Image segmentation methods, apparatus, systems and storage media

Abstract

This application provides a method, apparatus, system, and storage medium for image segmentation. The method includes the following operations: receiving image data; detecting one or more objects associated with the received image data; and determining one or more regions of interest (ROIs) in the received image data corresponding to one or more segments based on the detected objects.

Description

Technical Field

[0001] This disclosure generally relates to the field of data processing, and more specifically to image segmentation. Background Technology

[0002] Instance segmentation of biological images can be divided into two types: box-free instance segmentation and box-based instance segmentation. Box-free instance segmentation methods segment object instances by analyzing instance morphological attributes (e.g., object outline, texture, and shape) without the aid of object bounding boxes. Box-free instance segmentation methods often encounter the problem of separating contacting objects. Summary of the Invention

[0003] The embodiments relate to a method, system, and computer-readable medium for image segmentation. According to one aspect, a method for image segmentation is provided. The method may include: receiving image data; detecting one or more objects associated with the received image data; and determining one or more regions of interest in the received image data corresponding to one or more segments based on the detected objects.

[0004] According to another aspect, a computer system for image segmentation is provided. The computer system may include one or more processors, one or more computer-readable storage devices, one or more computer-readable tangible storage devices, and program instructions stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, thereby enabling the computer system to perform a method. The method may include: receiving image data; detecting one or more objects associated with the received image data; and determining one or more regions of interest in the received image data corresponding to one or more segments based on the detected objects.

[0005] According to another aspect, a computer-readable medium for image segmentation is provided. The computer-readable medium may include one or more computer-readable storage devices and program instructions stored on at least one of the one or more tangible storage devices for processor execution. The program instructions can be executed by the processor to perform a method. The method may accordingly include: receiving image data; detecting one or more objects associated with the received image data; and determining one or more regions of interest in the received image data corresponding to one or more segments based on the detected objects. Attached Figure Description

[0006] These and other objects, features, and advantages will become apparent from the following detailed description of illustrative embodiments, taken in conjunction with the accompanying drawings. The various features in the drawings are not drawn to scale because they are shown for clarity and to facilitate understanding by those skilled in the art in conjunction with the detailed description. In the drawings:

[0007] Figure 1 A networked computer environment according to at least one embodiment is shown;

[0008] Figure 2 It is a block diagram of a system for image segmentation according to at least one embodiment;

[0009] Figure 3 This is an operation flowchart illustrating the steps performed by a program for segmenting biological image data according to at least one embodiment;

[0010] Figure 4 According to at least one embodiment Figure 1 A block diagram depicting the internal and external components of a computer and server;

[0011] Figure 5 It includes, according to at least one embodiment Figure 1 A block diagram depicting an illustrative cloud computing environment for a computer system; and

[0012] Figure 6 According to at least one embodiment Figure 5 A block diagram illustrating the functional layers of a cloud computing environment. Detailed Implementation

[0013] This application discloses detailed embodiments of the claimed structures and methods; however, it is understood that the disclosed embodiments are merely illustrative of the claimed structures and methods, which can be implemented in various forms. These structures and methods can be implemented in many different forms and should not be construed as limited to the exemplary embodiments set forth herein. Rather, these exemplary embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope to those skilled in the art. Details of well-known features and techniques may be omitted in the description to avoid unnecessarily obscuring the presented embodiments.

[0014] It should be noted that the data collection and processing in this application should strictly comply with the requirements of relevant laws and regulations, obtain the informed consent or separate consent of the personal information subject, and carry out subsequent data use and processing within the scope of laws and regulations and the authorization of the personal information subject.

[0015] As mentioned earlier, instance segmentation of biological images can be divided into two types: frameless instance segmentation and bounding box-based instance segmentation. Frameless instance segmentation methods segment object instances by analyzing instance morphological attributes (e.g., object outline, texture, and shape) without the aid of object bounding boxes. Frameless instance segmentation methods often encounter the problem of separating contact objects.

