Device and method for automatically adjusting paraffin block position of tissue slicing device

Abstract

The present disclosure relates to a device and a method for automatically adjusting a paraffin block position of a tissue slicing device, the device including: a body unit; a block fixing unit for fixing a paraffin block and movably and rotatably installed with respect to the body unit; a cutting unit which is arranged on the lower side of the fixing unit in the body unit and on which a blade for cutting the paraffin block is mounted; an information acquisition unit for acquiring first information on the paraffin block fixed to the block fixing unit; and a processor for acquiring second information on a tissue within the paraffin block on the basis of the first information, determining a position and an angle for cutting the paraffin block on the basis of the second information, and controlling the operation of the block fixing unit on the basis of the determined position and angle.

Description

Device and method for automatically adjusting the position of a paraffin block in a tissue sectioning machine

[0001] The present disclosure relates to a device for automatically adjusting the position of a paraffin block to obtain tissue within the paraffin block.

[0002] Paraffin blocks, which are pathological specimens made into blocks by fixing and infiltrating paraffin after surgery or biopsy, are used as materials for DNA or RNA extraction for making HE slides, immunohistochemical staining, or biomarker testing for treatment.

[0003] The process of obtaining thin paraffin sections for making pathology slides or performing biomarker tests is called excision or cutting. The paraffin block is fixed to a means for fixing the paraffin block and excising it using a blade.

[0004] Typically, paraffin sections are cut until the paraffin block is parallel to the cutting blade, and then the sections are cut for further examination. This process of cutting until the paraffin block is parallel is called trimming.

[0005] In cases such as lung cancer biopsies where the tissue is very small and there are many biomarker tests to be performed, excessive trimming can lead to rapid tissue loss, making further testing impossible. Therefore, it is necessary to secure the block parallel to the paraffin cutting blade when starting cutting. Since this work has been done manually, it is difficult to avoid tissue loss due to trimming. In addition, there is the problem that the horizontality of the block itself and the true horizontality of the paraffin surface may not match.

[0006] Accordingly, there is a need for a device that automatically sets the paraffin block to be cut appropriately according to the shape, size, and number of tissues within the paraffin block, but such technology is not currently available.

[0007] The purpose of the embodiment disclosed in the present disclosure is to provide a device for automatically adjusting the position of a paraffin block of a tissue sectioner to accurately obtain tissue within the paraffin block.

[0008] In addition, the embodiment disclosed in the present disclosure aims to provide a device capable of automatically adjusting the position of a paraffin block using an image captured of the paraffin block and a step difference measured for a tissue within the paraffin block.

[0009] In addition, the embodiment disclosed in the present disclosure aims to provide a device capable of automatically adjusting the position of a paraffin block using an artificial intelligence model that learns how to adjust the position of a block fixing portion to which a paraffin block is fixed according to the size, shape and number of tissues within the paraffin block.

[0010] The problems to be solved by the present disclosure are not limited to the problems mentioned above, and other problems not mentioned will be clearly understood by those skilled in the art from the description below.

[0011] In order to solve the above-described problem, an apparatus for automatically adjusting the position of a paraffin block of a tissue sectioning machine according to one embodiment of the present disclosure comprises: a body part; a block fixing part that fixes a paraffin block and is installed to be movable and rotatable with respect to the body part; a cutting part that is disposed below the fixing part in the body part and has a blade for cutting the paraffin block mounted thereon; an information obtaining part that obtains first information about the paraffin block fixed to the block fixing part; and a processor that obtains second information about tissue in the paraffin block based on the first information, determines a position and angle for cutting the paraffin block based on the second information, and controls an operation of the block fixing part based on the determined position and angle.

[0012] In addition, the information acquisition unit may include a camera for capturing an image of the paraffin block; a step measuring device for measuring a step of the tissue within the paraffin block, and the first information may include the captured image and the measured step of the tissue.

[0013] Additionally, the position and angle for cutting the paraffin block can be determined based on the current angle of the blade, the current position of the paraffin block, and the current angle of the paraffin block.

[0014] In addition, the processor can calculate a position and angle for cutting the paraffin block to obtain the tissue based on an image of the tissue in the photographed image and a step difference of the tissue measured, and control the position and angle of the block fixing part so that the paraffin block is at the calculated position and the calculated angle.

[0015] Additionally, the second information may include at least one of the size, shape and number of the tissue within the paraffin block.

[0016] Additionally, the device may include a pre-learned artificial intelligence model, and the artificial intelligence model may be learned about a position and angle for cutting the paraffin block according to the size, shape, and number of tissues within the paraffin block.

[0017] Additionally, the processor can obtain the second information by inputting the captured image and the measured tissue step into the artificial intelligence model.

[0018] In addition, the cutting part can be angle-adjusted from the body part, and the artificial intelligence model can adjust the angle of the cutting part according to the size, shape, and number of tissues in the paraffin block.

[0019] In addition, a method for automatically adjusting the position of a paraffin block of a tissue sectioning machine according to one embodiment of the present disclosure for solving the above-described problem is a method performed by a device, comprising: a step of obtaining first information about a paraffin block fixed to a block fixing member that fixes the paraffin block and is installed to be movable and rotatable with respect to a body part; a step of obtaining second information about a tissue in the paraffin block based on the first information; a step of determining a position and angle for cutting the paraffin block based on the second information; and a step of controlling the block fixing member based on the determined position and angle.

