496 results about "Automated segmentation" patented technology

The invention discloses a cervical cancer close-range radiotherapy high-risk target area sketching method fused with a multi-modal image. The method comprises the following steps of collecting an MRI image scanned before radiotherapy and a CT image during radiotherapy of a cervical cancer close-range radiotherapy patient and annotation data of the MRI image and the CT image; the method comprises the following steps: preprocessing an MRI image scanned before radiotherapy and a CT image during radiotherapy, and converting annotation data into a three-dimensional tag image; on the basis of the preprocessed MRI image, the preprocessed CT image and the three-dimensional label image of the preprocessed MRI image, the preprocessed CT image and the three-dimensional label image of the preprocessed MRI image, registration of the MRI image and the CT image is conducted through a pre-constructed registration neural network model, feature extraction and fusion are conducted on the registered MRI image and the registered CT image through a pre-constructed high-risk target area automatic segmentation neural network model of a multi-scale cross-modal attention mechanism, and the high-risk target area automatic segmentation neural network model of the multi-scale cross-modal attention mechanism is obtained. Automatic delineation of a cervical cancer close-range radiotherapy high-risk target area is realized; according to the method, automatic segmentation of HR-CTV in close-range radiotherapy of cervical cancer is realized, high efficiency, accuracy and generalizability are realized, and intelligent support can be provided for clinical work.

The invention relates to the technical field of medical image processing, and particularly discloses a medical image segmentation method with adaptive receptive field and feature correction, which comprises the following steps: (1) acquiring an original medical image and a segmentation label thereof, and constructing a training and testing data set; (2) carrying out size normalization and enhancement processing on the image; (3) establishing an improved U-shaped encoder-decoder segmentation network, introducing an adaptive branch mixed shape convolution module in a shallow layer, and improving edge and texture feature modeling capability by adopting a multi-branch banded convolution and channel attention mechanism; (4) a residual directional feature interaction module is introduced into a deep layer, a spatial dependency relationship is modeled through an information interaction structure in the horizontal and vertical directions, and the direction sensing ability of the heterostructure is enhanced; and (5) completing network training and reasoning, and outputting a segmentation result. The method gives consideration to the calculation efficiency and the segmentation precision, and is suitable for the automatic segmentation task of various types of medical images with complex structures.

The invention relates to the technical field of image segmentation, in particular to an automatic segmentation method and system for an echocardiogram of the department of cardiology, and the method comprises the following steps: based on image data of the echocardiogram, extracting gray level distribution, edge feature and texture feature information, analyzing gray level change amplitude, screening gray level change abnormal regions, and recognizing connectivity features. According to the method, the segmentation accuracy is effectively improved by extracting image gray, edge and texture features and identifying abnormal regions, noise and artifact interference are reduced by optimizing low connectivity regions, and the segmentation accuracy is improved. A problem area is analyzed and positioned in combination with multi-frame gray level change, a segmentation result is adjusted, the processing stability and consistency are enhanced, meanwhile, an optimized alarm node is output based on a frequency trend, more accurate and stable heart image analysis is supported, and the clinical application practicability is improved.

The invention discloses a urinary calculus CT image automatic segmentation method based on deep learning, particularly relates to the technical field of medical image processing, and is used for solving the problem of low geometric fidelity of a segmentation result caused by hardening artifacts when an existing deep learning segmentation method is used for processing a high-density urinary calculus CT image. The method comprises the following steps: acquiring a urinary calculus CT image, performing initial segmentation by using a deep learning model to generate an initial calculus segmentation region, evaluating texture heterogeneity degree and identifying a hardening artifact risk region by analyzing feature value distribution of a structure tensor field, and positioning an artifact-causing source point based on a CT imaging projection geometric principle by reversely tracing a spatial position relation. According to the method, boundary distortion features are identified by analyzing CT value profile curve form distortion features and local boundary curvature singularity features, geometric correction is performed on corresponding boundaries in an initial stone segmentation region according to the boundary distortion features, a final stone segmentation region is obtained, and the geometric accuracy and reliability of a segmentation result are effectively improved.

