Method and apparatus for positioning a region of interest, ct device

Abstract

The application relates to the technical field of CT scanning, and discloses a method and device for positioning a region of interest and a CT device. The method comprises the following steps: acquiring a target positioning layer image; determining a first region of interest in the target positioning layer image based on a pre-constructed identification model; determining target standard setting information corresponding to the first region of interest in pre-stored statistical information; wherein the statistical information comprises standard setting information corresponding to different types of regions of interest; and adjusting the first region of interest according to the target standard setting information to determine a target region of interest in the target positioning layer image. The application can improve the accuracy of positioning a region of interest during CT image scanning.

Description

Technical Field

[0001] This application relates to the field of CT scanning technology, such as a method and apparatus for locating a region of interest, and CT equipment. Background Technology

[0002] Currently, in CT contrast-enhanced scanning procedures (e.g., CT angiography), after the injection of contrast agent, it is necessary to track the changes in CT values ​​within a designated vascular region (i.e., the region of interest) to determine whether subsequent scanning procedures should be triggered.

[0003] In related technologies, the region of interest is usually determined on the localization layer or the tracking layer, requiring doctors to manually delineate the location and size of the region of interest on the localization layer image.

[0004] In the process of implementing the embodiments of this disclosure, at least the following problems were found in the related art:

[0005] In related technologies, the accuracy of region of interest (ROI) segmentation depends on the physician's medical scanning experience. Limited experience can lead to significant errors in ROI tracking during CT image scanning. Therefore, improving the accuracy of ROI localization during CT image scanning has become a pressing technical problem.

[0006] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this application, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0007] To provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended as a general commentary, nor is it intended to identify key / important components or describe the scope of protection of these embodiments, but rather as a prelude to the detailed description that follows.

[0008] This disclosure provides a method and apparatus for locating regions of interest, as well as a CT device, which can improve the accuracy of locating regions of interest during CT image scanning.

[0009] In some embodiments, a method for locating a region of interest includes: acquiring a target localization layer image; determining a first region of interest within the target localization layer image based on a pre-built recognition model; determining target standard setting information corresponding to the first region of interest from pre-stored statistical information; wherein the statistical information includes standard setting information corresponding to different types of regions of interest; adjusting the first region of interest according to the target standard setting information to determine a target region of interest in the target localization layer image.

[0010] Optionally, acquiring the target positioning layer image includes: receiving an image selection instruction input by a user; and determining the target positioning layer image from multiple positioning layer images according to the image selection instruction.

[0011] Optionally, based on a pre-built recognition model, determining the first region of interest (ROI) within the target localization layer image includes: using the recognition model to identify multiple ROIs in the target localization layer image; receiving a region determination instruction input by the user and parsing the region determination information; wherein the region information includes region selection information or region delineation information; if the region information is region selection information, taking the ROI corresponding to the region selection information as the first ROI in the target localization layer image; if the region information is region delineation information, taking the region re-divided based on the region delineation information as the first ROI in the target localization layer image.

[0012] Optionally, determining the target standard setting information corresponding to the first region of interest from the pre-stored statistical information includes: identifying the region type to which the first region of interest belongs; and using the standard setting information corresponding to the region of interest with the same region type from the statistical information as the target standard setting information.

[0013] Optionally, after determining the target region of interest in the target localization layer image, the method further includes: acquiring the current frame tracking layer image corresponding to the target localization layer image; determining a second region of interest in the current frame tracking layer image based on a pre-built recognition model; comparing the target region of interest and the second region of interest to determine the deviation of the target region of interest; and adjusting the tracking area of ​​the tracking layer image according to the deviation of the target region of interest.

[0014] Optionally, the tracking region of the tracking layer image is adjusted according to the deviation of the target region of interest, including: if the target region of interest does not deviate from the second region of interest, the target region of interest is determined as the tracking region of the tracking layer image; if the target region of interest deviates from the second region of interest, the target region of interest is re-determined based on the second region of interest and statistical information, and the re-determined target region of interest is used as the tracking region of the tracking layer image.

[0015] Optionally, after redetermining the target region of interest based on the second region of interest and statistical information, and using the redetermined target region of interest as the tracking region of the tracking layer image, the method further includes: displaying on / off options for the automatic triggering function and the manual triggering function, so that the user can choose whether to turn off the automatic triggering function and turn on the manual triggering function instead.