[0020] In addition, a computer program stored in a computer-readable recording medium for executing the present disclosure may be further provided.

[0021] In addition, a computer-readable recording medium recording a computer program for executing a method for implementing the present disclosure may be further provided.

[0022] According to the aforementioned problem solving means of the present disclosure, the effect of automatically adjusting the position of the paraffin block of the tissue sectioner to accurately obtain the tissue within the paraffin block is provided.

[0023] In addition, according to the above-described problem solving means of the present disclosure, the position of the paraffin block can be automatically adjusted using an image captured for the paraffin block and a step difference measured for the tissue within the paraffin block.

[0024] In addition, according to the aforementioned problem solving means of the present disclosure, the position of the paraffin block can be automatically adjusted using an artificial intelligence model that has learned a method of adjusting the position of the block fixing part to which the paraffin block is fixed according to the size, shape, and number of tissues within the paraffin block.

[0025] FIG. 1 is a schematic diagram of a system according to an embodiment of the present disclosure.

[0026] FIG. 2 is a block diagram of a device for automatically adjusting the position of a paraffin block of a tissue sectioning machine according to an embodiment of the present disclosure.

[0027] Figure 3 is a drawing illustrating a paraffin block containing tissue.

[0028] FIG. 4 is an exemplary diagram of a device for automatically adjusting the position of a paraffin block of a tissue sectioning machine according to an embodiment of the present disclosure.

[0029] FIG. 5 is a flowchart of a method for automatically adjusting the position of a paraffin block of a tissue sectioning machine according to an embodiment of the present disclosure.

[0030] Figure 6 is a diagram illustrating a method of teaching an artificial intelligence model to automatically adjust the position of a paraffin block.

[0031] Fig. 7 is a drawing illustrating automatic adjustment of the paraffin block position using an artificial intelligence model learned as in Fig. 6.

[0032] Throughout this disclosure, the same reference numerals denote the same components. This disclosure does not describe all elements of the embodiments, and any content that is common in the technical field to which this disclosure pertains or that overlaps between embodiments is omitted. The terms "part, module, element, block" used in the specification may be implemented in software or hardware, and depending on the embodiments, multiple "parts, modules, elements, blocks" may be implemented as a single component, or a single "part, module, element, block" may include multiple components.

[0033] Throughout the specification, when a part is said to be "connected" to another part, this includes not only direct connection but also indirect connection, and indirect connection includes connection via a wireless communication network.

[0034] Additionally, when a part is said to "include" a component, this does not mean that it excludes other components, but rather that it may include other components, unless otherwise specifically stated.

[0035] Throughout the specification, when we say that an element is "on" another element, this includes not only cases where the element is in contact with the other element, but also cases where another element exists between the two elements.

[0036] The terms first, second, etc. are used to distinguish one component from another, and the components are not limited by the aforementioned terms.

[0037] Singular expressions include plural expressions unless the context clearly indicates otherwise.

[0038] The identification codes for each step are used for convenience of explanation and do not describe the order of each step. Each step may be performed in a different order than specified unless the context clearly indicates a specific order.

[0039] The operating principle and embodiments of the present disclosure are described below with reference to the attached drawings.

[0040] As used herein, the term "device according to the present disclosure" encompasses a variety of devices capable of performing computational processing and providing results to a user. For example, the device according to the present disclosure may include a computer, a server device, and a portable terminal, or may be any one of them.

[0041] Here, the computer may include, for example, a notebook, desktop, laptop, tablet PC, slate PC, etc. equipped with a web browser.

[0042] The above server device is a server that processes information by communicating with an external device, and may include an application server, a computing server, a database server, a file server, a game server, a mail server, a proxy server, and a web server.

[0043] The above portable terminal may include, for example, all kinds of handheld-based wireless communication devices such as PCS, GSM, PDC (Personal Digital Cellular), PHS (Personal Handyphone System), PDA (Personal Digital Assistant), IMT (International Mobile Telecommunication)-2000, CDMA (Code Division Multiple Access)-2000, W-CDMA (W-Code Division Multiple Access), WiBro (Wireless Broadband Internet) terminals, smart phones, etc., as well as wearable devices such as watches, rings, bracelets, anklets, necklaces, glasses, contact lenses, or head-mounted devices (HMD).

[0044] The artificial intelligence-related functions according to the present disclosure are operated through a processor and a storage unit. The processor may be composed of one or more processors. In this case, one or more processors may be a general-purpose processor such as a CPU, an AP, a Digital Signal Processor (DSP), a graphics-only processor such as a GPU or a Vision Processing Unit (VPU), or an artificial intelligence-only processor such as an NPU. One or more processors control the processing of input data according to predefined operation rules or artificial intelligence models stored in the storage unit. Alternatively, if one or more processors are artificial intelligence-only processors, the artificial intelligence-only processor may be designed with a hardware structure specialized for processing a specific artificial intelligence model.