[0016] In biological imaging, instance segmentation is a fundamental step in analyzing object behavior and attributes, such as cell interactions and nuclear processing responses. However, instance segmentation of biological images presents challenges due to object clustering, adhesion, and occlusion. Furthermore, biological tasks often require capturing fine details of instances, such as leafstalking and cell processes. Existing methods, such as DCAN, tend to oversegment when object boundaries are unclear. Cosine embedding separates contacting objects through pixel embedding clustering, but often results in fragmented segmentation. Other methods, such as StarDist, attempt to leverage object shape information to address the clustering problem, but their application is limited to convex objects. Therefore, it may be advantageous to utilize models that reuse features of the central object in segmentation branches to separate attached objects. With instance normalization, such models can suppress the distribution of neighboring objects and focus on the target, thus making instance segmentation of biological images more efficient by consuming fewer computational resources, reducing processing power, and saving time.

[0017] The methods, computer systems, and computer-readable media disclosed in this paper can be used for keypoint-based instance segmentation. Objects can be located using their center points, from which the width and height of bounding boxes can be learned. Features used to detect center points are then reused through instance normalization to help the model focus on the target object. In this way, the system disclosed in this paper is able to separate the target from its adjacent attached objects while preserving their fine details. The disclosed model reuses center object features to separate attached objects in the segmentation branch. With instance normalization, the proposed model is able to suppress the distribution of adjacent objects and focus on the target, thereby making instance segmentation of biological images more efficient by consuming fewer computational resources, reducing processing power, and saving time.

[0018] In this document, aspects are described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer-readable media according to various embodiments. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0019] The exemplary embodiments described below provide systems, methods, and computer programs for segmenting biological image data.

[0020] Now for reference Figure 1 A block diagram of a networked computer environment is shown, illustrating an image segmentation system 100 (hereinafter referred to as the "System") for segmenting biological images. It should be understood that... Figure 1 The illustration provides only one implementation and does not imply any limitation on environments in which different embodiments can be implemented. Many modifications can be made to the depicted environment based on design and implementation requirements.

[0021] System 100 may include computer 102 and server computer 114. Computer 102 may communicate with server computer 114 via communication network 110 (hereinafter referred to as the "network"). Computer 102 may include processor 104 and software program 108 stored on data storage device 106, enabling the computer to interface with a user and communicate with server computer 114. Reference will be made below. Figure 4 As discussed, computer 102 may include internal component 800A and external component 900A, and server computer 114 may include internal component 800B and external component 900B. For example, computer 102 may be a mobile device, telephone, personal digital assistant, netbook, laptop computer, tablet computer, desktop computer, or any type of computing device capable of running programs, accessing networks, and accessing databases.

[0022] Server computer 114 can also operate within cloud computing service models, such as Software as a Service (SaaS), Platform as a Service (PaaS), or Infrastructure as a Service (IaaS), as described below. Figure 5 and Figure 6 The server computer 114 may also be located in a cloud computing deployment model, such as a private cloud, community cloud, public cloud, or hybrid cloud.

[0023] A server computer 114, which can be used to segment biological and / or pathological image data, is capable of running an image segmentation program 116 (hereinafter referred to as the "program") that can interact with a database 112. The following is about... Figure 3 The image segmentation procedure method will be explained in more detail. In one embodiment, computer 102 may operate as an input device including a user interface, while program 116 may run primarily on server computer 114. In an alternative embodiment, program 116 may run primarily on one or more computers 102, while server computer 114 may be used to process and store the data used by program 116. It should be noted that program 116 may be a standalone program or may be integrated into a larger image segmentation procedure.

[0024] However, it should be noted that in some instances, processing of program 116 can be shared between computer 102 and server computer 114 at any ratio. In another embodiment, for example, program 116 can operate on more than one computer, server computer, or some combination of computers and server computers, such as multiple computers 102 communicating with a single server computer 114 via network 110. In another embodiment, for example, program 116 can operate on multiple server computers 114, which communicate with multiple client computers via network 110. Alternatively, the program can operate on a network server, which communicates with servers and multiple client computers via a network.

