treated or the lesion to be operated on using a preset medical image of the site; and a second determination unit 11, used to determine whether the lesion location corresponding to the lesion to be treated or the lesion to be operated on is adjacent to an artery based on the lesion mask image and the arterial mask image.

[0096] Figure 5 A block diagram of a lesion location determination system according to an embodiment of the present disclosure is shown. Figure 5 As shown, the lesion location determination system includes: a third determination unit 12, used to determine the lesion mask and arterial mask image corresponding to the lesion to be treated or the lesion to be operated on using a preset medical image of the site; and a fourth determination unit 13, used to determine whether the lesion location corresponding to the lesion to be treated or the lesion to be operated on is close to an artery based on the lesion mask and the arterial mask image.

[0097] According to one aspect of this disclosure, a lesion location determination system is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the instructions stored in the memory to execute the lesion location determination method described above.

[0098] According to one aspect of this disclosure, a lesion location determination system is provided, comprising: a computer-readable storage medium storing a computer program / instructions and a bit stream thereon, wherein the computer program / instructions, when executed by a processor, implement the above-described lesion location determination method to generate the bit stream.

[0099] According to one aspect of this disclosure, a medical image intelligent processing system is provided, comprising: a computer program product configured with a computer program / instruction, which, when executed by a processor, implements the lesion location determination method described above.

[0100] Figure 6 A block diagram of a medical image intelligent processing system according to an embodiment of the present disclosure is shown. Figure 6 As shown, the intelligent medical image processing system includes: a first determining unit 10, used to determine, using a preset site medical image, a lesion mask image and an arterial vessel mask image corresponding to the lesion to be treated or the lesion to be operated on; a second determining unit 11, used to determine, based on the lesion mask image and the arterial vessel mask image, whether the location of the lesion to be treated or the lesion to be operated on is adjacent to an artery; a third determining unit 14, used to determine, if the location of the lesion to be treated or the lesion to be operated on is adjacent to an artery, whether the artery passes through the lesion to be treated or the lesion to be operated on; and a first radiotherapy unit 15, which, if the artery passes through the lesion to be treated or the lesion to be operated on, performs radiotherapy on the artery passing through the lesion to be treated or the lesion to be operated on under ultrasound image guidance with a first radiotherapy dose, and performs radiotherapy on the lesion outside the artery under ultrasound image guidance with a second radiotherapy dose less than the first radiotherapy dose.

[0101] According to one aspect of this disclosure, a medical image intelligent processing system is provided, comprising: a fourth determining unit, configured to determine, using a preset site medical image, a lesion mask and an arterial vessel mask image corresponding to a lesion to be treated or a lesion to be operated on; a fifth determining unit, configured to determine, based on the lesion mask and the arterial vessel mask image, whether the location of the lesion to be treated or the lesion to be operated on is adjacent to an arterial vessel; a sixth determining unit, configured to, if the location of the lesion to be treated or the lesion to be operated on is adjacent to an arterial vessel, determine, based on the lesion mask area corresponding to the lesion to be treated or the lesion to be operated on and the arterial vessel mask area corresponding to the arterial vessel, whether the arterial vessel passes through the lesion to be treated or the lesion to be operated on; and a second radiotherapy unit, configured to, if the arterial vessel passes through the lesion to be treated or the lesion to be operated on, perform radiotherapy on the arterial vessel passing through the lesion to be treated or the lesion to be operated on under ultrasound image guidance with a first radiotherapy dose, and perform radiotherapy on the lesion outside the arterial vessel under ultrasound image guidance with a second radiotherapy dose less than the first radiotherapy dose.

[0102] According to one aspect of this disclosure, a medical image intelligent processing system is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the instructions stored in the memory to execute the above-described medical image intelligent processing method.

[0103] According to one aspect of this disclosure, a medical image intelligent processing system is provided, comprising: a computer-readable storage medium storing a computer program / instructions and a bit stream thereon, wherein the computer program / instructions, when executed by a processor, implement the above-described medical image intelligent processing method to generate the bit stream.

[0104] According to one aspect of this disclosure, a medical image intelligent processing system is provided, comprising: a computer program product configured with a computer program / instruction, which, when executed by a processor, implements the above-described medical image intelligent processing method.

[0105] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for determining the location of a lesion, characterized in that, include: Using medical images of preset sites, determine the lesion mask image and arterial mask image corresponding to the lesion to be treated or the lesion to be operated on; Based on the lesion mask image and the artery mask image, determine whether the location of the lesion to be treated or operated on is adjacent to an artery; or, Using medical images of preset locations, determine the lesion mask and arterial mask images corresponding to the lesions to be treated or operated on; based on the lesion mask and arterial mask images, determine whether the location of the lesion to be treated or operated on is close to an artery.