[0045] The predefined operation rules or artificial intelligence models are characterized by being created through learning. Here, being created through learning means that the basic artificial intelligence model is learned by a learning algorithm using a plurality of learning data, thereby creating the predefined operation rules or artificial intelligence models set to perform a desired characteristic (or purpose). This learning may be performed on the device itself on which the artificial intelligence according to the present disclosure is performed, or may be performed through a separate server and / or system. Examples of the learning algorithm include supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but are not limited to the examples described above.

[0046] An artificial intelligence model may be composed of multiple neural network layers. Each of the multiple neural network layers has multiple weights, and performs neural network operations through operations between the operation results of the previous layer and the multiple weights. The multiple weights of the multiple neural network layers may be optimized based on the learning results of the artificial intelligence model. For example, the multiple weights may be updated so that the loss value or cost value obtained from the artificial intelligence model is reduced or minimized during the learning process. The artificial neural network may include a deep neural network (DNN), and examples thereof include, but are not limited to, a convolutional neural network (CNN), a deep neural network (DNN), a recurrent neural network (RNN), a restricted boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), or a deep Q-network.

[0047] According to an exemplary embodiment of the present disclosure, a processor can implement artificial intelligence. Artificial intelligence refers to a machine learning method based on an artificial neural network that mimics human neurons (biological neurons) to enable machines to learn. AI methodologies can be categorized into supervised learning, where input and output data are provided together as training data, thereby determining the solution (output data) to a problem (input data); unsupervised learning, where only input data is provided without output data, so the solution (output data) to a problem (input data) is not determined; and reinforcement learning, where a reward is provided from an external environment each time an action is taken in the current state, and learning proceeds in a direction that maximizes this reward. Furthermore, AI methodologies can be categorized by the architecture of the learning model. The architectures of widely used deep learning technologies can be categorized into convolutional neural networks, recurrent neural networks, transformers, and generative adversarial networks.

[0048] The device may include an artificial intelligence model. The artificial intelligence model may be a single artificial intelligence model or may be implemented as multiple artificial intelligence models. The artificial intelligence model may be composed of a neural network (or artificial neural network) and may include statistical learning algorithms that mimic biological neurons in machine learning and cognitive science. A neural network may refer to a model in general that has problem-solving capabilities by changing the binding strength of synapses through learning, formed by artificial neurons (nodes) that form a network by combining synapses. The neurons of the neural network may include a combination of weights or biases. The neural network may include one or more layers composed of one or more neurons or nodes. For example, the device may include an input layer, a hidden layer, and an output layer. The neural network constituting the device can infer a desired outcome from an arbitrary input by changing the weights of the neurons through learning.

[0049] The processor can create a neural network, train (or learn) a neural network, perform a calculation based on received input data, generate an information signal based on the calculation result, or retrain the neural network. The models of the neural network can include various types of models such as CNN, R-CNN, RPN, RNN, S-DNN, S-SDNN, Deconvolution Network, DBN, RBM, Fully Convolutional Network, LSTM Network, Classification Network, etc., such as GoogleNet, AlexNet, VGG Network, etc., but are not limited thereto. The processor can include one or more processors for performing calculations according to the models of the neural network. For example, the neural network can include a deep neural network.

[0050] Neural networks include CNN, RNN, perceptron, multilayer perceptron, FF (Feed Forward), RBF (Radial Basis Network), DFF (Deep Feed Forward), LSTM (Long Short Term Memory), GRU (Gated Recurrent Unit), AE (Auto Encoder), VAE (Variational Auto Encoder), DAE (Denoising Auto Encoder), SAE (Sparse Auto Encoder), MC (Markov Chain), HN (Hopfield Network), BM (Boltzmann Machine), RBM (Restricted Boltzmann Machine), DBN (Depp Belief Network), DCN (Deep Convolutional Network), DN (Deconvolutional Network), DCIGN (Deep Convolutional Inverse Graphics Network), GAN (Generative Adversarial Network), LSM (Liquid State Machine), ELM (Extreme Learning) Machine), ESN (Echo State Network), DRN (Deep Residual Network), DNC (Differentiable Network) It will be understood by those skilled in the art that the neural network may include any neural network, including but not limited to a Neural Computer (NN), a Neural Turning Machine (NTM), a Capsule Network (CN), a Kohonen Network (KN), and an Attention Network (AN).

[0051] According to an exemplary embodiment of the present disclosure, the processor may be configured to perform a process for generating a CNN (Convolution Neural Network) such as GoogleNet, AlexNet, VGG Network, Region with Convolution Neural Network (R-CNN), Region Proposal Network (RPN), Recurrent Neural Network (RNN), Stacking-based deep Neural Network (S-DNN), State-Space Dynamic Neural Network (S-SDNN), Deconvolution Network, Deep Belief Network (DBN), Restrcted Boltzman Machine (RBM), Fully Convolutional Network, Long Short-Term Memory (LSTM) Network, Classification Network, Generative Modeling, eXplainable AI, Continual AI, Representation Learning, AI for Material Design, BERT, SP-BERT, MRC / QA for natural language processing, Text Analysis, Dialog System, GPT-3, GPT-4, Visual Analytics for vision processing, Visual Understanding, Video Synthesis, ResNet for data intelligence, Anomaly Detection, Prediction, Time-Series Forecasting, Various artificial intelligence structures and algorithms, including optimization, recommendation, and data creation, can be utilized, but are not limited thereto. Hereinafter, embodiments of the present disclosure will be described in detail with reference to the attached drawings.