[0025] Network 110 may include wired connections, wireless connections, fiber optic connections, or some combination thereof. Typically, network 110 may be any combination of connections and protocols that support communication between computer 102 and server computer 114. Network 110 may include various types of networks, such as local area networks (LANs), wide area networks (WANs) such as the Internet, telecommunications networks such as the Public Switched Telephone Network (PSTN), wireless networks, public switched networks, satellite networks, cellular networks (e.g., fifth-generation (5G), Long Term Evolution (LTE), third-generation (3G), Code Division Multiple Access (CDMA), etc.), public land mobile networks (PLMNs), metropolitan area networks (MANs), private networks, self-organizing networks, intranets, fiber-optic-based networks, etc., and / or combinations of these or other types of networks.

[0026] Figure 1 The number and layout of devices and networks shown are provided as examples. In practice, there may be more. Figure 1 The devices and / or networks shown may be more devices and / or networks, fewer devices and / or networks, different devices and / or networks, or related to Figure 1 The devices and / or networks shown are arranged differently. Furthermore, Figure 1 The two or more devices shown can be implemented within a single device, or Figure 1 The single device shown can be implemented as multiple distributed devices. Alternatively, a group of devices in system 100 (e.g., one or more devices) can perform one or more functions described as being performed by another group of devices in system 100.

[0027] Now for reference Figure 2A block diagram of a biological image segmentation encoder system 200 is depicted. The biological image segmentation encoder system 200 may be, for example, a ResNet50 network with one or more layers. The biological image segmentation encoder system 200 may, among other things, include an object detection module 202 and an object-guided segmentation module 204. The object detection module 202 and the object-guided segmentation module 204 may each include one or more skip combination modules. The object detection module 202 can predict the center heatmap, the width and height of the bounding box, and one or more center offsets of the image data 206. The predicted bounding box flows to the segmentation branch to crop the region of interest (ROI) patch. Object features are used as guidance for the model to separate attached objects. Given a ROI patch x of size H×W, instance normalization can be written as:

[0028]

[0029] in, The first region of interest is represented by the block number 1. row and number Column characteristics, This represents the mean of the features of the region of interest across the channels. This represents the standard deviation of the features of the region of interest across the channels. and These represent the scaling factor and offset of the channel, respectively. This represents the result of instance normalization.

[0030] The object detection module 202 can output data 208 corresponding to the region of interest in the image data 206.

[0031] Now for reference Figure 3 The flowchart illustrates the steps of a method 300 performed by a program that segments biological image data.

[0032] At 302, method 300 may include receiving image data.

[0033] At 304, method 300 may include detecting one or more objects associated with the received image data.

[0034] At 306, method 300 may include determining one or more regions of interest in the received image data that correspond to one or more segments based on the detected object.

[0035] This can be understood as, Figure 3 This is merely an illustration of one implementation and does not imply any limitations on how different embodiments are implemented. Many modifications can be made to the depicted environment based on design and implementation requirements.

[0036] Figure 4 According to the illustrative embodiments Figure 1 Block diagram 400 depicts the internal and external components of the computer. It should be understood that... Figure 4 The illustrations are provided only to illustrate one implementation and do not imply any limitation on the environments in which different embodiments may be implemented. Many modifications can be made to the depicted environment based on design and implementation requirements.

[0037] Computer 102 ( Figure 1 ) and server computer 114 ( Figure 1 ) may include Figure 5 The internal components 800A, 800B and the corresponding groups of external components 900A, 900B shown, each group of internal components 800 includes one or more processors 820 on one or more buses 826, one or more computer-readable RAMs 822 and one or more computer-readable ROMs 824, one or more operating systems 828 and one or more computer-readable tangible storage devices 830.

[0038] Processor 820 is implemented in hardware, firmware, or a combination of hardware and software. Processor 820 is a central processing unit (CPU), graphics processing unit (GPU), accelerated processing unit (APU), microprocessor, microcontroller, digital signal processor (DSP), field-programmable gate array (FPGA), application-specific integrated circuit (ASIC), or other type of processing component. In some implementations, processor 820 includes one or more processors that can be programmed to perform functions. Bus 826 includes components that allow communication between internal components 800A and 800B.