The invention discloses an abdominal multi-organ CT image automatic segmentation method based on deep learning. The method comprises the following steps: establishing a training sample set; constructing an improved encoder; an improved decoder is constructed; a PCE-TransUNet segmentation network model is established, and the PCE-TransUNet segmentation network model is Training the PCE-TransUNet segmentation network model by using the training set, and optimizing by using a joint loss function of cross entropy loss and Dice loss to obtain a trained PCE-TransUNet model; and inputting the test set into the trained PCE-TransUNet model, and outputting a segmented image by the PCE-TransUNet model. According to the method, partial convolution and an efficient channel attention mechanism are introduced, the ability of the model to extract image details is enhanced, the problem that feature extraction is insufficient in a traditional method is solved, and especially when small organs and complex boundaries are processed, the segmentation precision is remarkably improved.

The invention discloses a swallowing function evaluation method and system based on tongue motion nuclear magnetic image registration, and belongs to the technical field of medical image processing. The system comprises a time sequence nuclear magnetic image reading module, a tongue body dynamic mask generation module, a tongue body dynamic image extraction module, a dynamic tongue body sequence registration module and a patient swallowing recovery evaluation module. Through an automatic segmentation and image registration technology, the system can accurately extract tongue motion displacement and calculate a local displacement vector, and evaluate the swallowing function recovery condition of a tongue cancer postoperative patient. The method comprises the steps of image reading, image screening, data set making, model training and tongue swallowing recovery evaluation, and the system can identify a tongue motion abnormal mode and provide an objective and quantitative evaluation result. According to the method, the accuracy and efficiency of tongue motion feature analysis are remarkably improved, and the method has a wide clinical application prospect.

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.

According to various examples of the present disclosure, there is provided a machine learning image segmentation model for automatically identifying and segmenting structural features of a vessel tree from image frames. The model comprises a 3D encoder and 2D decoder, the 3D encoder and 2D decoder connected by at least one interlinked convolution node and a plurality of temporal extraction nodes therebetween. The model is configured to identify and segment structural features of a vessel tree from the plurality of image frames, by the model being configured to: receive a plurality of image frames as an input to the 3D encoder; provide an output of the 3D encoder as an input to the plurality of temporal extraction nodes to extract temporal information; generate, by the plurality of temporal extraction nodes, a 2D temporal output based on the extracted temporal information; provide the generated 2D temporal output to the at least one interlinked 2D convolution node and 2D decoder; generate, at the 2D decoder, a combined temporal output based on an output of the at least one interlinked 2D convolution node and at least one temporal extraction node, wherein the combined temporal output represents a predicted segmentation of the vessel; and generate an output representative of the segmented structural features of the vessel tree based on the predicted segmentation.

The invention belongs to the technical field of image recognition, and discloses a pelvic medical image automatic segmentation model based on global-local feature optimization. According to the model, an efficient non-local attention mechanism and a double attention mechanism are combined, global anatomical structure understanding and local fracture feature extraction are collaboratively optimized, and a three-level optimization strategy is adopted to achieve collaboration of anatomical constraint and pathological response. Specifically, the efficient non-local attention mechanism can enhance the global perception ability of the model and help the model to better understand a complex pelvic anatomical structure; and the double attention mechanism is helpful for the model to pay attention to details of a local area, such as changes of a fracture edge and a lesion area, so that the segmentation precision is improved. Meanwhile, according to the scheme, a mixed loss function combining label distribution perception loss and a surface supervision strategy is provided, the segmentation precision of an edge region is optimized through a guide network, the probability of missing detection and misjudgment is reduced, and then the robustness of the model in a complex pathological state is enhanced.

The invention provides a cervical cancer MRI image automatic segmentation method based on multi-modal fusion, and the method comprises the following steps: S1, obtaining tumor segmentation mask images of three modals T1c, T2 and DWI of a cervical cancer MRI image through an automatic prompt segmentation method, and carrying out the cross-modal attention interaction of a task token and an output token, dynamic alignment of medical image features and segmentation tasks is achieved, and segmentation mask images of all modes are generated in combination with sparse and dense prompts; and S2, performing registration and feature extraction on the segmentation mask images of the three modes to obtain a structured medical description of the segmentation mask image of each mode, and performing fusion processing on the structured medical descriptions by using a large language model to obtain a tumor segmentation mask image after multi-mode fusion. According to the method, the segmentation mask image of the MRI multi-mode image is fused by introducing the large language model, and the fused tumor segmentation mask image is finally obtained by combining the segmentation characteristics of different modes, so that the segmentation precision of the MRI image is greatly improved.

