374 results about "Automated segmentation" patented technology

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 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 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 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 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 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.

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 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.

The invention discloses a quantitative characterization method for a multi-scale gamma'phase of a polycrystalline high-temperature alloy, which comprises the following steps: sequentially carrying out metallographic sample preparation, electrolytic polishing and gamma 'phase in-situ electrolytic etching on a standard high-temperature alloy sample, collecting a microscopic electronic image, and carrying out image processing, multi-scale gamma' phase labeling and data augmentation to obtain a training sample; iteratively training the U-Net convolutional neural network architecture by using the training sample to obtain a multi-scale gamma'phase feature extraction model; and inputting the gamma'phase secondary electron image of the surface of the polycrystalline high-temperature alloy to be detected into the multi-scale gamma 'phase feature extraction model to obtain a binary gamma' phase feature image, and performing statistical distribution characterization to obtain statistical distribution data of each gamma 'phase. According to the method, automatic segmentation identification and quantitative statistics of the multi-scale gamma'phase in the polycrystalline high-temperature alloy are realized by adopting in-situ electrolytic etching, high-flux scanning electron microscope acquisition, image super-resolution processing and a deep learning algorithm, and the speed, the accuracy and the engineering practicability of quantitative analysis of the gamma 'phase are remarkably improved.

The invention relates to the technical field of medical image processing and three-dimensional reconstruction, and particularly discloses a reconstruction and analogue simulation method based on a cardiac image. The method comprises the following steps: firstly, preprocessing an input CT or MRI medical image, and automatically segmenting a heart multi-tissue structure by using an nnUNetv2 model, including a plurality of anatomical regions such as atrium, ventricle, myocardial tissue and blood pool; and then performing topology repair, smoothing processing and label resampling on the segmentation result to obtain a three-dimensional label body with a continuous structure and a complete boundary. And based on the optimized tag body, constructing a heart three-dimensional surface model by adopting a Marching Cubes algorithm, and generating a multi-material volume grid suitable for finite element analysis. According to an electrode position and a radio frequency parameter set by a user, a radio frequency ablation simulation model based on a Pennes biological heat conduction equation and an Arrhenius model is established, a temperature field and a tissue thermal damage range are calculated, and a simulation result is displayed in a three-dimensional mode in an overlapping mode. According to the method, the integrated process from medical image automatic segmentation to heart three-dimensional reconstruction and thermal simulation is realized, the segmentation precision, the modeling efficiency and the simulation reliability are improved, and the method can be applied to application scenes such as surgical planning, preoperative evaluation and medical teaching.

The invention relates to the technical field of medical image processing and deep learning crossing, and discloses a congenital heart disease whole heart segmentation method based on text guidance. According to the method, based on a U-Net architecture, a text-image alignment module is added between different levels of jump connections, and semantic information in report texts and CT image features are subjected to feature semantic alignment and fusion layer by layer; a multi-scale image feature fusion module is added between adjacent levels of the encoder, and the feature extraction capability of the encoder on each substructure is improved through mask adaptive fusion weight; a text-driven multi-level segmentation supervision module is added to a decoder part, and a segmentation result is guided and optimized by using semantic features, so that a constructed deep learning network effectively understands heterogeneity features of a congenital heart disease structure, and the structure distinguishing capability of a fuzzy boundary is enhanced; automatic segmentation of the congenital heart disease whole heart structure can be achieved, and effectiveness and accuracy of congenital heart disease whole heart segmentation are improved.

The invention belongs to the technical field of image processing, and particularly relates to a geological disaster risk area dynamic updating method based on slope unit automatic segmentation. Comprising the following steps: 1, acquiring digital elevation model raster data covering a target area and remote sensing image raster data spatially aligned with the digital elevation model raster data, and outputting slope unit vector data; 2, acquiring disaster-bearing body vector data, performing spatial superposition on the disaster-bearing body vector data and the slope unit vector data, and combining outer boundaries of slope units of all geological disaster risk areas to generate risk area vector boundary data; 3, generating new year risk area vector boundary data based on the new year data; and updating the risk area vector boundary data of the last year based on the risk area newly-added area and the risk area reduced area. According to the method, the slope unit division precision, the disaster-bearing body exposure description capability and the timeliness of risk area range updating are remarkably improved.

