821 results about "Endoscopic image" patented technology

The invention discloses a Transform-CNN medical image segmentation method and system based on multi-scale fusion semantic enhancement, and relates to the technical field of image segmentation, and the method comprises the steps: collecting medical image data, and constructing a medical image data set; constructing a medical image segmentation network model; training the medical image segmentation network model through the medical image data set; and inputting real-time acquired data into the trained medical image segmentation network model to obtain a medical image segmentation result. According to the invention, double encoders, LGDA modules and the like are adopted to capture multi-scale global features of kidney tumors and enhance representation, and the LGDA module is used for adapting to size and form changes of the kidney tumors; the MLCF module is used for supplementing information of the main encoder; the PSE module captures multi-scale global semantic information and integrates local context information, local features are fused through the SAM module, and kidney tumor positioning in an endoscope image is achieved.

The invention discloses a surgical instrument segmentation method based on a double prior guidance network, and the method specifically comprises the steps: constructing a hybrid encoder composed of a visual basic model and a state space model, and carrying out the coding processing of an input surgical endoscope image; the visual basic model is an SAM2-Hira model; the state space model is a MambaVision model; an adapter layer composed of a standard adapter and a Mama enhancement adapter is arranged between the coding layer and the decoding layer, and coding features are processed; setting a multi-context guide decoder, and performing multi-scale feature recovery and mask segmentation; a hybrid encoder, a Mama enhancement adapter and a multi-context guidance decoder form a double-priori guidance network DPG-Net, and high-precision surgical instrument segmentation is realized by using the DPG-Net. Accurate visual support is provided for the surgical robot, and the accuracy and safety of surgical operation are greatly improved.

The invention discloses an endoscope multi-region semantic perception time domain fusion adaptive white balance algorithm. The algorithm comprises the following steps: acquiring an endoscope image; performing tissue region division on the endoscope image through a lightweight semantic segmentation network to obtain a plurality of region images; converting each regional image from an RGB space to a YCbCr color space, separating a brightness channel Y from chrominance channels Cb and Cr, and calculating a channel mean value of the chrominance channels Cb and Cr; calculating the average offset of the chrominance channels Cb and Cr according to the calculated channel average of the chrominance channels Cb and Cr; performing white point detection on each regional image to obtain an RGB three-channel mean value; combining the calculated mean offset of the chromaticity channels Cb and Cr, the RGB three-channel mean obtained by white point detection of each regional image and a statistical index to form a multi-dimensional regional color feature vector; and fusing historical frame features with the regional color feature vector to obtain a feature vector based on space-time fusion.

The invention relates to an intelligent endoscope image evaluation method for ulcerative colitis, and the method comprises the steps: synchronously collecting video streams and three-dimensional point cloud data of a colonic mucosa through an endoscope system equipped with a structured light depth sensor, and constructing a three-dimensional coordinate system based on a colon anatomy trend; based on the three-dimensional point cloud data, constructing a complex topological structure on the surface of the colonic mucosa, and calculating a continuous homology feature set of the lesion area; and based on the persistent homology feature set, constructing a multi-scale graph neural network model of ulcerative colitis and Crohn disease specificity. By implementing the scheme, the structural and topological modeling of the colon mucosa lesion area can be realized, and the morphological characteristic difference between ulcerative colitis and Crohn's disease can be effectively captured. The multi-scale graph neural network fuses macroanatomical segmentation and micropathology features, efficient distinguishing of continuous and jumping lesions is achieved, finally, an intelligent and visual endoscope evaluation report is generated, and the accuracy and real-time performance of auxiliary diagnosis in an operation are improved.

The invention discloses a defogging method based on an endoscope image and an endoscope system.The method is applied to a processor of the endoscope system.The method comprises the steps that the endoscope image is obtained; extracting a texture feature, at least one color feature and a global contrast feature of the endoscope image; determining that fog exists in the endoscope image according to the texture feature, the at least one color feature and the global contrast feature; determining the fog concentration of the endoscope image; and according to the fog concentration, carrying out defogging processing on the endoscope image so as to obtain a defogged image. The accuracy of fog detection and fog removal can be improved, automatic fog monitoring and removal are achieved, and the automation degree of the endoscope is improved.