2. The method for determining the location of a lesion according to claim 1, characterized in that, The step of determining the lesion mask image and artery mask image corresponding to the lesion to be treated or the lesion to be operated on using medical images of preset locations includes: training a first preset deep learning segmentation network using a preset number of preset location medical training images and their corresponding lesion mask training images to obtain a preset lesion segmentation model; training a second preset deep learning segmentation network using a preset number of preset location medical training images and their corresponding artery mask training images to obtain a preset artery segmentation model; segmenting the lesion to be treated or the lesion to be operated on in the preset location medical images using the preset lesion segmentation model to obtain the corresponding lesion mask image; and segmenting the arteries using the preset artery segmentation model to obtain the corresponding artery mask image; or... The step of determining the lesion mask and arterial mask images corresponding to the lesions to be treated or the lesions to be operated on using medical images of preset locations includes: training a preset deep learning segmentation network using a preset number of preset medical training images of preset locations and their corresponding lesion mask and arterial mask training images to obtain a preset lesion and arterial segmentation model; and segmenting the lesions to be treated or the lesions to be operated on and the arterial vessels using the preset lesion and arterial segmentation model to obtain the corresponding lesion mask and arterial mask images.

3. The method for determining the location of a lesion according to any one of claims 1 or 2, characterized in that, The step of determining whether the location of the lesion to be treated or operated on is adjacent to an artery based on the lesion mask image and the artery mask image includes: calculating multiple positional distances between the artery edge mask position coordinates corresponding to the artery mask image and the lesion edge mask position coordinates corresponding to the lesion mask image; if the minimum positional distance among the multiple positional distances is less than or equal to a preset positional distance, then the lesion location is determined to be adjacent to an artery; otherwise, the lesion location is determined to be far from an artery; or, The step of determining whether the location of the lesion to be treated or operated on is close to an artery based on the lesion mask and artery mask images includes: calculating multiple distances between the coordinates of the artery edge mask and the coordinates of the lesion edge mask corresponding to the lesion mask and artery mask images; if the minimum distance among the multiple distances is less than or equal to a preset distance, the lesion location is determined to be close to an artery; otherwise, the lesion location is determined to be far from an artery.

4. The method for determining the location of a lesion according to any one of claims 1-3, characterized in that, Before determining the lesion mask image and arterial mask image corresponding to the lesion to be treated or the lesion to be operated on using medical images of preset locations, the method includes: detecting whether the lesion boundary in the lesion mask image corresponding to the medical image of the preset location is blurred and whether there is a spike-like sign at the edge of the lesion; if the lesion boundary in the lesion mask image is blurred and there is a spike-like sign at the edge of the lesion, then the lesion is configured as a lesion to be treated or a lesion to be operated on; or, Before determining the lesion mask and arterial mask images corresponding to the lesion to be treated or the lesion to be operated on using the medical image of the preset location, the method includes: detecting whether the lesion boundary in the lesion and arterial mask image corresponding to the medical image of the preset location is blurred and whether there is a spike sign at the edge of the lesion; if the lesion boundary in the lesion and arterial mask image is blurred and there is a spike sign at the edge of the lesion, then the lesion is configured as a lesion to be treated or a lesion to be operated on.

5. The method for determining the location of a lesion according to claim 4, characterized in that, Determining whether the lesion boundary is blurred includes: mapping the lesion location coordinates in the lesion masking image to a preset site medical image; calculating multiple gradients corresponding to the lesion location coordinates in the preset site medical image; if the gradient magnitude values ​​corresponding to the multiple gradients are less than a preset gradient magnitude value, then the lesion boundary at the location corresponding to the lesion with a gradient magnitude value less than the preset gradient magnitude value is determined to be blurred; and / or, If the gradient magnitude corresponding to the plurality of gradients is greater than or equal to a preset gradient magnitude, then the boundary of the lesion location corresponding to the lesion location that is greater than or equal to the preset gradient magnitude is determined to be clear; and / or, Before mapping the lesion location coordinates in the lesion masking image to the preset site medical image, the procedure includes: performing an erosion operation on the lesion mask in the lesion masking image corresponding to the preset site medical image to obtain a lesion mask erosion image; and subtracting the lesion mask erosion image from the lesion masking image to obtain the lesion masking image.

6. The method for determining the location of a lesion according to any one of claims 4 or 5, characterized in that, Determining whether a spiky sign exists at the edge of the lesion includes: determining whether the medical image of the preset location is a two-dimensional or three-dimensional medical image; if the medical image of the preset location is a two-dimensional medical image, then calculating the ratio of the long axis to the short axis of each lesion based on the long axis and short axis of each lesion in the lesion mask image corresponding to the preset location medical image; if the ratio of the long axis to the short axis is greater than or equal to a first preset ratio of the long axis to the short axis, then determining that the corresponding lesion has a spiky sign; otherwise, determining that the corresponding lesion does not have a spiky sign; if If the medical image of the preset location is a three-dimensional medical image, then based on the major and minor axes of each three-dimensional lesion in the lesion mask image corresponding to the medical image of the preset location, multiple major-minor axis ratios corresponding to each three-dimensional lesion are calculated; if any one of the major-minor axis ratios is greater than or equal to a first preset major-minor axis ratio, then the corresponding lesion is determined to have a spiky sign; and / or, if any one of the major-minor axis ratios is less than the first preset major-minor axis ratio, then the corresponding lesion does not have a spiky sign.