The embodiment of the invention discloses an automatic image segmentation method and device based on semantic segmentation and superpixel fusion, and the method comprises the steps: carrying out the image feature extraction of a target image, carrying out the semantic region division through a semantic segmentation module, and outputting a tongue region target image after region division; the method comprises the following steps: inputting a target image of a tongue region into a clustering engine for iterative calculation to obtain a super-pixel block set, setting a segmentation threshold value, performing pixel proportion calculation on the target image of the tongue region to determine a target affiliation category of the super-pixel block, and determining affiliation division of a tongue edge in the target image of the tongue region; according to the embodiment of the invention, semantic segmentation and pixel clustering are fused, an approximate tongue region is firstly positioned, and then the edge of the tongue is finely corrected, so that the edge blurring of pure semantic segmentation is avoided, and the semantic deviation of pure super-pixel segmentation is solved; meanwhile, a low segmentation threshold value is set to solve the under-segmentation problem of an edge fuzzy region, and the whole process is automatic without manual intervention.

1. Name of the product in this design: Graphical User Interface for Medical Image Annotation Function in Electronic Devices. 2. Purpose of this design: An electronic device. 3. The key design features of this product are its graphical user interface. 4. The image or photograph that best illustrates the design's key features: the front view. 5. Purpose of the graphical user interface: to display medical image data and to complete the annotation of medical image data. 6. The process of changing the graphical user interface: The main view is the initial interface of the software. Click the "SAM" button in the main view to enter the interface change state diagram 1; in the interface change state diagram 1, select a label in the label list on the right and move the mouse to the area to be labeled, and click the left mouse button to enter the interface change state diagram 2; in the interface change state diagram 2, after SAM automatically segments the ROI, press the "space bar" on the keyboard to enter the interface change state diagram 3; in the interface change state diagram 3, click the "Edge Modification" button at the top to enter the interface change state diagram 4.

The invention belongs to the technical field of computer vision, and discloses an unmanned aerial vehicle building outer wall crack adaptive segmentation method based on deep learning, which comprises the steps of collecting an outer wall image in a process that an unmanned aerial vehicle flies along a building facade, and synchronously obtaining space reference information corresponding to the outer wall image; based on the spatial reference information, establishing a spatial reference relationship corresponding to the building facade for the outer wall image; basic image correction processing is performed on the outer wall image, and self-adaptive adjustment is performed on the contrast ratio and noise suppression parameters of the outer wall image according to the surface material and texture feature difference of the outer wall; depth feature coding of crack analysis is executed according to the adjusted outer wall image, and in the coding process, crack structure sensing features containing prior information of a crack structure are formed through adaptive modeling of the slender shape, direction consistency and cross-regional continuity characteristics of the crack; the automatic segmentation and detection of the external wall crack with high precision, good continuity and spatial positioning are realized.

This invention discloses an automatic segmentation and scoring method and system for FDG PET-CT lesions in lymphoma, belonging to the field of medical image analysis technology. It aims to improve the segmentation accuracy of lymphoma lesions and the objectivity of the Deauville score. First, standardized uptake values ​​are calculated for FDG PET images, and lymph node morphological features are extracted from CT images based on multi-scale Hessian enhancement filtering to construct a dual-modality PET-CT image pair. Then, a dual-channel depth network is used to extract anatomical structural features and metabolic distribution features respectively. Cross-modal gating fusion is used to suppress physiological uptake interference, outputting preliminary lesion segmentation results. Next, three-dimensional connected component analysis is performed on the segmentation mask, and metabolic heterogeneity index is extracted by combining kurtosis and Haar wavelet multi-scale energy. A graph attention network is used to identify key lesions. Finally, the ratio of key lesions to standardized liver uptake values ​​is combined with the metabolic heterogeneity index to correct the Deauville score, achieving automation from lesion detection to treatment efficacy evaluation.