[0052] FIG. 1 is a schematic diagram showing a system (10) according to an embodiment of the present disclosure.

[0053] Referring to FIG. 1, a system (10) according to an embodiment of the present disclosure includes a tissue sectioning device (100), a paraffin block management system (200), and a digital pathology system (300).

[0054] However, in some embodiments, the system may include fewer or more components than those illustrated in FIG. 1.

[0055] The tissue sectioning device may be a device (100) that automatically adjusts the paraffin block position of the tissue sectioning device (100) according to an embodiment of the present disclosure.

[0056] In an embodiment of the present disclosure, the paraffin block may be composed of tissue, paraffin, and a plastic plate.

[0057] In order to transmit and receive information between systems, the tissue sectioning device, the paraffin block management system (200), and the digital pathology system (300) may each include a communication unit and a network including wired / wireless communication functions.

[0058] A network can include any accessible network, such as the Internet, an intranet, an extranet, a Local Area Network (LAN), a Metropolitan Area Network (MAN), or a Wide Area Network (WAN).

[0059] The communication interface may include a module for performing mobile communication, short-range communication, etc., and may include a module for performing wired or wireless communication.

[0060] For example, the communication interface may perform data communication of the computing device using at least one of data communication methods including wired LAN, wireless LAN, Wi-Fi, Bluetooth, zigbee, Wi-Fi Direct (WFD), infrared Data Association (IrDA), Bluetooth Low Energy (BLE), Near Field Communication (NFC), Wireless Broadband Internet (Wibro), World Interoperability for Microwave Access (WiMAX), Shared Wireless Access Protocol (SWAP), Wireless Gigabit Alliances (WiGig), and RF communication.

[0061] A tissue sectioning device that obtains tissue by cutting a paraffin block, a paraffin block management system that manages the paraffin block, and a digital pathology system that digitalizes pathology-related tasks using the obtained tissue can be combined to build a single system. In the embodiment of the present disclosure, the tissue sectioning device will be described in detail.

[0062] FIG. 2 is a block diagram of a device (100) for automatically adjusting the position of a paraffin block (50) of a tissue sectioning machine according to an embodiment of the present disclosure.

[0063] Figure 3 is a drawing illustrating a paraffin block (50) containing tissue.

[0064] FIG. 4 is an exemplary diagram of a device (100) that automatically adjusts the position of a paraffin block (50) of a tissue sectioning machine according to an embodiment of the present disclosure.

[0065] Referring to FIGS. 2 to 4 together, a device (100) according to an embodiment of the present disclosure will be described.

[0066] Referring to FIGS. 2 to 4, a device (100) for automatically adjusting the position of a paraffin block (50) of a tissue sectioning machine according to an embodiment of the present disclosure includes a processor (110), a storage unit (120), a block fixing unit (130), a cutting unit (140), an information acquisition unit (150), a body unit (105), and a blade (145), and the information acquisition unit (150) includes a camera (152) and a step measuring device (156).

[0067] However, in some embodiments, the device (100) may include fewer or more components than those illustrated in FIG. 2.

[0068] FIG. 2 illustrates configurations that can be controlled by a processor (110), and FIG. 3 illustrates configurations that appear externally of the device (100).

[0069] A device (100) for automatically adjusting the position of a paraffin block (50) of a tissue sectioning machine according to an embodiment of the present disclosure may be referred to as a tissue sectioning machine device (100), a tissue sectioning machine device (100) capable of adjusting the position of a paraffin block (50), etc.

[0070] The processor (110) can execute instructions and algorithms stored in the storage unit (120) and automatically control the position of the paraffin block (50) using an artificial intelligence model, and can control the block fixing unit (130), cutting unit (140), and information acquisition unit (150).

[0071] The processor (110) may be implemented as a storage unit (120) that stores data regarding an algorithm for controlling the operation of components within the device (100) or a program that reproduces the algorithm, and at least one processor (110) that performs the aforementioned operation using the data stored in the storage unit (120). In this case, the storage unit (120) and the processor (110) may each be implemented as separate chips. Alternatively, the storage unit (120) and the processor (110) may be implemented as a single chip.

[0072] In addition, the processor (110) can control any one or a combination of the components described above to implement various embodiments according to the present disclosure described in the drawings below on the device (100).

[0073] In addition to the operations related to the above-described application, the processor (110) can typically control the overall operation of the device (100). The processor (110) can process signals, data, information, etc. input or output through the components discussed above, or run an application program stored in the storage unit (120), thereby providing or processing appropriate information or functions to the user.

[0074] In addition, the processor (110) can control at least some of the components of the device (100) to run an application program stored in the storage unit (120). Furthermore, the processor (110) can operate at least two or more of the components included in the device (100) in combination with each other to run the application program.

[0075] The storage unit (120) can store data that supports various functions of the device (100). The storage unit (120) can store a plurality of application programs (or applications) that run on the device (100), data for the operation of the device (100), and commands. At least some of these application programs may exist for the basic functions of the device (100). Meanwhile, the application programs may be stored on the storage unit (120), installed on the device (100), and driven to perform operations (or functions) by the processor (110).