The invention discloses a tumor image segmentation method and system of a multi-scale feature fusion network based on boundary enhancement, and relates to the technical field of image segmentation, and the method comprises the steps: obtaining a to-be-segmented tumor image; constructing a tumor image segmentation model based on a pyramid visual converter PVTv2 backbone network; training the tumor image segmentation model through the to-be-segmented tumor image and the known tumor image to obtain an optimal tumor image segmentation model; and acquiring a real-time to-be-segmented tumor image and inputting the real-time to-be-segmented tumor image into the optimal tumor image segmentation model to obtain a tumor image segmentation result. Aiming at the endoscope image segmentation of the kidney tumor, the kidney tumor in the endoscope image is efficiently, stably and automatically segmented through the boundary-enhanced multi-scale feature fusion network, clinical doctors are helped to provide accurate tumor area positioning in endoscopy and surgical operations, and compared with the most advanced method, the method has the advantages that the accuracy is high, and the efficiency is high. According to the method, better segmentation capability and stronger generalization capability are obtained.

The invention relates to the technical field of image focus detection, in particular to a fiber endoscope image focus detection method and system, and the method comprises the following steps: providing a data quality guide interface; capturing narrow-band light images of the endoscope away from a first preset position and a second preset position of the intestinal wall in the axial movement process, calculating brightness gain values of a blue light channel and a green light channel, calculating an asymmetric scattering correction coefficient, and updating indication of color information calibration integrity; tracking the pixel moving speed of the interferent in the view in the propulsion operation process, calculating the relative depth of the interferent, and updating the indication of the validity of the depth data of the interference layer; identifying the image area which is not shielded by the interferent, and updating the indication of the information coverage degree of the background area; triggering an image synthesis operation; performing color compensation on the image information of the image area which is stored in the background canvas and is not shielded by the interferent; reconstructing a clear image; and performing focus detection on the clear image. The method improves the accuracy of focus detection.

The invention discloses an endoscope image processing method and system for improving image quality, and relates to the technical field of image processing.The method comprises the steps that a highlight reflection area of an endoscope image is extracted by setting a detection and extraction mechanism of the highlight reflection area; the method comprises the following steps: dividing an image of which a highlight reflection region is extracted into a base layer and a detail layer, performing global illumination adjustment on the base layer, performing detail enhancement on the detail layer, and fusing the enhanced base layer and detail layer to obtain a pre-enhanced image; if the pre-enhanced image extracts an over-high-brightness area, repairing a high-brightness reflection area and outputting the high-brightness reflection area; according to the method, global enhancement of the endoscope image is realized through highlight reflection area detection, image hierarchical processing and highlight reflection area repair, and the visibility and availability of the endoscope image are improved.

The invention provides a real-time positioning and puncture track control method and system in a breast duct endoscope operation, and relates to the technical field of medical instruments, and the method comprises the following steps: establishing a real-time coordinate system by collecting an endoscopic image sequence and electromagnetic position data, obtaining lumen three-dimensional depth information by using a light field reconstruction algorithm, and adjusting and calculating stress field distribution by using a grid form. And determining an optimal track based on the puncture safety coefficient, and adjusting compensation parameters in real time by using the optical fiber strain sensing array. Accurate positioning and track control of the puncture instrument in the breast duct endoscope operation are achieved, and operation safety and accuracy are improved.

The invention belongs to the technical field of medical equipment. The endoscopic surgery operation quality real-time quantitative evaluation system comprises an endoscopic camera probe used for collecting endoscopic images and an endoscopic handle used for operation. The displacement sensor is integrated on the endoscope camera probe and is used for detecting the endoscope entering speed; the pressure sensor is integrated on the gas injection pipeline and is used for detecting the intracavity pressure; the force sensor is integrated on the endoscope handle and is used for detecting the tissue traction deformation; the optical coherence layer scanning probe is integrated on the endoscope camera probe and used for detecting the thickness of the submucous membrane, and the processing terminal is used for conducting real-time quantitative evaluation on the endoscopic surgery operation quality according to the endoscope entering speed, the intracavity pressure, the thickness of the submucous membrane, the tissue traction deformation image, the included angle between an instrument and a focus normal and the complication response time. According to the invention, full-process datamation real-time accurate evaluation of operation skills and operation safety of operators is realized.