The invention provides a breast cancer recurrence risk prediction method, system and device based on an ultrasonic image, and relates to the field of intelligent medical treatment, the method uses a deep convolutional neural network to perform deep network feature extraction on a breast ultrasonic image, and uses a deep learning semantic segmentation algorithm to perform accurate positioning and automatic segmentation on a breast tumor region of interest, thereby improving the accuracy of breast cancer recurrence risk prediction. Meanwhile, habitat analysis is carried out on the ultrasonic images to extract tumor heterogeneity features, multi-level and multi-mode features such as deep learning features, radiomics features and habitat analysis features are fused, a breast cancer recurrence risk prediction model is constructed, breast cancer recurrence risk prediction is carried out, and a breast cancer recurrence risk assessment result is output. And a quantitative basis is provided for clinical treatment decisions. The accuracy and robustness of recurrence risk prediction are remarkably improved through multi-feature fusion, and standardization and objectification of breast cancer prognosis evaluation are achieved. In addition, the method further has the advantages of being easy and convenient to operate, low in cost, noninvasive, nonradiative, good in repeatability and the like.

The invention discloses a meniscus injury prediction method and system based on a neural network, and the method comprises the steps: obtaining a knee MRI image, carrying out the automatic segmentation of the knee MRI image based on a convolutional neural network, extracting a plurality of image features based on the segmented image, and obtaining a meniscus injury prediction result. And constructing a meniscus damage prediction model based on the SNN network model, and carrying out model training and verification. The meniscus injury diagnosis accuracy and diagnosis efficiency are improved, future injury risk prediction is achieved, valuable prediction information is provided for clinicians, prevention and treatment schemes are helped to be formulated, meanwhile, radiomics feature and load structure feature heat maps and a mixed attention mechanism are introduced, the clinical interpretability is enhanced, and the diagnosis accuracy and efficiency of meniscus injury are improved. And a full-automatic diagnosis process is realized.

The invention relates to the technical field of concrete structure damage detection, in particular to a post-fire RC structure burst damage three-dimensional segmentation method, which comprises the following steps: firstly, determining the type and three-dimensional characteristics of post-fire RC structure member surface concrete burst damage, and constructing a corresponding three-dimensional point cloud data set for network training and verification; and then based on a KP-FCNN network structure, a KPConv layer is improved and optimized, so that the detection and segmentation precision is improved, the model size is reduced, the reasoning time is remarkably shortened, the optimal segmentation precision of 82.3% is achieved under the working conditions of different damage degrees, automatic damage segmentation of the burst damage of the concrete structure after the fire disaster is realized, and the automatic damage segmentation of the burst damage of the concrete structure after the fire disaster is realized. And technical support is provided for subsequent damage three-dimensional quantification and deployment to an unmanned aerial vehicle system.

The invention discloses a handwritten element automatic segmentation and extraction method for a complex layout, relates to the technical field of handwritten element automatic segmentation and extraction, and aims to solve the technical problem that the recognition and separation precision of handwritten contents in a mixed image-text layout is insufficient. S201, a dynamic threshold segmentation algorithm is carried out; s202, context sensing connected domain analysis is carried out; s203, judging whether the elements are handwritten elements or not; s204, if the judgment result is yes, the handwriting region candidate is reserved; and S205, if not, filtering and eliminating. According to the method, the dynamic threshold segmentation algorithm and the context sensing connected domain analysis technology are cooperated, the segmentation threshold can be adaptively adjusted according to the pixel mean value and the standard deviation of the image local window through dynamic threshold segmentation, and the context sensing connected domain analysis is combined with the context information of the document to perform semantic analysis on the connected domain. The problem that the recognition and separation precision of the handwritten content in the mixed image-text layout is insufficient is solved.

The invention relates to the technical field of image segmentation, in particular to an anorectal focus automatic segmentation method based on deep learning, which comprises the following steps: acquiring an anorectal image pixel map, extracting contrast and direction offset to mark candidate focus points, screening overlapped marks to generate a focus activation mark map, and establishing a response map to generate a boundary response distribution map. And training the network to output a classification graph, and extracting a truncation path to complete image segmentation. According to the invention, through extracting the contrast value and the gradient amplitude of the local gray level co-occurrence matrix, accurate capturing of the spatial difference of the lesion area under a complex background is realized, through constructing a response map and direction consistency comparison mechanism and combining multi-dimensional features such as a direction gradient histogram and a structure tensor, the area discrimination capability and the edge classification precision are improved, and the accuracy of edge classification is improved. The texture stability is judged by means of anisotropic standard deviation, a fuzzy edge mask is set, truncation paths are screened in combination with a main direction vector included angle deviation trend, and continuity and stability of a boundary convergence position are ensured.