[0076] The storage unit (120) can store data supporting various functions of the device (100) and programs for the operation of the processor (110), input / output data (e.g., music files, still images, moving images, etc.) can be stored, and a plurality of application programs (or applications) running on the device (100), data for the operation of the device (100), and commands can be stored. At least some of these application programs can be downloaded from an external server via wireless communication.

[0077] The storage unit (120) may include at least one type of storage medium among a flash memory type, a hard disk type, an SSD (Solid State Disk type), an SDD (Silicon Disk Drive type), a multimedia card micro type, a card type memory (e.g., an SD or XD storage unit (120)), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, and an optical disk. In addition, the storage unit (120) may be a database that is separate from the device (100) but is connected by wire or wirelessly.

[0078] Additionally, the storage unit (120) may have multiple processes for the device (100).

[0079] The storage unit (120) stores various commands (instructions) and algorithms for automatically adjusting the position of the paraffin block (50) of the tissue sectioner, and stores a pre-learned artificial intelligence model.

[0080] In the embodiment of the present disclosure, the artificial intelligence model may be composed of an RNN, transformer, etc. capable of time-series data learning.

[0081] In the embodiment of the present disclosure, the information acquisition unit (150) may be a sensing unit, and may include at least one of a camera (152) and a step measuring device (156) configured to acquire information about a paraffin block (50) and a sample within the paraffin block (50).

[0082] The camera (152) may be applied with an image sensor that acquires RGB data, and the step measuring device (156) may be applied with a step measuring sensor that measures / senses the step of the tissue within the paraffin block (50). In addition, the information acquisition unit (150) may be applied with any configuration that can measure / sensing the paraffin block (50), image data on the tissue within the paraffin block (50), and the step of the tissue within the paraffin block (50).

[0083] The camera (152) processes image frames, such as still images or moving images, obtained by the image sensor in shooting mode. The processed image frames can be displayed on the display unit or stored in the storage unit (120).

[0084] The sensing unit senses at least one of the internal information of the device (100), the surrounding environmental information surrounding the device (100), and the user information, and generates a sensing signal corresponding thereto. Based on this sensing signal, the processor (110) can control the operation or driving of the device (100), or perform data processing, functions, or operations related to an application program installed in the device (100).

[0085] As described above, the sensing unit may include at least one of a proximity sensor, an illumination sensor, a touch sensor, an acceleration sensor, a magnetic sensor, a G-sensor, a gyroscope sensor, a motion sensor, an RGB sensor, an infrared sensor (IR sensor), a fingerprint recognition sensor, an ultrasonic sensor, an optical sensor (e.g., a camera (152)), a microphone, an environmental sensor (e.g., at least one of a barometer, a hygrometer, a thermometer, a radiation detection sensor, a heat detection sensor, and a gas detection sensor), and a chemical sensor (e.g., a healthcare sensor, a biometric recognition sensor, etc.). Meanwhile, the present device (100) may utilize information sensed by at least two or more of these sensors in combination.

[0086] The body part (105) refers to the body of the device (100). A block fixing part (130), a cutting part (140), a processor (110), and a storage part (120) can be provided in the body part (105).

[0087] The block fixing member (130) fixes the paraffin block (50) but is installed so that it can move and rotate with respect to the body member (105).

[0088] The block fixing member (130) may include a fixing means for fixing the paraffin block (50), and may be coupled to a movable member to enable movement while coupled / installed to the body member (105). The movable member may move up and down and left and right from the body member (105) to adjust its position, and may further include components such as a motor and actuator for movement.

[0089] That is, the body part (105) can have a moving means installed, and a block fixing part (130) can be installed on the moving means.

[0090] Additionally, the block fixing member (130) may be installed so as to be rotatable from the body member (105). The moving means may be capable of rotation as well as positional movement, and the block fixing member (130) may be formed so as to be rotatable. In some implementations, the fixing means for fixing the paraffin block (50) may be configured to be rotatable from the block fixing member (130).

[0091] The cutting part (140) is placed on the lower side of the fixed part in the body part (105), and a blade (145) for cutting the paraffin block (50) can be mounted / combined.

[0092] In one embodiment, the cutting portion (140) may be formed so that its angle can be adjusted manually or automatically from the body portion (105).

[0093] For example, the cutting section (140) may include an angle adjustment means so that the user can adjust the angle.

[0094] For example, the cutting section (140) may include an angle adjustment means so that the processor (110) can adjust the angle.

[0095] The angle adjustment means can adjust the angle of the cutting part (140) or can adjust the angle of the blade (145) mounted on the cutting part (140).

[0096] Below, the operating process of the device (100) is described in detail with reference to the flowchart of FIG. 5 and other drawings.

[0097] FIG. 5 is a flowchart of a method for automatically adjusting the position of a paraffin block (50) of a tissue sectioning machine according to an embodiment of the present disclosure.

[0098] The processor (110) obtains first information about a paraffin block (50) fixed to a block fixing member (130). (S100)

[0099] The processor (110) controls the information acquisition unit (150) to capture an image of the paraffin block (50) using the camera (152) and to measure the step height of the tissue within the paraffin block (50) using the step height measuring device (156).

[0100] The first information includes an image / video captured by a camera (152) and a value measured by a step measuring device (156).