The invention relates to the technical field of medical image processing, in particular to a digestive endoscopy image deblurring enhancement method and system.The method comprises the steps that firstly, an input digestive endoscopy original image is processed through a blurred region classification network, and a pixel-level blurred classification map capable of distinguishing an adhesion blurred region and a motion blurred region is generated; then, parallel processing is carried out according to the classification graph: for an adhesion fuzzy region, physical model restoration and color correction are carried out by estimating a transmissivity graph and an ambient light value; for a motion blur region, a self-adaptive non-blind deconvolution kernel is constructed to perform deconvolution sharpness. And finally, inputting the two processing results and the original clear area into a multi-scale feature fusion network together, carrying out adaptive feature weighted fusion and image reconstruction, and outputting a globally clear and detail-enhanced final image. According to the method, accurate identification and targeted enhancement of composite blurring are realized, and the visual quality and diagnosis availability of the digestive endoscopy image are effectively improved.

The invention relates to the technical field of neurosurgical intelligent operations, and discloses a neuroendoscopic surgery intelligent puncture positioning system based on image navigation, and the system comprises a preoperative modeling module which constructs a preoperative tissue model and a corresponding elastic tensor field based on a three-dimensional medical image of a patient; the image fusion module is used for receiving the data of the preoperative modeling module, collecting intraoperative endoscopic images, predicting the tensor disturbance of tissues through a deep learning model, and feeding back the updated tensor disturbance to the tensor updating module; and the tensor updating module is in two-way communication with the image fusion module and is used for superposing tensor disturbance obtained by processing the intraoperative image with the preoperative tensor. Through fusion of an image navigation technology and preoperative modeling, a three-dimensional positioning frame based on neuroendoscopy operation requirements is constructed, intelligent planning and real-time correction of a puncture path are realized, important brain regions and vascular structures are effectively avoided, intra-operative risks are reduced, and positioning precision and safety are improved.

A multispectral endoscope image intelligent fusion method relates to the technical field of medical image processing, and mainly comprises the following steps: directionally enhancing images in different channels; determining a target of image fusion network design; on the basis of a U-Net network architecture, a multi-expert encoder, a medical priori enhanced cross-channel attention mechanism, a focus adaptive spectral attention decoder and a physically driven reflectivity-illumination decoupling module are introduced, and a deep learning fusion network architecture with pathological interpretation is constructed; jointly optimizing image quality and medical priori knowledge to construct a loss function; a plurality of groups of four-channel endoscope images are collected, and staged progressive training and optimization are carried out. The method has multispectral feature fusion capability and physical interpretability, cross-scale fusion of deep blood vessels and mucous membrane textures can be achieved through a cross attention mechanism, and the visual identification degree and diagnosis specificity of early lesions are remarkably improved.

The invention provides a feature point matching method for digestive tract capsule endoscope images. The method comprises the following steps: after preprocessing, converting each image into a grayscale image, sequentially rotating each grayscale image from an initial angle according to different angles, carrying out feature point identification on the image after each rotation, and taking a subsequent feature point set with the maximum number of feature points as a feature point set; feature point matching is carried out on the feature point sets of the two adjacent frames of grayscale images, and part of matched feature points in the matched feature point sets are removed according to feature point position threshold conditions, feature point density threshold conditions and feature point brightness threshold conditions; and according to a geometric consistency condition and a confidence threshold condition, removing a part of matched feature points in the preliminary matched feature point set to obtain a final matched feature point set. According to the method, more feature points can be obtained, the distribution uniformity of the feature points is improved, and the quality of the feature points is improved.

The invention discloses a capsule endoscopic image super-resolution reconstruction method based on deep learning. The method comprises the steps of designing a data enhancement and label mechanism for pollution shielding; optimizing the ESRGAN model in combination with a lightweight structure and an attention mechanism; generating a structure sensing label; and training the model. The invention provides an image super-resolution reconstruction method fused with medical structure perception in order to solve the problems that a capsule endoscopic image is low in resolution, serious in pollution and shielding, difficult in structural detail recognition and the like in an actual clinical environment. According to the method, a pollution simulation data enhancement mechanism and a structural mask label system are constructed, so that the attention capability of the model on medical key information is effectively improved; a super-division network architecture combining lightweight improvement and attention mechanism optimization is adopted, so that the reconstruction quality is ensured, and meanwhile, the computing resource demand is greatly reduced; and a structure weighting loss function and an edge perception loss strategy are designed, so that the visual performance of the focus area is obviously enhanced.