[0039] One or more operating systems 828, software programs 108 ( Figure 1 ) and server computer 114 ( Figure 1 Image segmentation program 116 on ) Figure 1 The data is stored on one or more storage devices in a corresponding computer-readable tangible storage device 830 for execution by one or more processors in a corresponding processor 820 via one or more RAMs in a corresponding RAM 822 (which typically includes cache memory). Figure 4In the illustrated embodiment, each storage device in the computer-readable tangible storage device 830 is a disk storage device in the form of an internal hard disk drive. Alternatively, each storage device in the computer-readable tangible storage device 830 is a semiconductor storage device, such as ROM 824, EPROM, flash memory, optical disc, magneto-optical disc, solid-state disk, compact disc (CD), digital versatile disc (DVD), floppy disk, cassette tape, magnetic tape, and / or other types of non-transitory computer-readable tangible storage devices capable of storing computer programs and digital information.

[0040] Each set of internal components 800A, 800B also includes an R / W drive or interface 832 for reading from and writing to one or more portable computer-readable tangible storage devices 936, such as CD-ROMs, DVDs, memory sticks, magnetic tapes, disks, optical discs, or semiconductor storage devices. Software programs, such as software program 108 (… Figure 1 ) and image segmentation program 116 ( Figure 1 It can be stored on one or more storage devices in a corresponding portable computer-readable tangible storage device 936, and read and loaded into a corresponding hard disk drive 830 via a corresponding R / W drive or interface 832.

[0041] Each set of internal components 800A, 800B also includes a network adapter or interface 836, such as a TCP / IP adapter card; a wireless Wi-Fi interface card; or a 3G, 4G, or 5G wireless interface card, or other wired or wireless communication links. Software program 108 ( Figure 1 ) and server computer 114 ( Figure 1 Image segmentation program 116 on ) Figure 1 Computer 102 can download data from an external computer to the computer via a network (such as the Internet, LAN, or other networks, WAN) and a corresponding network adapter or interface 836. Figure 1 The software program 108 and the image segmentation program 116 on the server computer 114 are loaded from the network adapter or interface 836 into the corresponding hard disk drive 830. The network may include copper wire, fiber optic, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers.

[0042] Each set of external components 900A, 900B may include a computer monitor 920, a keyboard 930, and a computer mouse 934. External components 900A, 900B may also include a touchscreen, virtual keyboard, touchpad, pointing device, and other human-machine interface devices. Each set of internal components 800A, 800B also includes a device driver 840 that interfaces with the computer monitor 920, keyboard 930, and computer mouse 934. Device driver 840, R / W driver or interface 832, and network adapter or interface 836 include hardware and software (stored in storage device 830 and / or ROM 824).

[0043] It should be understood beforehand that while this disclosure includes a detailed description of cloud computing, the implementation of the teachings described herein is not limited to cloud computing environments. Rather, some embodiments can be implemented in conjunction with any other type of computing environment now known or developed hereafter.

[0044] Cloud computing is a service delivery model that enables convenient, on-demand network access to a shared pool of configurable computing resources, such as networks, network bandwidth, servers, processing power, storage, applications, virtual machines, and services. This shared pool of configurable computing resources can be rapidly provisioned and released with minimal management effort or interaction with the service provider. This cloud model may include at least five features, at least three service models, and at least four deployment models.

[0045] The features are as follows:

[0046] On-demand self-service: Cloud consumers can automatically and unilaterally provide computing power, such as server time and network storage, as needed, without requiring manual interaction with the service provider.

[0047] Extensive network access: Capabilities are available through the network and accessed via standard mechanisms that facilitate the use of heterogeneous thin or thick client platforms, such as mobile phones, laptops, and PDAs.

[0048] Resource pooling: A provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, where different physical and virtual resources are dynamically allocated and reallocated based on demand. Location-independent meaning exists because consumers typically do not control or know the precise location of the resources provided, but can specify the location at a higher level of abstraction (e.g., country, state, or data center).