[0101] In one embodiment, the first information includes an image of a paraffin block (50) captured by a camera (152) and a step height of tissue within the paraffin block (50) measured by a step height measuring device (156).

[0102] In the embodiment of the present disclosure, the camera (152) and the step measuring device (156) may be installed so as to face the position where the paraffin block (50) is combined. That is, the camera (152) photographs the paraffin block (50) and the step measuring device (156) measures the step of the tissue within the paraffin block (50).

[0103] The processor (110) can recognize an image of tissue within a paraffin block (50) from an image captured by a camera (152).

[0104] In one embodiment, the processor (110) may recognize an image of a tissue from an image captured by the camera (152), adjust the shooting magnification of the camera (152) based on the recognized tissue image, and capture an image at the adjusted shooting magnification to obtain a final image. For example, if the size of the tissue in the initially captured image is expected to be smaller than a preset standard, thereby reducing the accuracy of the judgment, the processor (110) may increase the shooting magnification of the camera (152) to control the tissue in the paraffin block (50) to be captured at a large size.

[0105] In one embodiment, the processor (110) recognizes an image of a tissue from an image captured by the camera (152), and if it is determined that a captured image from a different angle is needed based on the recognized tissue image, the processor (110) can control the position of the camera (152) to acquire an additional captured image.

[0106] In some embodiments, the device (100) may include multiple cameras (152) installed at different locations, and the processor (110) may obtain second information using images captured at different angles with respect to the paraffin block (50).

[0107] In one embodiment, the processor (110) may determine the size of the tissue from an image acquired through the information acquisition unit (150), and cause the process to be performed if the size of the tissue is less than 0.5 mm.

[0108] Based on the first information obtained from S100, second information about the tissue in the paraffin block (50) is obtained. (S200)

[0109] In an embodiment of the present disclosure, the second information includes at least one of the size, shape, and number of tissues within the paraffin block (50). In an embodiment of the present disclosure, the second information may further include the location of the tissues within the paraffin block (50).

[0110] That is, the processor (110) can recognize an image of a tissue within a paraffin block (50) from an image captured by a camera (152), and calculate at least one of the size, shape, and dimension of the tissue within the paraffin block (50) based on the image of the tissue and the step of the tissue. In some embodiments, the processor (110) can further determine the location of the tissue within the paraffin block (50).

[0111] Based on the second information, the position and angle for cutting the paraffin block (50) are determined. (S300)

[0112] The processor (110) can calculate the optimal position and optimal angle for cutting the paraffin block (50) fixed to the block fixing member (130) with the blade (145) by using information on at least one of the size, shape, and size of the tissue within the paraffin block (50). At this time, the processor (110) can calculate the optimal position and optimal angle for cutting the paraffin block (50) by further considering the position of the tissue within the paraffin block (50).

[0113] That is, it means that the best results can be obtained when the processor (110) in S300 cuts the paraffin block (50) at the position and angle determined for cutting the paraffin block (50).

[0114] In the embodiment of the present disclosure, determining the position and angle for cutting the paraffin block (50) means determining the optimal position and angle at which tissue within the paraffin block (50) can be obtained without tissue loss.

[0115] In the embodiment of the present disclosure, determining the optimal position and optimal angle of the paraffin block (50) for cutting the paraffin block (50) may mean aligning the tissue so that it is parallel to the blade (145).

[0116] Controls the block control unit based on the position and angle determined in S300. (S400)

[0117] In detail, the processor (110) controls the block fixing part (130) so that the paraffin block (50) becomes the position and angle of the paraffin block (50) determined in S300.

[0118] The processor (110) can also control the block fixing member (130) so that only one of the position and angle of the paraffin block (50) is adjusted, if necessary.

[0119] For example, if the position of the blade (145) of the paraffin block (50) needs to be maintained while the angle needs to be changed, the processor (110) can control the angle to be changed to the corresponding angle while the position of the block fixing member (130) is maintained.

[0120] For example, if the position of the blade (145) of the paraffin block (50) needs to be changed and the angle needs to be maintained, the processor (110) can control the position to be changed to that position while maintaining the angle of the block fixing member (130).

[0121] For example, if the position and angle of the blade (145) of the paraffin block (50) need to be changed, the processor (110) can control the position and angle of the block fixing part (130) to be changed to the corresponding position and angle.

[0122] In some embodiments, the cutting member (140) or blade (145) may include an angle adjustment means capable of automatically adjusting the angle.

[0123] In one embodiment, the processor (110) calculates the optimal cutting position of the paraffin block (50) for obtaining the tissue, the optimal cutting angle of the paraffin block (50), and the optimal cutting angle of the blade (145) based on the image of the tissue in the photographed image and the measured difference in the tissue,

[0124] The position and angle of the block fixing part (130) can be controlled so that the paraffin block (50) becomes the calculated cutting position and cutting angle, and the cutting part (140) can be controlled so that the blade (145) becomes the calculated cutting angle.

[0125] In one embodiment, the processor (110) may control the cutting unit (140) to adjust the angle of the blade (145) if it is determined that adjusting the angle of the blade (145) is more advantageous to the section of the paraffin block (50) than adjusting the angle of the block fixing unit (130).