The invention discloses a digital image enhancement method and system based on an endoscope, and aims to solve the problem of imaging limitation of a traditional endoscope in multi-focus positioning and targeted intervention. According to the digital image enhancement method based on the endoscope, endoscope image acquisition, wireless transmission, abnormal anchor point identification and controllable terminal targeted intervention operation can be carried out. The enhancement system for realizing the digital image enhancement method based on the endoscope identifies a suspected lesion point through image analysis, the control unit sends a release instruction to enable the endoscope to release the controllable terminal and establish communication, the scheduling system navigates the controllable terminal to an abnormal anchor point according to position information, and the abnormal anchor point is sent to the control unit. A focus image after digital image enhancement is collected and transmitted, and a judgment unit analyzes whether a target focus with an intervention value exists or not. According to the method, the digital image in the human body cavity can be enhanced, and the focus recognition accuracy and intervention efficiency are improved.

The invention provides a real-time processing method for endoscopic image blood vessel enhancement and microcirculation evaluation, which comprises the following steps of: processing an original endoscopic image, and locally enhancing a brightness channel through a contrast-limited adaptive histogram equalization technology; self-adaptive frequency domain-space domain decomposition is realized based on local texture complexity analysis; performing multi-scale Hessian matrix blood vessel detection on the low-frequency component; applying a directional Gabor filter bank to the high-frequency component; spatial-temporal feature fusion is realized through multi-resolution pyramid optical flow calculation; synchronously completing blood vessel probability prediction, blood vessel diameter estimation and blood flow direction prediction by using a lightweight multi-task deep learning network; the blood flow velocity is analyzed and calculated based on a speckle mode, the perfusion density is subjected to accelerated statistics through an integrogram, and vascular morphological parameters are extracted by adopting an improved skeleton algorithm. According to the invention, an enhanced blood vessel visualization effect and a real-time microcirculation quantitative evaluation function can be provided, and the overall improvement of the endoscope image processing quality and efficiency is realized.

The invention relates to the field of endoscope image processing, and particularly discloses an automatic lesion recognition system for endoscopy of the digestive system department, which is characterized in that after a preprocessed original endoscope image is acquired, a double-branch parallel processing architecture is used to acquire characteristics with low resolution and rich semantic information through a deep context branch, and the characteristics of the original endoscope image are acquired. The potential area of the focus is accurately deduced; meanwhile, the fine texture of the mucous membrane is captured in a lossless manner through shallow detail branches which keep high resolution in the whole process. Furthermore, through a context-guided asymmetric enhancement mechanism, a global view of a deep branch is utilized to generate an uncertainty perception attention map as a reference, and weak detail features corresponding to a potential focus area in a shallow branch are accurately irradiated and adaptively enhanced. Thus, a conservative enhancement strategy is adopted in an uncertain focus area, background noise is effectively inhibited, and therefore the detection sensitivity and robustness of low-contrast and flat focuses are fundamentally improved.

The invention relates to a tracheal intubation navigation method and device based on image segmentation and three-dimensional reconstruction, and belongs to the field of wisdom medicines.The tracheal intubation navigation method comprises the steps that an endoscope image frame sequence in the intubation process is collected, and image segmentation is conducted on the endoscope image frame sequence to obtain an intubation head segmentation profile diagram; the method comprises the following steps: performing three-dimensional reconstruction on the interior of a trachea by using an endoscope image frame sequence and adopting three network branches of monocular scene three-dimensional reconstruction, inter-frame multi-view geometric pose estimation and optical flow matching three-dimensional reconstruction to obtain a three-dimensional point cloud model of the interior of the trachea; performing pose estimation on the head of the trachea cannula through the segmentation contour of the instrument at the head of the trachea cannula in the endoscope image to obtain a three-dimensional point cloud model of the head of the trachea cannula and instrument estimation pose sequence information; and inputting the trachea internal three-dimensional point cloud model, the intubation head three-dimensional point cloud model and instrument estimation pose sequence information into a Unreal Engine virtual engine to generate a digital twin dynamic navigation image in the intubation process.

The invention discloses an ultra-wide-spectrum endoscopic imaging device capable of being used for short-wave infrared fluorescence imaging, which is used for in-vivo minimally invasive imaging and comprises an illumination module, a wide-spectrum image transmission module, an imaging module and an image processing module. The illumination module consists of a light source and a light guide optical fiber, and is used for generating illumination light from ultraviolet to short wave infrared band and transmitting the illumination light to the wide-spectrum image transmission module; the wide-spectrum image transmission module consists of a few-interface optical imaging channel and a light guide illumination channel, and is used for illuminating an imaging target, exciting target fluorescence and collecting image signals from ultraviolet to short-wave infrared bands; the imaging module is composed of an optical filter and an imaging camera, and the imaging camera can be a wide spectral response image sensor or can be formed by combining cameras working in different wavebands and is used for converting optical images in different wavebands into image data; and the image processing module is used for registering and fusing image data of different wavebands in real time and displaying the image data.