[0016] Optionally, comparing the target region of interest (ROI) and the second ROI to determine the deviation of the target ROI includes: identifying a first number of all pixels in the target ROI; identifying a second number of pixels in the target ROI in the second ROI; if the ratio of the second number to the first number is greater than or equal to a set threshold, determining that the target ROI has not deviated from the second ROI; if the ratio of the second number to the first number is less than a set threshold, determining that the target ROI has deviated from the second ROI.

[0017] Optionally, the recognition model is constructed according to the following steps: acquiring sample images of regions of interest annotated by the user; wherein the annotated regions of interest are convex regions; the sample images include multiple human body parts, localization layer images and / or tracking layer images under multiple imaging fields of view; and training a deep neural network model to obtain the recognition model based on the sample images.

[0018] In some embodiments, the apparatus for locating a region of interest includes a processor and a memory storing program instructions, the processor being configured to perform the method for locating a region of interest as described above when the program instructions are executed.

[0019] In some embodiments, a CT device includes: a device body; and means for locating a region of interest, as described above, mounted on the device body.

[0020] The method, apparatus, and CT equipment for locating regions of interest provided in this disclosure can achieve the following technical effects:

[0021] In this embodiment, a first region of interest (ROI) in the target localization layer image is identified using a pre-built recognition model. The position and / or size of the first ROI are adjusted based on target standard setting information corresponding to the first ROI in pre-stored statistical information to determine the final target ROI in the target localization layer image. This eliminates the need for the user to manually divide the position and size of the ROI in the localization layer image, reducing ROI segmentation errors caused by the user's lack of scanning experience. Therefore, this embodiment can improve the accuracy of ROI localization during CT image scanning.

[0022] The above general description and the description below are exemplary and illustrative only and are not intended to limit this application. Attached Figure Description

[0023] One or more embodiments are illustrated by way of example with reference to the accompanying drawings. These illustrations and drawings do not constitute a limitation on the embodiments. Elements having the same reference numerals in the drawings are shown as similar elements. The drawings are not to be scaled. And wherein:

[0024] Figure 1 This is a schematic diagram of a CT device provided in an embodiment of this disclosure;

[0025] Figure 2 This is a schematic diagram of a method for locating a region of interest provided in an embodiment of this disclosure;

[0026] Figure 3 This is a schematic diagram of a positioning layer image provided in an embodiment of this disclosure;

[0027] Figure 4 This is a schematic diagram of another positioning layer image provided in an embodiment of this disclosure;

[0028] Figure 5 This is a schematic diagram of another method for locating a region of interest provided in an embodiment of this disclosure;

[0029] Figure 6 This is a schematic diagram of a tracking layer image provided in an embodiment of the present disclosure, showing that the target region of interest has not deviated from the second region of interest;

[0030] Figure 7 This is a schematic diagram of a tracking layer image where a target region of interest deviates from a second region of interest, provided in an embodiment of this disclosure;

[0031] Figure 8 This is a schematic diagram of an apparatus for locating a region of interest according to an embodiment of this disclosure. Detailed Implementation

[0032] To provide a more detailed understanding of the features and technical content of the embodiments of this disclosure, the implementation of the embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. The accompanying drawings are for illustrative purposes only and are not intended to limit the embodiments of this disclosure. In the following technical description, for ease of explanation, several details are used to provide a full understanding of the disclosed embodiments. However, one or more embodiments may still be implemented without these details. In other cases, well-known structures and devices may be simplified in their depiction to simplify the drawings.

[0033] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this disclosure described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion.

[0034] Unless otherwise stated, the term "multiple" means two or more.

[0035] In this embodiment of the disclosure, the character " / " indicates that the objects before and after it are in an "or" relationship. For example, A / B means: A or B.

[0036] The term "and / or" describes an association between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or A and B.

[0037] The term "correspondence" can refer to an association or binding relationship. The correspondence between A and B means that there is an association or binding relationship between A and B.

[0038] It should be noted that, unless otherwise specified, the embodiments and features described in the present disclosure can be combined with each other.

[0039] like Figure 1 As shown, the CT device 100 provided in this embodiment includes a device body 110 and a device 800 for locating the region of interest.

[0040] Specifically, a device 800 for locating the region of interest is provided on the device body 110.