[0126] Figure 6 is a drawing illustrating a method of teaching an artificial intelligence model to automatically adjust the position of a paraffin block (50).

[0127] Fig. 7 is a drawing illustrating automatic adjustment of the position of a paraffin block (50) using an artificial intelligence model learned as in Fig. 6.

[0128] The artificial intelligence model may be trained on the optimal cutting position and optimal cutting angle of the paraffin block (50) that correspond to the position of the blade (145) according to at least one of the size, shape, and number of tissues within the paraffin block (50).

[0129] The processor (110) can input the first information acquired through the information acquisition unit (150) into an artificial intelligence model to acquire information on the optimal position and optimal angle for cutting the paraffin block (50).

[0130] In the embodiment of the present disclosure, the storage unit (120) may store at least one artificial intelligence model, and, depending on the embodiment, a plurality of artificial intelligence models may be stored.

[0131] For example, the artificial intelligence model may include a first artificial intelligence model, a second artificial intelligence model, and a third artificial intelligence model.

[0132] The first artificial intelligence model may be a model that has learned how to obtain second information about tissue within a paraffin block (50) when an image of the paraffin block (50) and a step for the tissue are input.

[0133] In detail, when first information acquired through the information acquisition unit (150) is input, the first artificial intelligence model can determine at least one of the size, shape, and number of tissues in the paraffin block (50).

[0134] The second artificial intelligence model may be a model that has learned a method of calculating an optimal cutting position and an optimal cutting angle for a paraffin block (50) for cutting the paraffin block (50) when at least one of the size, shape, and number of tissues in the paraffin block (50) is input.

[0135] Therefore, the second artificial intelligence model can calculate the optimal cutting position and optimal cutting angle for the paraffin block (50) for cutting the paraffin block (50) when at least one of the size, shape, and number of tissues in the paraffin block (50) is input.

[0136] In addition, the second artificial intelligence model can also determine the angle of the blade (145) for cutting the paraffin block (50).

[0137] The third artificial intelligence model may be one that has learned how to control the device (100) based on the judgment results of the first artificial intelligence model and the second artificial intelligence model.

[0138] That is, the processor (110) can control at least one of the cutting unit (140) and the block fixing unit (130) using the third artificial intelligence model.

[0139] In one embodiment, the third artificial intelligence model may control at least one of the cutting unit (140) and the block fixing unit (130) based on the judgment results of the first artificial intelligence model and the second artificial intelligence model to ensure that the device (100) is set to the optimal setting for cutting the paraffin block (50), and may control the device (100) to proceed with cutting the paraffin block (50).

[0140] At this time, the third artificial intelligence model may determine the cutting speed of the paraffin block (50) based on at least one of the location, size, shape, and number of tissues within the paraffin block (50).

[0141] In detail, the third artificial intelligence model can calculate the tissue loss rate according to the cutting speed of the paraffin block (50) based on at least one of the position, size, shape, and number of the paraffin block (50), and can control the block fixing unit (130) based on the cutting speed of the paraffin block (50) so as to minimize the tissue loss rate.

[0142] To explain the process for this, the processor (110) can control at least one of the cutting unit (140) and the block fixing unit (130) to obtain the optimal position and optimal angle of the paraffin block (50) for cutting the paraffin block (50) obtained through the artificial intelligence model.

[0143] Next, the processor (110) can control at least one of the cutting unit (140) and the block fixing unit (130) to cut the paraffin block (50).

[0144] According to the device, method and program for automatically adjusting the position of a paraffin block of a tissue sectioning machine described above, the problem of tissue loss occurring due to inaccurate cutting of paraffin blocks by conventional human judgment and operation is solved, and automatic cutting is also possible.

[0145] The method according to one embodiment of the present disclosure described above can be implemented as a program (or application) and stored in a medium to be executed in combination with a hardware server.

[0146] The above-described program may include codes coded in a computer language, such as C, C++, JAVA, or machine language, that can be read by the processor (CPU) of the computer through the device interface of the computer, so that the computer reads the program and executes the methods implemented as a program. Such codes may include functional codes related to functions that define functions necessary for executing the methods, and may include control codes related to execution procedures necessary for the processor of the computer to execute the functions according to a predetermined procedure. In addition, such codes may further include memory reference-related codes regarding which location (address address) of the internal or external memory of the computer should reference additional information or media necessary for the processor of the computer to execute the functions. In addition, if the processor of the computer needs to communicate with any other computer or server located remotely in order to execute the functions, the code may further include communication-related code regarding how to communicate with any other computer or server located remotely using the communication module of the computer, and what information or media to send and receive during communication.

[0147] The above storage medium refers to a medium that stores data semi-permanently and can be read by a device, rather than a medium that stores data for a short period of time, such as a register, cache, or memory. Specifically, examples of the storage medium include, but are not limited to, ROM, RAM, CD-ROM, magnetic tape, floppy disk, and optical data storage device. That is, the program can be stored in various recording media on various servers that the computer can access or in various recording media on the user's computer. In addition, the medium can be distributed across network-connected computer systems, so that computer-readable code can be stored in a distributed manner.