The invention discloses a multi-photon optical fiber array endoscopic imaging device and method based on hollow spiral scanning, and belongs to the technical field of biological imaging. The device comprises a laser collimation and beam expansion module, a pulse chirp module, a light beam switching module, an endoscopic detection module and a fluorescence acquisition module. The light beam switching module comprises an optical switch and an optical fiber array composed of at least two low-group-velocity dispersion single-mode optical fibers. The endoscopic detection module drives the optical fiber array to execute hollow spiral scanning through the resonance driver, the track starting point is not located at the view field original point, and central dense sampling is avoided. According to the method, the optical fibers are switched through the optical switch, so that the optical fibers execute spatial non-coincident hollow spiral scanning, multi-photon signals such as fluorescence, second harmonic and fluorescence lifetime are acquired, a central hole is complemented through image reconstruction and fusion, and a complete field imaging result is obtained. Light damage of the central area is remarkably reduced, the imaging resolution uniformity is improved, the fused image is true and reliable based on physical sampling, and the system stability is high.

The invention discloses an image ranging system and method for a bronchus endoscope, and belongs to the field of image processing. The system mainly comprises an endoscope image acquisition module, an image preprocessing module, an image ranging module, a training module, a distance verification module and a data output module. The image preprocessing module performs size adjustment and distortion elimination on the acquired image data and integrates the image data into a data set; the image ranging module constructs a ranging model by using a convolutional neural network, inputs a data set into the stable ranging model trained by the training module to obtain a predicted value of a distance, and finally verifies and outputs a ranging result; the image ranging system adopts a small convolution kernel, a small number of convolution layers and image input of a specific size, is higher in fitting degree, lower in storage requirement and higher in calculation efficiency for a specified scene, can serve as a judgment condition for bending of the front end of a catheter, provides a research basis for a bronchoscope interventional operation path, and has a good application prospect. The accuracy of clinical diagnosis and treatment is improved.

The invention relates to an early cancerization risk early warning method and device based on multiband image fusion. The method comprises the following steps: respectively acquiring N endoscopic images shot at the same time at the same position under different band light sources; for a light source of any wave band, performing image fusion on the N endoscopic images of the light source to obtain a fusion representation image; registering and preprocessing the fused representation images corresponding to all the waveband light sources to obtain processed fused representation images corresponding to all the waveband light sources; inputting the processed fusion representation images corresponding to all the waveband light sources into a pre-trained deep learning model, and obtaining an output result of the deep learning model, namely a multi-waveband fusion image; and carrying out visual display on the multi-band fusion image. And obtaining an early canceration risk analysis result for the multi-band fusion image, and performing early warning when an early canceration risk exists. According to the invention, the accuracy of early canceration risk early warning results can be improved based on multiband endoscope image fusion.

In an endoscopy support device, a display control means superimposes and displays a lesion guide for specifying a lesion in an endoscopic image. An acquisition means acquires the endoscopic image. An analysis means segments a lesion region from the endoscopic image based on the endoscopic image and the lesion guide. An output means outputs the lesion region segmented. The endoscopy support device can support decision making by a user in the field of healthcare.

The invention relates to the technical field of endoscopes, and provides a spectral image processing method for a three-dimensional endoscope. The method comprises the following steps: synchronously acquiring single-band spectral images returned by multiple sensors of the endoscope; after the single-band spectral image is corrected, a two-dimensional spectral image is generated by combining multi-level fusion of the spectral weight; constructing a three-dimensional point cloud coordinate set through parallax calculation; mapping the two-dimensional spectrum fusion image to a three-dimensional point cloud based on a back projection sub-pixel mapping algorithm to obtain a three-dimensional spectrum point cloud; and performing Poisson fusion driven inter-block splicing fusion on the three-dimensional spectrum point cloud, and outputting an interactive three-dimensional navigation model. The technical problem that image details are lost and three-dimensional reconstruction is inaccurate due to the fact that spectral image fusion precision is insufficient in an existing three-dimensional endoscope image processing method is solved, registration and reconstruction of high-precision three-dimensional spectral point clouds are achieved through multi-modal image collaborative correction and optimization fusion, and the image fusion precision is improved. And the accuracy and the real-time performance of navigation in the endoscope are improved.