[0041] Optionally, the device 800 for locating the region of interest includes a processor. During CT image scanning, the processor can determine a first region of interest in the target localization layer image based on a pre-built recognition model, and can adjust the position and / or size of the first region of interest according to target standard setting information corresponding to the first region of interest in pre-stored statistical information, so as to determine the final target region of interest in the target localization layer image.

[0042] In conjunction with the aforementioned CT equipment, embodiments of this disclosure provide a method for locating a region of interest, such as... Figure 2 As shown, the method includes:

[0043] S201, the processor acquires the target localization layer image.

[0044] Specifically, a localizer image is an image acquired using a lower radiation dose and a faster scanning speed before the formal CT scan. A target localizer image is a localizer image of the body part that the user wants to scan, or a localizer image selected by the user from multiple localizer images of different body parts.

[0045] S202, the processor determines the first region of interest within the target localization layer image based on a pre-built recognition model.

[0046] Specifically, the region of interest (ROI) is the area that the user wants to track during a CT scan.

[0047] Specifically, the recognition model is a pre-built deep neural network model used to identify regions of interest in localization layer images or tracking layer images.

[0048] Specifically, in the CT image scanning process, after the injection of contrast agent, it is necessary to track the changes in CT values ​​within the region of interest (ROI) to determine whether to trigger subsequent scanning procedures. However, if the ROI is manually defined by users with limited medical scanning experience, there is a risk of large segmentation errors. Therefore, it is necessary to identify the first ROI in the target localization layer image based on a pre-built recognition model.

[0049] S203, the processor determines the target standard setting information corresponding to the first region of interest from the pre-stored statistical information.

[0050] The statistical information includes standard settings for different types of regions of interest.

[0051] Specifically, in this embodiment of the disclosure, users with extensive medical scanning experience have pre-collected setting information for parameters such as the location and size of different types of regions of interest, and stored this information as standard setting information corresponding to different types of regions of interest as statistical information.

[0052] Specifically, after identifying the first region of interest (ROI) in the target localization layer image using the recognition model, the difference between the identified ROI and the ROI defined by a user with extensive medical scanning experience can be determined by identifying the target standard setting information corresponding to the ROI in the statistical information. This facilitates the adjustment of the ROI based on the target standard setting information.

[0053] Optionally, determining the target standard setting information corresponding to the first region of interest from the pre-stored statistical information includes: identifying the region type to which the first region of interest belongs; and using the standard setting information corresponding to the region of interest with the same region type from the statistical information as the target standard setting information.

[0054] Specifically, since each type of region of interest corresponds to a specific set of standard settings, the target standard settings for the first region of interest can be determined from statistical information based on the region type to which the first region of interest belongs.

[0055] S204, the processor adjusts the first region of interest according to the target standard setting information to determine the target region of interest in the target localization layer image.

[0056] Specifically, adjusting the position and / or size of the first region of interest according to the target criteria settings can make the final determined region of interest more in line with the user's tracking needs.

[0057] Specifically, the target standard setting information includes information such as the reference size of the region of interest.

[0058] For example, taking an adult patient being scanned, an imaging field of view of 300mm, and the target being tracked as a coronary artery, the corresponding target localization layer image is as follows: Figure 3 As shown, Figure 3 Region A in the image represents the coronary artery region, i.e., the first region of interest (ROI). The actual size of the ROI in the target localization layer image is 42 pixels (diameter), while the reference size specified in the target standard settings is 17 pixels (diameter). Based on this, the size of the ROI can be adjusted to 17 pixels according to the reference size, and the center position can be adopted from the original center position of the ROI, thus obtaining the final target region of interest (e.g., ...). Figure 3 (Region B in the text). Adjusting the first region of interest (ROI) involves centering it at its core and then expanding outwards to the reference size of the target standard settings. The ROI is typically elliptical or circular, and its center can be obtained by recognizing its outline.

[0059] In this embodiment, a first region of interest (ROI) in the target localization layer image is identified using a pre-built recognition model. The position and / or size of the first ROI are adjusted based on target standard setting information corresponding to the first ROI in pre-stored statistical information to determine the final target ROI in the target localization layer image. This eliminates the need for the user to manually divide the position and size of the ROI in the localization layer image, reducing ROI segmentation errors caused by the user's lack of scanning experience. Therefore, this embodiment can improve the accuracy of ROI localization during CT image scanning.