The invention provides an interactive pavement disease segmentation method based on a large model, and the method comprises the steps: constructing pavement disease segmentation models which comprise a disease detection network model, a disease initial segmentation network model and a disease segmentation large model, carrying out the offline training of the disease detection network model and the disease initial segmentation network model, and obtaining a disease segmentation model; performing fine adjustment on the disease segmentation large model; inputting the disease image into a pavement disease segmentation model, and detecting a disease area by using a disease detection network model based on a deep learning algorithm; based on a deep learning algorithm, performing disease initial segmentation on the disease areas by using the disease initial segmentation network model to obtain a disease initial segmentation result corresponding to each disease area, and automatically obtaining a disease positive prompt point and a disease negative prompt point; performing interactive correction on the automatically obtained disease positive prompt point and negative prompt point, and updating the positive prompt point, the negative prompt point and the frame; and based on the updated positive and negative prompt points and the frame, automatically segmenting the disease by using a disease segmentation large model to obtain a final segmentation mask.

The invention discloses a Crohn disease focus automatic segmentation and activity evaluation system based on deep learning, which belongs to the field of medical artificial intelligence and comprises a data preprocessing unit, a focus automatic segmentation unit, a radiomics feature extraction unit, a feature screening and dimension reduction unit and an activity classification unit. According to the method, an nnU-Net deep learning segmentation model is combined with image omics feature extraction, multi-stage feature screening and machine learning classification technologies, so that full-process automation from CTE image preprocessing, focus automatic segmentation, feature extraction and screening to activity classification is realized. The system can efficiently and accurately segment the focus of Crohn's disease, automatically assesses the disease activity based on the screened key radiomics characteristics, significantly improves the consistency, objectivity and efficiency of diagnosis, and is suitable for clinical auxiliary diagnosis and scientific research analysis.

The invention discloses a pig organ automatic segmentation method and segmentation system based on a CT anatomical structure relationship. The method comprises the following steps: acquiring a whole-body CT scanning image of a live pig; and processing the pig whole body CT scanning image by using a pig multi-organ automatic segmentation model to obtain a pig multi-organ prediction mask, and completing automatic segmentation of the multiple organs of the pig. The live pig multi-organ automatic segmentation system comprises a live pig CT image acquisition module and a multi-organ automatic segmentation module, wherein the live pig CT image acquisition module is used for acquiring a to-be-segmented live pig CT image; the multi-organ automatic segmentation module comprises a visual state space block-based encoder-decoder architecture, a spatial link GRU module, a global organ category encoding module, a global organ category guiding module and a semi-supervised training framework. According to the method, multiple organs in the CT image of the live pig can be segmented efficiently and accurately, the problems of low segmentation speed and low precision of a traditional method are solved, and a more efficient solution is provided for medical image analysis and animal husbandry management of the pig.

The invention provides an endoscopic surgery video multi-target segmentation method based on a basic segmentation large model and mixed expert fine tuning. The method comprises the following steps: preprocessing a data set, establishing an SAM2 baseline segmentation network, constructing a hierarchical hybrid expert module, constructing a stage gating network, establishing an endoscopic surgery video segmentation network, training the endoscopic surgery video segmentation network, and automatically segmenting an endoscopic surgery video by the segmentation network. According to the invention, accurate segmentation of different operation scenes can be realized. The hierarchical hybrid expert module and the stage gating network are introduced, and the problems of scene difference and multi-organization segmentation commonly existing in endoscopic surgery video segmentation tasks can be well solved.

The invention discloses a road surface state recognition method based on machine vision and force feedback. The method comprises the following steps: S1, continuously shooting a front road surface image flow by using a vehicle front-end image acquisition device; s2, automatically segmenting a road surface abnormal region by adopting an arbitrary target segmentation model; s3, arranging a piezoelectric film vibration sensing unit in the contact surface of the vehicle tire and the road surface to collect tiny vibration signals in real time; s4, extracting time domain and frequency domain features of the vibration signals; s5, performing vision and force feedback feature multi-scale coding; s6, dynamically adjusting the weights of the two modal features, and generating fusion decision features; and S7, outputting a road surface state recognition result. The method is used for improving automatic driving safety.