[0049] Rapid and flexible: Capabilities can be provided quickly and flexibly, automatically in some cases to scale out quickly and release quickly to scale in quickly. To consumers, the available capacity often appears unlimited and can be purchased in any quantity at any time.

[0050] Measuring services: Cloud systems automatically control and optimize resource usage by leveraging metering capabilities at a certain level of abstraction appropriate to service types (such as storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency to both service providers and consumers.

[0051] The service model is as follows:

[0052] Software as a Service (SaaS): This provides consumers with the ability to use applications running on the provider's cloud infrastructure. The applications can be accessed from various client devices via thin client interfaces such as web browsers (e.g., web-based email). Consumers may not manage or control the underlying cloud infrastructure, including the network, servers, operating system, storage, or even the individual application capabilities, except for limited user-specific application configuration settings.

[0053] Platform as a Service (PaaS): This provides capabilities to consumers that are deployed on cloud infrastructure. These applications are created or acquired by the consumer using programming languages ​​and tools supported by the provider. Consumers do not manage or control the underlying cloud infrastructure, including networks, servers, operating systems, or storage, but they do control over the deployed applications and the application configurations in the potentially hosted environments.

[0054] Infrastructure as a Service (IaaS): This provides consumers with processing, storage, networking, and other basic computing resources that enable them to deploy and run any software, including operating systems and applications. Consumers do not manage or control the underlying cloud infrastructure, but they do have control over the operating system, storage, deployed applications, and possibly limited control over the selection of networking components (e.g., host firewalls).

[0055] The deployment model is as follows:

[0056] Private cloud: Cloud infrastructure that operates solely for an organization. This cloud infrastructure can be managed by the organization or a third party and can exist in an internal or external deployment.

[0057] Community cloud: Cloud infrastructure shared by several organizations and supporting a specific community with shared concerns (such as tasks, security requirements, policies, and compliance considerations). The cloud infrastructure can be managed by the organization or a third party and can exist in an internal or external deployment.

[0058] Public cloud: Cloud infrastructure available to the general public or large industrial groups and owned by organizations that sell cloud services.

[0059] Hybrid cloud: A cloud infrastructure consisting of two or more clouds (private, community, or public) that retain their own entities but are bound together by standardized or proprietary technologies that enable data and application portability (e.g., cloud bursts for load balancing between clouds).

[0060] Cloud computing environments are service-oriented, focusing on statelessness, loose coupling, modularity, and semantic interoperability. At the heart of cloud computing is the infrastructure of a network of interconnected nodes.

[0061] refer to Figure 5 The diagram illustrates an illustrative cloud computing environment 500. As shown, the cloud computing environment 500 includes one or more cloud computing nodes 10, whose local computing devices, such as personal digital assistants (PDAs) or cellular phones 54A, desktop computers 54B, laptop computers 54C, and / or automotive computer systems 54N, can communicate with each other. The cloud computing nodes 10 can be physically or virtually grouped (not shown) into one or more networks, such as the private cloud, community cloud, public cloud, or hybrid cloud described above, or a combination thereof. This allows the cloud computing environment 500 to provide infrastructure, platform, and / or software as a service that cloud consumers do not need to maintain resources on their local computing devices. It should be understood that... Figure 5 The types of computing devices 54A-N shown are intended to be illustrative only, and cloud computing node 10 and cloud computing environment 500 can communicate with any type of computerized device via any type of network and / or network-addressable connection (e.g., using a web browser).

[0062] refer to Figure 6 This demonstrates the 500-fold cloud computing environment ( Figure 5 This provides a set of functional abstraction layers, 600. It should be understood beforehand that... Figure 6 The components, layers, and functions shown are intended to be illustrative only, and the embodiments are not limited thereto. As depicted, the following layers and corresponding functions are provided:

[0063] The hardware and software layer 60 includes hardware and software components. Examples of hardware components include: a host 61; a server 62 based on a RISC (Reduced Instruction Set Computer) architecture; a server 63; a blade server 64; a storage device 65; and network and networking components 66. In some embodiments, software components include network application server software 67 and database software 68.