The invention discloses an ear-nose-throat endoscope wireless image transmission system based on NFC near field triggering, relates to the field of medical apparatuses and instruments, and solves the problem that an existing wireless image transmission system is poor in image acquisition and transmission efficiency. The data acquisition module is used for performing cavity cross-sectional area analysis according to an image intercepted by an endoscope to obtain target endoscopic video analysis data, screening disease matching patients according to patient symptoms and obtaining corresponding endoscopic video analysis data to obtain endoscopic patient initial matching data; the data analysis module is used for analyzing the cross sectional area of the endoscopic video stream and the endoscopic depth of a lens to obtain endoscopic cavity similarity and performing type division on the endoscopic video stream of the matched patient according to the endoscopic cavity similarity to obtain video stream matching data; and the image transmission module is used for carrying out endoscope near-field triggering setting and endoscopic image wireless transmission on the target endoscopic patient according to the video stream matching data. According to the system and the method, the collection efficiency and the transmission efficiency of the endoscopic image can be improved.

The invention relates to the field of medical artificial intelligence, in particular to a laryngeal cancer early intelligent diagnosis system and method based on multi-modal deep learning, and the system comprises a multi-modal data collection module, a cross-modal feature extraction module, a cross-modal attention network module, an expert-level knowledge distillation network module, and a focus evolution prediction and diagnosis decision and visualization module. Endoscope images, acoustic features and clinical data are collected, a system extracts high-dimensional feature vectors, a cross-modal attention network is used for feature fusion, an expert-level knowledge distillation network is combined with pathology and expert experience, neural network learning is guided, a lesion evolution prediction module tracks lesion changes, risk prediction is generated, and finally, the lesion evolution prediction module is used for predicting the lesion change. And the diagnosis decision and visualization module generates a diagnosis result and explanation. The system improves the early detection rate of laryngeal cancer through multi-modal data fusion.

The invention discloses an endoscope image depth estimation method based on deep learning. According to the method, a registration module is introduced to assist in appearance flow network training; the registration module predicts an inter-frame optical flow through an optical flow network OFNet and executes an automatic registration step; the registration module also generates a visibility mask, and filters pixel points that are occluded or beyond the field of view. Through an improved self-supervision framework, the OFNet does not need a real optical flow label, and learns high-quality motion information, so that adjacent frames are aligned, the influence caused by the motion of an endoscope camera is reduced, the appearance flow estimation precision is improved, complex brightness changes in an endoscope environment can be effectively coped, and more accurate depth and attitude estimation is provided for endoscope images. By estimating the forward optical flow and the backward optical flow, the estimation of the appearance flow network is assisted, so that the estimation precision of the appearance flow network is improved, and the accuracy of final endoscope image depth estimation is improved.

The invention discloses an endoscope image processing method based on oxyhemoglobin saturation and multiple central wavelengths, and relates to the field of medical image processing.The method comprises the steps that a first initial image signal, a second initial image signal, a third initial image signal and a fourth initial image signal are obtained; calculating oxyhemoglobin concentration information of the target part according to the second initial image signal and the third initial image signal; calculating deoxidized hemoglobin concentration information of the target part according to the second initial image signal and the fourth initial image signal; calculating the oxyhemoglobin saturation of the target part according to the oxyhemoglobin concentration information and the deoxidized hemoglobin concentration information; performing brightness enhancement on the second initial image signal based on the oxyhemoglobin saturation to obtain a second enhanced image signal; and generating a target image according to the second enhanced image signal, the first initial image signal and the third initial image signal, wherein the target image is used for identifying the bleeding position of the target part.

The present invention relates to an endoscopic probe for photoacoustic tomography of cavity side walls, characterized by comprising a hollow tubular probe housing with a closed front end; N acoustic wave receiving windows and M laser output windows formed on the side wall of the tubular probe housing; an array of linear laser output ports connected to a pluggable optical fiber connector via an optical fiber bundle; and an ultrasonic transducer array connected to a multi-channel signal connector via a signal harness. The present invention employs a method of arranging array transducers and array laser outputs on the side wall of the probe, eliminating the need for a reflector. The probe is less than 2 centimeters in size and can be placed in close proximity to the prostate through the rectum for photoacoustic imaging of the prostate, or in close proximity to the inner wall of the rectum for photoacoustic imaging of the inner wall of the rectum. The endoscopic probe provided by the present invention, combined with a photoacoustic imaging system host, utilizes photoacoustic endoscopic imaging technology to perform real-time transrectal prostate imaging and rectal imaging, achieving ideal imaging results.