The invention discloses a CCTA image-based full coronary artery tree complete automatic segmentation and blood vessel center line extraction system, and the system comprises a positioning module which is used for carrying out the positioning and extraction of a heart according to a CCTA image; the extraction module is used for performing primary extraction of coronary artery tree segmentation according to the CCTA image after heart positioning to obtain a coronary artery tree; the repairing module is used for extracting the center line of the blood vessel based on the extracted coronary artery tree, and extending the center line at the breakpoint to connect the fractures, or further extending the incomplete center line to the tail end of the blood vessel to obtain a repaired center line; the supplementing module is used for supplementing the coronary artery tree structure and repeating the steps of blood vessel repairing and coronary artery tree supplementing to obtain a coronary artery blood vessel area; and the reconstruction module is used for obtaining a coronary artery tree blood vessel result according to the multiple planes of the reconstructed blood vessel. According to the method, full-automatic accurate extraction of the complete coronary vessel tree can be realized, and technical innovation and clinical application of coronary imaging are promoted.

The invention discloses a deep learning model-based automatic segmentation method for a plain-scan CT image, and the method comprises the steps: carrying out the region segmentation of a brain on the section image data of the brain through employing a cascading Deep-MSVM-UNet model trained in a first stage, extracting a rough brain region contour, and then carrying out the segmentation of the region of the brain through employing a Cascade Deep-MSVM-UNet model, and inputting the segmentation result of the brain region into the Deep-MSVM-UNet model trained in the second stage to carry out fine segmentation on each brain region so as to further accurately divide the boundary and the internal structure of the brain region. Accurate segmentation of the brain region is finally realized through multiple iterations and cascade processing; according to the cascade Deep-MSVM-UNet model constructed by the method, multi-scale visual state space blocks and a UNet framework are combined, multi-scale feature representation can be more effectively captured and aggregated, and meanwhile, the long-time dependency relationship between pixels is simulated, so that the brain region segmentation precision is improved.

The application discloses a kind of nnUNet zebra fish juvenile whole brain vascular system segmentation methods based on autonomous data set training, it is related to high-resolution imaging technology, image processing and medical image segmentation field, the method makes full use of zebra fish live transparency and fluorescent label advantage, obtains high-resolution whole brain three-dimensional vascular image data, and constructs high-quality segmentation truth value database by semi-automatic segmentation and artificial correction, training is carried out using nnU-Net deep learning model, realize the three-dimensional automatic segmentation of zebra fish brain vascular system signal.The application method significantly improves the degree of automation and precision of image segmentation, effectively solves the problems of low efficiency, high artificial dependence and poor repeatability of traditional brain vascular segmentation.The method is suitable for large-scale high-throughput data processing, can provide efficient, standardized image processing scheme for zebra fish brain vascular development mechanism and brain vascular disease model research, and has wide application prospect.

A plain-scan CT image aortic valve calcification segmentation system based on space-time priori comprises a position priori information extraction module, an encoding module and a decoding module, after an aortic image is automatically segmented in an off-line stage to obtain a segmentation mask, attention weights are generated through preprocessing and encoding, and the aortic valve calcification is subjected to image segmentation; and inputting the aorta image serving as a training set into a segmentation model comprising a position prior information extraction module, a coding module and a decoding module, and performing real-time image segmentation through the trained segmentation model in an online stage. According to the method, calcification point features of different sizes and forms are captured through multi-scale prediction; the aorta segmentation prior is introduced, the sensitivity and positioning precision of the model to aortic valve calcification are improved, coronary artery opening sequence time sequence information is introduced, a double-branch structure is adopted to adapt to structural differences, accurate positioning and recognition of aortic valve calcification lesions are achieved, and the false detection rate and the omission ratio are effectively reduced.

The invention relates to organ segmentation quality assurance based on deep learning for medical images. A deep learning-based framework for assessing the quality of automatic segmentation of medical images is described. According to an example, a computer-implemented method includes receiving segmentation masks generated via one or more segmentation models from medical image data depicting an anatomical region of a subject, where each of the segmentation masks depicts a different anatomical structure of a set of different anatomical structures included in the anatomical region. The method further includes generating a reconstructed version of the segmented mask based on applying a multi-channel reconstruction model to the segmented mask, wherein the reconstructed version corresponds to an optimized version of the segmented mask. The method further includes determining a quality assessment of the segmented mask based on comparing the segmented mask to the reconstructed version; generating output data regarding the quality assessment; and presenting the output data via an electronic output device.