[0064] The virtualization layer 70 provides an abstraction layer from which the following examples of virtual entities can be provided: virtual server 71; virtual storage 72; virtual network 73, including virtual private network; virtual application and operating system 74; and virtual client 75.

[0065] In one embodiment, management layer 80 may provide the functions described below. Resource provisioning 81 provides dynamic procurement of computing resources and other resources for performing tasks within the cloud computing environment. Metering and pricing 82 provides cost tracking and billing or invoicing for the consumption of these resources as they are utilized in the cloud computing environment. In one embodiment, these resources may include application software licenses. Security provides authentication for cloud consumers and tasks, and protection for data and other resources. User portal 83 provides access to the cloud computing environment for consumers and system administrators. Service level management 84 provides cloud resource allocation and management to meet required service levels. Service level agreement (SLA) planning and implementation 85 provides pre-scheduling and procurement of cloud resources, anticipating future demand for cloud resources based on the SLA.

[0066] Workload layer 90 provides examples of functionalities that can be leveraged in a cloud computing environment. Examples of workloads and functionalities that can be provided from this layer include: mapping and navigation 91; software development and lifecycle management 92; virtual classroom education delivery 93; data analytics and processing 94; transaction processing 95; and image segmentation 96. Image segmentation 96 can segment biological image data based on reusing central data associated with objects in the image data.

[0067] Some embodiments may relate to systems, methods, and / or computer-readable media integrated at any possible level of technical detail. A computer-readable medium may include a computer-readable non-transitory storage medium (or medium) having computer-readable program instructions on it for causing a processor to perform operations.

[0068] A computer-readable storage medium can be a tangible device capable of holding and storing instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to: electronic storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of computer-readable storage media includes: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable optical disc read-only memory (CD-ROM), digital versatile disc (DVD), memory stick, floppy disk, mechanical encoding devices (e.g., punched cards or raised structures in recesses on which instructions are recorded), and any suitable combination of the foregoing. As used herein, a computer-readable storage medium should not be construed as being itself a transient signal, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses passing through fiber optic cables), or electrical signals transmitted through wires.

[0069] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to a suitable computing / processing device, or downloaded via a network (e.g., the Internet, a local area network, a wide area network, and / or a wireless network) to an external computer or external storage device. The network may include copper transmission cables, fiber optic cables, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives and forwards the computer-readable program instructions from the network to a computer-readable storage medium within the suitable computing / processing device.

[0070] Computer-readable program code / instructions used to perform operations can be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, integrated circuit configuration data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and procedural programming languages ​​such as the "C" programming language or similar programming languages. The computer-readable program instructions can run entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., via the Internet provided by an Internet service provider). In some embodiments, electronic circuitry, such as that including programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), can be customized using state information from the computer-readable program instructions to run the computer-readable program instructions to perform aspects or operations.

[0071] These computer-readable program instructions may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute on the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / actions specified in one or more blocks of a flowchart and / or block diagram. These computer-readable program instructions may also be stored in a computer-readable storage medium that directs a computer, programmable data processing apparatus, and / or other device to function in a particular manner, such that the computer-readable storage medium storing the instructions includes an article of writing comprising instructions for implementing aspects of the functions / actions specified in one or more blocks of a flowchart and / or block diagram.

[0072] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus or other equipment to cause a series of operational steps to be performed on the computer, other programmable apparatus or other equipment to produce a computer-implemented process, such that the instructions running on the computer, other programmable apparatus or other equipment perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0073] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer-readable media according to various embodiments. In this regard, each box in a flowchart or block diagram may represent a module, segment, or portion of instructions, including one or more executable instructions for implementing a specified logical function. Methods, computer systems, and computer-readable media may include more, fewer, different, or differently arranged boxes compared to those depicted in the figures. In some alternative implementations, the functions marked in the boxes may occur in a non-consecutive order. For example, two boxes shown consecutively may actually execute simultaneously or substantially simultaneously, or these boxes may sometimes execute in reverse order, depending on the functions involved. It should also be noted that each box in the block diagrams and / or flowcharts, and combinations of boxes in the block diagrams and / or flowcharts, may be implemented by a hardware-based dedicated system that performs the specified function or action or implements a combination of dedicated hardware and computer instructions.