7. A medical image intelligent processing method, characterized in that, include: The method for determining the location of a lesion as described in any one of claims 1-6 is used to determine whether the location of the lesion to be treated or the lesion to be operated on is adjacent to an artery. If the location of the lesion to be treated or operated on is close to an artery, then based on the lesion mask area corresponding to the lesion to be treated or operated on and the artery mask area corresponding to the artery, it is determined whether the artery passes through the lesion to be treated or operated on.

8. The intelligent medical image processing method according to claim 7, characterized in that, The step of determining whether an artery passes through a lesion requiring chemotherapy or surgery, based on the lesion mask region corresponding to the lesion to be treated or operated on, and the artery mask region corresponding to the artery, includes: extracting the lesion mask position coordinates corresponding to the lesion mask region of the lesion requiring chemotherapy or surgery, and the artery mask position coordinates corresponding to the artery mask region of the artery; if the artery mask position coordinates overlap with the lesion mask position coordinates, then it is determined that the artery passes through the lesion requiring chemotherapy or surgery; otherwise, it is determined that the artery does not pass through the lesion requiring chemotherapy or surgery; and / or, It also includes: using one or more imaging features extracted from the medical image of the preset site, such as the average gray value of the lesion, the ratio of the long diameter to the short diameter of the lesion, whether the lesion is near an artery, and whether there is a spike sign at the edge of the lesion, to predict the efficacy of radiotherapy.

9. A lesion location determination system, characterized in that, include: The first determining unit is used to determine the lesion mask image and arterial mask image corresponding to the lesion to be treated or the lesion to be operated on using a preset medical image of the site. The second determining unit is used to determine, based on the lesion mask image and the arterial vessel mask image, whether the location of the lesion to be treated or the lesion to be operated on is adjacent to an artery; or, The third determining unit is used to determine the lesion mask and arterial mask image corresponding to the lesion to be treated or the lesion to be operated on using a preset medical image of the site; the fourth determining unit is used to determine whether the lesion location corresponding to the lesion to be treated or the lesion to be operated on is close to an artery based on the lesion mask and the arterial mask image. or, The method comprises: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke instructions stored in the memory to execute the lesion location determination method according to any one of claims 1-6; or, it comprises: a computer-readable storage medium having a computer program / instructions and a bit stream stored thereon, wherein the computer program / instructions, when executed by a processor, implement the lesion location determination method according to any one of claims 1-6 to generate the bit stream; or, it comprises: a computer program product having a computer program / instructions configured thereon, wherein the computer program / instructions, when executed by a processor, implement the lesion location determination method according to any one of claims 1-6.

10. A medical image intelligent processing system, characterized in that, include: The first determining unit is used to determine the lesion mask image and arterial mask image corresponding to the lesion to be treated or the lesion to be operated on using a preset medical image of the site. The second determining unit is used to determine whether the location of the lesion to be treated or the lesion to be operated on is close to an artery based on the lesion mask image and the artery mask image. The third determining unit is used to determine whether an artery passes through the lesion to be treated or operated on if the lesion location is adjacent to an artery. This determination is based on the lesion mask area corresponding to the lesion to be treated or operated on and the artery mask area corresponding to the artery. The first radiotherapy unit, if it is determined that the artery passes through the lesion to be treated or operated on, performs radiotherapy on the artery passing through the lesion under ultrasound image guidance with a first radiotherapy dose, and performs radiotherapy on the lesion outside the artery under ultrasound image guidance with a second radiotherapy dose less than the first radiotherapy dose. Or... The fourth determining unit is used to determine the lesion mask and arterial mask image corresponding to the lesion to be treated or the lesion to be operated on using a preset medical image of the site. The fifth determining unit is used to determine whether the location of the lesion to be treated or operated on is adjacent to an artery based on the lesion mask and artery mask images; the sixth determining unit is used to determine whether the artery passes through the lesion to be treated or operated on if the location of the lesion to be treated or operated on is adjacent to an artery, based on the lesion mask area corresponding to the lesion to be treated or operated on and the artery mask area corresponding to the artery; the second radiotherapy unit, if the artery passes through the lesion to be treated or operated on, performs radiotherapy on the artery passing through the lesion to be treated or operated on under ultrasound image guidance with a first radiotherapy dose, and performs radiotherapy on the lesion outside the artery with a second radiotherapy dose less than the first radiotherapy dose under ultrasound image guidance; or, The method comprises: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the instructions stored in the memory to execute the intelligent medical image processing method of claim 7 or 8; or, a computer-readable storage medium having a computer program / instructions and a bit stream stored thereon, wherein the computer program / instructions, when executed by a processor, implement the intelligent medical image processing method of claim 7 or 8 to generate the bit stream; or, a computer program product having a computer program / instructions configured thereon, wherein the computer program / instructions, when executed by a processor, implement the intelligent medical image processing method of claim 7 or 8.

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