The invention discloses a method, a device and equipment for automatically identifying a thrombus proportion. The method comprises the following steps: performing blood vessel segmentation and thrombus segmentation on a medical image to obtain a blood vessel segmentation image and a thrombus segmentation image; extracting a blood vessel center line based on the blood vessel segmentation image, and obtaining spatial position information of a point set on the blood vessel center line; clustering based on the spatial position information of each thrombus voxel in the thrombus segmentation image to form a thrombus clustering center set; and respectively determining a to-be-analyzed point set on a corresponding blood vessel center line for the thrombus clustering center. And calculating the thrombus proportion on the blood vessel section where each point included in each to-be-analyzed point set is located. Automatic segmentation of the blood vessel and the thrombus in the medical image is achieved, the core position of the thrombus is aimed at, a plurality of center points related to the position are determined from the center line of the blood vessel according to the core position, the blood vessel section is conveniently determined, and then the thrombus proportion is obtained. A large amount of time and manpower are not needed to identify, judge and mark the thrombus, and the difficulty of thrombus severity typing is effectively reduced.

A method includes obtaining initial transcription for input natural speech; performing segmentation of initial transcription into text portions, based on punctuation marks in initial transcription; determining segment-level timestamps for text portions based on the input natural speech; performing audio segmentation on input natural speech, by cutting input natural speech based on segment-level timestamps, to obtain audio chunks; generating transcription portions for each of the audio chunks; merging transcription portions to form re-transcription; determining word-level timestamps for re-transcription, by aligning input natural speech against re-transcription; calculating silence time periods, each corresponding to silence between each two adjacent words of input natural speech, based on word-level timestamps; performing a final segmentation on input natural speech and re-transcription, based on silence time periods, to generate final audio segments and corresponding final transcription portions. The final audio segments and corresponding final transcription portions may be included in training dataset for training a model.

The invention discloses a skin lesion image segmentation method based on feature interaction fusion, and relates to the technical field of medical image segmentation. Comprising the following steps: firstly, carrying out size normalization and noise suppression on a dermatoscope image, and expanding a data set through random rotation and overturning operation; inputting the training data into the network model for training to obtain a trained model weight; and finally, inputting a to-be-segmented skin lesion image into the trained network model to realize automatic segmentation of a skin lesion area. According to the invention, a focusing feature interaction fusion module is designed, and deep complementary fusion of CNN and Transform features is realized; a multi-scale context attention module is constructed, multi-scale accurate feature extraction is realized by extracting spatial information from local scale to global scale, and meanwhile, the calculation efficiency is kept. The invention aims to solve the problems of complex shape, variable size, low contrast and the like of the lesion area of the skin lesion image, and improve the segmentation precision and robustness of the skin lesion image.

The invention discloses a pulmonary interstitial image analysis method and system based on clinical prior guidance feature fusion, and the method comprises the steps: obtaining chest CT images of a user at a plurality of time points and corresponding clinical data, and carrying out the data preprocessing and region segmentation; performing feature extraction on the automatically segmented chest CT image and the corresponding clinical data by using specific indexes; specific time points are coded into time embedding vectors, total image feature vectors are projected to the same dimension as the time embedding vectors through a linear layer, and time coding fusion is generated; the most relevant CT image follow-up visit time points are actively'inquired 'and'weighted' by using clinical risk factors, time sequence image feature fusion is carried out, and fusion features after weighted fusion are output; and carrying out progress probability calculation by using the fusion features, predicting the progress risk in the next one year according to the calculation result, and realizing dynamic time sequence feature selection driven by clinical priori knowledge.

This invention discloses a method, apparatus, medium, and equipment for detecting tracheid cavities in transverse sections of coniferous wood, belonging to the field of wood identification technology. Based on an active learning approach, this invention combines generative adversarial networks (GANs) for sparse data sampling and uses the output of a target detection model as a SAM (Self-Assisted Analysis) prompting strategy to achieve automatic segmentation of tracheid cavities in coniferous wood transverse sections and obtain quantitative anatomical data. By optimizing the data sampling process and enhancing the model's adaptability, this method effectively improves the accuracy and efficiency of cavity segmentation and can be adapted to any coniferous wood transverse section microscopic image. It achieves low-cost construction of a coniferous wood microscopic image database and the development of a model for the detection, segmentation, and measurement of coniferous wood tracheid cavities. This solves the problems of difficult manual detection and high collection and annotation costs caused by the complex structure and sampling difficulty of tracheid cavities in coniferous wood transverse sections, enabling automatic and rapid detection, segmentation, and measurement of coniferous wood tracheid cavities.