[0148] The steps of a method or algorithm described in connection with the embodiments of the present disclosure may be implemented directly in hardware, implemented as a software module executed by hardware, or implemented by a combination thereof. The software module may reside in a random access memory (RAM), a read only memory (ROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a flash memory, a hard disk, a removable disk, a CD-ROM, or any other form of computer-readable recording medium well known in the art to which the present disclosure pertains.

[0149] While the embodiments of the present disclosure have been described above with reference to the attached drawings, those skilled in the art will appreciate that the present disclosure can be implemented in other specific forms without altering the technical spirit or essential features thereof. Therefore, the embodiments described above should be understood to be illustrative in all respects and not restrictive.

Claims

1. Body part; A block fixing part that fixes a paraffin block and is installed to be able to move and rotate with respect to the body part; A cutting section positioned on the lower side of the fixed section in the body section and equipped with a blade for cutting the paraffin block; An information acquisition unit for acquiring first information about a paraffin block fixed to the block fixing unit; and Obtaining second information about the tissue within the paraffin block based on the first information, Based on the second information, determine the position and angle for cutting the paraffin block, A processor comprising: a processor for controlling the operation of the block fixing part based on the determined position and angle; A device for automatically adjusting the position of a paraffin block in a tissue sectioning machine.

2. In paragraph 1, The above information acquisition unit, a camera for taking images of the paraffin block; and Including a step measuring device for measuring the step of the tissue within the paraffin block, The first information includes the photographed image and the step of the measured tissue. A device for automatically adjusting the position of a paraffin block in a tissue sectioning machine.

3. In paragraph 2, The position and angle for cutting the above paraffin block are: characterized in that it is determined based on the current angle of the blade, the current position of the paraffin block, and the current angle of the paraffin block. A device for automatically adjusting the position of a paraffin block in a tissue sectioning machine.

4. In paragraph 2, The above processor, Based on the image of the tissue in the above-mentioned photographed image and the measured tissue step, the position and angle for cutting the paraffin block to obtain the tissue are calculated, Characterized in that the position and angle of the block fixing part are controlled so that the paraffin block is at the calculated position and the calculated angle. A device for automatically adjusting the position of a paraffin block in a tissue sectioning machine.

5. In paragraph 2, The second information includes at least one of the size, shape and number of the tissue in the paraffin block, The above device includes a pre-learned artificial intelligence model, The above artificial intelligence model is characterized in that it has learned the position and angle for cutting the paraffin block according to the size, shape and number of tissues in the paraffin block. A device for automatically adjusting the position of a paraffin block in a tissue sectioning machine.

6. In paragraph 5, The above processor, Characterized in that the second information is obtained by inputting the photographed image and the measured tissue step into the artificial intelligence model. A device for automatically adjusting the position of a paraffin block in a tissue sectioning machine.

7. In paragraph 6, The above cutting part can be adjusted at an angle from the body part, The above artificial intelligence model is characterized in that it adjusts the angle of the cutting part according to the size, shape and number of tissues in the paraffin block. A device for automatically adjusting the position of a paraffin block in a tissue sectioning machine.

8. In a method performed by a device, A step of obtaining first information about a paraffin block fixed to a block fixing member that is installed to be movable and rotatable relative to a body portion while fixing the paraffin block; A step of obtaining second information about the tissue in the paraffin block based on the first information; A step in which the processor of the device determines a position and angle for cutting the paraffin block based on the second information; and A step in which the processor controls the block fixing part based on the determined position and angle, A method for automatically adjusting the position of a paraffin block in a tissue sectioning machine.

9. In paragraph 1, The first information includes an image taken of the paraffin block and a step difference of the tissue measured within the paraffin block. A method for automatically adjusting the position of a paraffin block in a tissue sectioning machine.

10. In paragraph 9, The position and angle for cutting the above paraffin block are: characterized in that it is determined based on the current angle of the blade, the current position of the paraffin block, and the current angle of the paraffin block. A method for automatically adjusting the position of a paraffin block in a tissue sectioning machine.

11. In paragraph 9, The above processor, Based on the image of the tissue in the above-mentioned photographed image and the measured tissue step, the position and angle for cutting the paraffin block to obtain the tissue are calculated, Characterized in that the position and angle of the block fixing part are controlled so that the paraffin block is at the calculated position and the calculated angle. A method for automatically adjusting the position of a paraffin block in a tissue sectioning machine.

12. In paragraph 9, The second information includes at least one of the size, shape and number of the tissue in the paraffin block, The above device includes a pre-learned artificial intelligence model, The above artificial intelligence model is characterized in that it has learned the position and angle for cutting the paraffin block according to the size, shape and number of tissues in the paraffin block. A method for automatically adjusting the position of a paraffin block in a tissue sectioning machine.

13. In paragraph 12, The above processor, Characterized in that the second information is obtained by inputting the photographed image and the measured tissue step into the artificial intelligence model. A method for automatically adjusting the position of a paraffin block in a tissue sectioning machine.

14. In paragraph 13, The above cutting part can be adjusted at an angle from the body part, The artificial intelligence model is characterized in that it adjusts the angle of the cutting part of the device equipped with a blade for cutting the paraffin block according to the size, shape and number of tissues in the paraffin block. A method for automatically adjusting the position of a paraffin block in a tissue sectioning machine.

15. A computer-readable recording medium that is combined with a computer as hardware and stores a program for executing the method of claim 8.

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