[0060] In some embodiments, acquiring a target positioning layer image includes: receiving an image selection instruction input by a user; and determining a target positioning layer image from multiple positioning layer images according to the image selection instruction.

[0061] Specifically, when multiple positioning layer images are obtained from the scan, the user can select the target positioning layer image from the multiple positioning layer images by inputting an image selection command.

[0062] In some embodiments, determining a first region of interest (ROI) within a target localization layer image based on a pre-built recognition model includes: identifying multiple ROIs in the target localization layer image using the recognition model; receiving a region determination instruction input by a user and parsing the instruction to determine region information; wherein the region information includes region selection information or region delineation information; if the region information is region selection information, selecting the ROI corresponding to the region selection information as the first ROI in the target localization layer image; if the region information is region delineation information, selecting the region re-divided based on the region delineation information as the first ROI in the target localization layer image.

[0063] Specifically, when the recognition model identifies multiple regions of interest in the target localization layer image, the user needs to input a region determination command to determine the target region of interest among the multiple regions of interest.

[0064] Specifically, region information can be determined by parsing the region determination command. This region information includes region selection information and region segmentation information. Specifically, if the region information is region selection information, it indicates that among the identified regions of interest (ROIs), there is a region that the user wants to track. Therefore, in this case, the ROI corresponding to the region selection information can be taken as the first ROI in the target localization layer image. If the region information is region segmentation information, it indicates that among the identified ROIs, there is no region that the user wants to track. Therefore, in this case, the region re-segmented based on the region segmentation information needs to be taken as the first ROI in the target localization layer image.

[0065] For example, taking the identification of blood vessels in the target localization layer image as the first region of interest, the identified image is as follows: Figure 4 As shown. According to Figure 4 It can be seen that the identified regions of interest include the coronary artery region ( Figure 4 (C region in the middle) and pulmonary artery region ( Figure 4 (Region D in the image). In this case, if the region to be selected is determined based on the region determination instruction input by the user, the coronary artery region can be used as the first region of interest in the target localization layer image.

[0066] It should be noted that when the recognition model identifies regions of interest (ROIs) in the target localization layer image, there may be cases where only one ROI is identified. In such cases, this ROI can be directly designated as the first ROI in the target localization layer image. Alternatively, the user can be prompted to manually segment the ROI.

[0067] In this embodiment, the recognition model can simultaneously identify multiple regions of interest (ROIs) in the target localization layer image. In this case, the user can determine the first ROI among the multiple ROIs by inputting a region determination command. This provides the user with a variety of selectable tracking regions, allowing them to choose the tracking region according to actual scanning needs.

[0068] Furthermore, the region determination command based on the received user input also provides a filtering basis for subsequent adjustment of the region of interest based on statistical information. In other words, the information on which region of interest to extract from the statistical information can be obtained through the determination command.

[0069] This disclosure provides another method for locating a region of interest, such as... Figure 5 As shown, the method includes:

[0070] S501, the processor acquires the target localization layer image.

[0071] S502, the processor determines the first region of interest within the target localization layer image based on a pre-built recognition model.

[0072] S503, the processor determines the target standard setting information corresponding to the first region of interest from the pre-stored statistical information.

[0073] The statistical information includes standard settings for different types of regions of interest.

[0074] S504, the processor adjusts the first region of interest according to the target standard setting information to determine the target region of interest in the target localization layer image.

[0075] S505, the processor acquires the current frame tracking layer image corresponding to the target localization layer image.

[0076] Specifically, the tracking layer image is a cross-sectional image at the same location as the positioning layer image but taken at a different time.

[0077] S506, the processor determines the second region of interest within the current frame tracking layer image based on a pre-built recognition model.

[0078] Specifically, in the CT image scanning process, after the injection of contrast agent, it is necessary to track the changes in CT values ​​within the region of interest to determine whether to trigger subsequent scanning procedures. Therefore, after obtaining the current frame tracking layer image corresponding to the target localization layer image, it is necessary to identify the second region of interest in the current frame tracking layer image based on the recognition model in order to identify the changes in CT values ​​within the region of interest.

[0079] S507, the processor compares the target region of interest and the second region of interest to determine the deviation of the target region of interest.