[0074] It will be apparent that the systems and / or methods described herein can be implemented in various forms of hardware, firmware, or a combination of hardware and software. The actual dedicated control hardware or software code used to implement these systems and / or methods is not a limitation on the implementation method. Therefore, the operation and behavior of the systems and / or methods are described herein without reference to any specific software code—it should be understood that software and hardware can be designed to implement the systems and / or methods based on the description herein.

[0075] Elements, actions, or instructions used herein should not be construed as essential or indispensable unless explicitly stated otherwise. Furthermore, as used herein, the articles “a” and “an” are intended to include one or more items and are used interchangeably with “one or more.” Additionally, as used herein, the term “set / group” is intended to include one or more items (e.g., related items, unrelated items, combinations of related and unrelated items, etc.) and is used interchangeably with “one or more.” The term “an” or similar language is used where the intent is to include only one item. Furthermore, as used herein, the terms “have,” “possess,” “contain,” etc., are intended to be open-ended terms. Additionally, the phrase “based on” is intended to mean “at least partially based on” unless explicitly stated otherwise.

[0076] Various aspects and embodiments have been described for illustrative purposes, but these aspects and embodiments are not intended to be exhaustive or limited to the disclosed embodiments. While combinations of features are recited in the claims and / or disclosed in the description, these combinations are not intended to limit the disclosure of possible implementations. In fact, many of these features can be combined in ways not specifically recited in the claims and / or disclosed in the description. Although each dependent claim listed below may be directly dependent on only one claim, the disclosure of possible implementations includes combinations of each dependent claim with each other claim in the claim set. Many modifications and variations will be apparent to those skilled in the art without departing from the scope of the described embodiments. The terminology used herein is chosen to best explain the principles of the embodiments, practical applications or improvements to techniques found in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for image segmentation, characterized in that, The method includes: Receive image data; Determine the center object data of at least one object contained in the received image data; By reusing at least one of the central object data to separate the attached object from the foreground, the image data contains the at least one object, wherein the at least one object is detected based on predicting at least one of the following: a center heatmap, the width and height of a bounding box, and at least one center offset associated with the at least one object; segmenting the attached object from the at least one object based on instance normalization of object features extracted from the central object data; and Identify at least one region of interest in the received image data that corresponds to at least one segment of the detected object.

2. The method according to claim 1, characterized in that, The method further includes determining the at least one region of interest based on the predicted bounding box.

3. The method according to claim 1, characterized in that, The method further includes: performing instance normalization on the received image data based on the detection of adhesion between the at least one object.

4. The method according to claim 1, characterized in that, The method further includes: performing instance normalization on the received image data based on detecting occlusion of a portion of the object from the at least one object.

5. A computer system for segmenting image data, characterized in that, The computer system includes: At least one computer-readable storage medium configured to store computer program code; and At least one computer processor is configured to access the computer program code and operate according to the instructions of the computer program code to perform the image segmentation method as described in any one of claims 1 to 4.

6. An image segmentation apparatus, characterized in that, The device includes: The receiving module is used to receive image data; A detection module is configured to determine center object data of at least one object contained in the received image data; to detect the at least one object contained in the image data by separating attached objects from the foreground through multiplexing at least one of the center object data, wherein the at least one object is detected based on predicting at least one of the following: a center heatmap, the width and height of a bounding box, and at least one center offset associated with the at least one object; and to segment the attached object from the at least one object based on instance normalization of object features extracted from the center object data; and The determination module is used to determine at least one region of interest in the received image data that corresponds to at least one segment of the detected object.

7. A computer-readable storage medium, characterized in that, It stores a computer program for image segmentation, the computer program being configured to cause at least one computer processor to perform the image segmentation method as described in any one of claims 1 to 4.

Images