[0080] Specifically, if the target region of interest (ROI) in the target localization layer image deviates from the second ROI in the current frame tracking layer image, each subsequent frame of the tracking layer image acquired based on the target ROI will be affected by this deviation, causing the continuous localization error to increase frame by frame. This accumulation of error may eventually lead to target tracking failure. Therefore, after identifying the second ROI in the current frame tracking layer image, the target ROI and the second ROI are compared to determine the deviation of the target ROI.

[0081] S508, the processor adjusts the tracking area of ​​the tracking layer image based on the deviation of the target's region of interest.

[0082] Specifically, if the target's region of interest (ROI) deviates from its target's region of interest, it indicates that continuing to acquire the next tracking layer image based on the ROI may lead to target tracking failure. Therefore, it is necessary to adjust the tracking region of subsequent tracking layer images according to the deviation of the ROI.

[0083] Optionally, the tracking region of the tracking layer image is adjusted according to the deviation of the target region of interest, including: if the target region of interest does not deviate from the second region of interest, the target region of interest is determined as the tracking region of the tracking layer image; if the target region of interest deviates from the second region of interest, the target region of interest is re-determined based on the second region of interest and statistical information, and the re-determined target region of interest is used as the tracking region of the tracking layer image.

[0084] Specifically, if the target region of interest (ROI) does not deviate from the second ROI, it indicates that target tracking based on the ROI results in no positioning error in the current frame tracking layer image. Therefore, in this case, the ROI can continue to be defined as the tracking region of the tracking layer image.

[0085] Specifically, if the target region of interest deviates from the second region of interest, it indicates that continuing to acquire the next frame of the tracking layer image based on the target region of interest may lead to target tracking failure. Therefore, in this case, it is necessary to redetermine the target region of interest based on the second region of interest and statistical information, and use the redetermined target region of interest as the tracking region for subsequent tracking layers.

[0086] In this embodiment of the disclosure, after each acquisition of the current frame tracking layer image corresponding to the target positioning layer image, it is confirmed whether there is a deviation between the target region of interest and the second region of interest in the current frame tracking layer image, and the tracking area of ​​subsequent tracking layer images is adjusted according to the deviation of the region of interest. In this way, the tracking area can be adjusted in a timely manner to ensure that the tracked target is consistent with the preset target.

[0087] In some embodiments, after redetermining the target region of interest based on the second region of interest and statistical information, and using the redetermined target region of interest as the tracking region of the tracking layer image, the method further includes: displaying on / off options for automatic triggering and manual triggering functions, so that the user can choose whether to turn off the automatic triggering function and turn on the manual triggering function instead.

[0088] Specifically, automatic triggering means that during the tracking scan of the tracking layer image, if the CT value of the second region of interest in the tracking layer image reaches the CT threshold, the scanning process of the next frame of the tracking layer image is automatically triggered. Manual triggering means that the tracking layer image is continuously scanned until a specified number of revolutions are reached, and during this period, the user can manually trigger the scanning of the next frame of the tracking layer image at any time.

[0089] In some embodiments, comparing a target region of interest (ROI) and a second ROI to determine the deviation of the target ROI includes: identifying a first number of all pixels in the target ROI; identifying a second number of pixels in the target ROI in the second ROI; determining that the target ROI has not deviated from the second ROI if the ratio of the second number to the first number is greater than or equal to a set threshold; and determining that the target ROI has deviated from the second ROI if the ratio of the second number to the first number is less than a set threshold.

[0090] Specifically, if the ratio of the second number of pixels in the target region of interest to the first number of all pixels in the target region of interest is greater than or equal to a set threshold, it indicates that the second region of interest in the current frame tracking layer image basically includes all the content of the target region of interest (the specific situation is as follows). Figure 6 As shown, Figure 6 (Region E is the second region of interest, and region F is the target region of interest). Therefore, in this case, it can be determined that the target region of interest has not deviated from the second region of interest.

[0091] Specifically, if the ratio of the second number of pixels in the target region of interest to the first number of all pixels in the target region of interest is less than a set threshold, it indicates that the second region of interest in the current frame tracking layer image is missing some important content from the target region of interest (the specific situation is as follows). Figure 7 As shown, Figure 7 (Region E is the second region of interest, and region F is the target region of interest). Therefore, in this case, it can be determined that the target region of interest deviates from the second region of interest.

[0092] Optionally, the threshold value is determined based on the scanned body part, with different body parts corresponding to different threshold values. Taking the scanning of blood vessels in the lungs as an example, the threshold value is set to be greater than or equal to 0.95.

[0093] In addition to the methods mentioned above, other ways can be used to confirm the deviation of the target region of interest. For example, by determining the relationship between the bounding rectangle corresponding to the target region of interest and the second region of interest.

[0094] In some embodiments, the recognition model is constructed according to the following steps: acquiring sample images of regions of interest annotated by the user; wherein the annotated regions of interest are convex regions; the sample images include multiple human body parts, localization layer images and / or tracking layer images under multiple imaging fields of view; and training a deep neural network model to obtain the recognition model based on the sample images.

[0095] Specifically, the sample images include multiple human body parts, positioning layer images and / or tracking layer images under multiple imaging fields of view. The regions of interest are all annotated by users with rich medical scanning experience (such as professional physicians or professional CT equipment operators). The annotation content is that the blood vessel regions corresponding to different human body parts are taken as regions of interest, and the annotated regions of interest are convex regions.

[0096] Specifically, before training a deep neural network model based on sample images, spline interpolation can be performed on the sample images to improve image quality and increase the number of sample images.

[0097] Specifically, the network structure of the deep neural network model can be the UNet network structure, and the loss function used can be Dice loss or cross-entropy loss.

[0098] In addition, a segmentation network structure can be used as the network structure of a deep neural network model, or other traditional image processing algorithms (such as threshold segmentation, region growing, and edge detection) can be used to construct a deep neural network model.

[0099] Combination Figure 8As shown, this disclosure provides an apparatus 800 for locating a region of interest, comprising a processor 801 and a memory 802. Optionally, the apparatus may further include a communication interface 803 and a bus 804. The processor 801, communication interface 803, and memory 802 can communicate with each other via the bus 804. The communication interface 803 can be used for information transmission. The processor 801 can invoke logical instructions stored in the memory 802 to execute the method for locating a region of interest described in the above embodiments.

[0100] Furthermore, the logic instructions in the aforementioned memory 802 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium.

[0101] The memory 802, as a computer-readable storage medium, can be used to store software programs and computer-executable programs, such as program instructions / modules corresponding to the methods in the embodiments of this disclosure. The processor 801 executes functional applications and data processing by running the program instructions / modules stored in the memory 802, that is, it implements the method for locating the region of interest in the above embodiments.

[0102] The memory 802 may include a program storage area and a data storage area. The program storage area may store the operating system and application programs required for at least one function; the data storage area may store data created based on the use of the terminal device. Furthermore, the memory 802 may include high-speed random access memory and may also include non-volatile memory.

[0103] This disclosure provides a computer-readable storage medium storing computer-executable instructions configured to perform the above-described method for locating a region of interest.

[0104] The aforementioned computer-readable storage medium may be a transient computer-readable storage medium or a non-transitory computer-readable storage medium.

[0105] The technical solutions of this disclosure can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes one or more instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in this disclosure. The aforementioned storage medium can be a non-transitory storage medium, including: a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, and other media capable of storing program code; it can also be a transient storage medium.

[0106] The foregoing description and accompanying drawings fully illustrate embodiments of this disclosure to enable those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, procedural, and other changes. The embodiments represent only possible variations. Individual components and functions are optional unless explicitly required, and the order of operation may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. Moreover, the terminology used in this application is for describing embodiments only and is not intended to limit the claims. As used in the description of embodiments and claims, the singular forms “a,” “an,” and “the” are intended to equally include the plural forms unless the context clearly indicates otherwise. Similarly, the term “and / or” as used in this application means including one or more of the associated listed items and all possible combinations thereof. Additionally, when used in this application, the term "comprise" and its variations "comprises" and / or "comprising" refer to the presence of stated features, integrals, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof. Without further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the process, method, or apparatus that includes said element. In this document, each embodiment may focus on the differences from other embodiments, and similar or identical parts between embodiments can be referred to mutually. For methods, products, etc., disclosed in the embodiments, if they correspond to the method section disclosed in the embodiments, the relevant parts can be referred to the description of the method section.

[0107] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of this disclosure. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0108] The methods and products (including but not limited to devices and equipment) disclosed in the embodiments herein can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units may be merely a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the shown or discussed units may be through some interfaces, and the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected to implement this embodiment according to actual needs. Furthermore, the functional units in the embodiments of this disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0109] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

Claims

1. A method for locating a region of interest, characterized in that, include: Acquire the target localization layer image; Based on a pre-built recognition model, the first region of interest within the target localization layer image is determined; Determine the target standard setting information corresponding to the first region of interest from the pre-stored statistical information; wherein, the statistical information includes standard setting information corresponding to different types of regions of interest, and the target standard setting information includes the reference size of the region of interest; The first region of interest is adjusted according to the target standard setting information to determine the target region of interest in the target localization layer image; Obtain the current frame tracking layer image corresponding to the target localization layer image; Based on a pre-built recognition model, the second region of interest within the current frame tracking layer image is determined; By comparing the target region of interest with the second region of interest, the deviation of the target region of interest can be determined. Adjust the tracking area of ​​the tracking layer image based on the deviation of the target's region of interest; The process of adjusting the tracking area of ​​the tracking layer image based on the deviation of the target region of interest includes: when the target region of interest deviates from the second region of interest, the target region of interest is re-determined based on the second region of interest and statistical information, and the re-determined target region of interest is used as the tracking area of ​​the tracking layer image. The target region of interest is used to determine whether to trigger the subsequent scanning process based on the change of CT value within the target region of interest.

2. The method according to claim 1, characterized in that, Obtain the target localization layer image, including: Receive image selection instructions from the user; Based on the image selection instructions, the target positioning layer image is determined from multiple positioning layer images.

3. The method according to claim 1, characterized in that, Based on a pre-built recognition model, the first region of interest within the target localization layer image is determined, including: A recognition model is used to identify multiple regions of interest in the target localization layer image; Receives a region determination command input by the user and parses the region determination command to determine region information; wherein, the region information includes region selection information or region delineation information; When the region information is region selection information, the region of interest corresponding to the region selection information is taken as the first region of interest in the target localization layer image; When the region information is region segmentation information, the region re-segmented based on the region segmentation information is taken as the first region of interest in the target localization layer image.

4. The method according to claim 1, characterized in that, Determine the target standard settings information corresponding to the first region of interest from the pre-stored statistical information, including: Identify the region type to which the first region of interest belongs; Use the standard settings information corresponding to the region of interest with the same region type in the statistical information as the target standard settings information.

5. The method according to claim 1, characterized in that, Adjusting the tracking area of ​​the tracking layer image based on the deviation of the target's region of interest also includes: If the target region of interest does not deviate from the second region of interest, the target region of interest is determined as the tracking region of the tracking layer image.

6. The method according to claim 1, characterized in that, After redetermining the target region of interest based on the second region of interest and statistical information, and using the redetermined target region of interest as the tracking region of the tracking layer image, the method further includes: Displays the on / off options for the automatic triggering function and the manual triggering function, allowing users to choose whether to turn off the automatic triggering function and turn on the manual triggering function instead.

7. The method according to claim 1, characterized in that, By comparing the target region of interest (ROI) and the second ROI, the deviation of the target ROI is determined, including: The first count of all pixels in the region of interest of the target; Identify the second number of pixels in the target region of interest within the second region of interest; If the ratio of the second quantity to the first quantity is greater than or equal to a set threshold, it is determined that the target region of interest has not deviated from the second region of interest. If the ratio of the second quantity to the first quantity is less than a set threshold, it is determined that the target region of interest deviates from the second region of interest.

8. The method according to any one of claims 1 to 4, characterized in that, Build the recognition model using the following steps: Acquire sample images of regions of interest annotated by the user; wherein the annotated regions of interest are convex regions; the sample images include localization layer images and / or tracking layer images from multiple human body parts and multiple imaging fields of view; A deep neural network model is trained based on sample images to obtain a recognition model.

9. An apparatus for locating a region of interest, comprising a processor and a memory storing program instructions, characterized in that, The processor is configured to, when executing the program instructions, perform the method for locating a region of interest as described in any one of claims 1 to 8.

10. A CT scanner, characterized in that, include: Equipment body; The apparatus for locating a region of interest as described in claim 9 is mounted on the device body.

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