The application discloses a fiber bundle automatic segmentation and quantitative labeling method for white matter abnormalities of Parkinson's disease, and belongs to the technical field of medical image processing. The application solves the problem that the existing technology relies on manual delineation or traditional machine learning methods for white matter abnormality detection, and has the problems of low efficiency and strong subjectivity. By setting a first threshold value and a second threshold value, not only can the abnormal fiber bundle be identified, but also the required threshold range can be selected according to different research purposes and clinical needs, so that reliable judgment results can be provided in both diagnosis and early screening. If both threshold values are selected, the method can comprehensively judge each parameter of each fiber bundle, so as to more accurately identify the white matter abnormal fiber bundle of the Parkinson's disease patient, improve the accuracy of the fiber bundle labeling result of the white matter abnormalities of Parkinson's disease, realize the function of high-precision positioning of the white matter abnormal fiber bundle of Parkinson's disease, and provide stronger support for clinical diagnosis.

The invention discloses a small sample abdomen multi-organ image segmentation method based on a prototype network and cross attention. The method comprises the following steps: firstly, preprocessing an abdominal computed tomography (CT) image, and completing resampling and intensity normalization; dividing the preprocessed image into a support set and a query set, and constructing a small sample segmentation task; a support image and a query image are input into a deep learning network model, a cross attention module is introduced into a multi-layer structure of an encoder to explicitly model foreground, background and boundary regions, interaction and fusion among different level features are enhanced, and pixel-by-pixel matching and distinguishing of support prototype and query features are realized in combination with a double-branch contrast learning structure. According to the method, a cross attention mechanism is introduced into multiple layers of the encoder, the correlation and boundary expression ability between the features are effectively enhanced, the discrimination and robustness of the model under the small sample condition are further improved through a double-branch contrast learning structure, and therefore under the condition that labeling data is limited, the accuracy and robustness of the model are improved. And efficient and automatic segmentation of multiple organs of the abdomen is realized.

The invention discloses a medical organ image automatic segmentation and discrimination marking system based on deep learning. The system comprises an acquisition module, an identification module, a determination module, a determination module, an adjustment module, a marking module and an output module. According to the method, temporary grids are preliminarily screened out by analyzing the ratio and the surface roughness of deep color areas; determining key grids in combination with physiological status characteristics such as saturation and creeping frequency; the threshold value is dynamically adjusted according to key grid data, and the accuracy is improved; and finally, synthesizing the echo uniformity and the ideal polyp image, and determining the target polyp image, thereby reducing the risk of errors of a single model, facilitating the reduction of missed diagnosis and misdiagnosis, improving the diagnosis reliability, assisting the formulation of a scientific treatment scheme, and improving the diagnosis efficiency. The problems of inaccuracy in polyp recognition and inaccuracy in image segmentation caused by poor adaptability to unstable stomach peristalsis due to excessive dependence on a model are effectively solved.

The invention provides a chest CT image automatic segmentation and interstitial pneumonia prediction method and system, and the method and system achieve the automatic segmentation of a CT image through deep learning, greatly improve the image analysis efficiency, reduce the error of manual intervention, and reduce the judgment deviation caused by subjective factors. Quantitative features are combined with a statistical model, small changes of lung images are effectively captured, and the accuracy of IP risk prediction is remarkably improved. According to test data, in a pneumonia patient sample, the accuracy rate of model prediction IP is improved to 85% or above, the method provides timely IP risk prompts for clinicians based on an early warning function of CT image segmentation and feature analysis, and is beneficial to optimizing a treatment scheme, delaying or reducing the probability of occurrence of serious complications, and improving the accuracy rate of the model prediction IP in the pneumonia patient sample. According to the automatic image analysis and prediction system, the working pressure of image doctors can be relieved, the film reading burden is greatly reduced, and the response efficiency of a medical system is improved.

An automated segmentation method for quality inspection is proposed. A method for measuring defects in components using quality inspection methods is presented. The method includes: receiving a digital image of a component to be inspected, receiving a first reference image of the component to be inspected, and obtaining a second reference image from a combination of the received digital image and the first reference image. Furthermore, the method includes: activating a machine learning-based trained classifier system, which has been trained with training data to build a model, wherein the model serves as the basis for semantic segmentation of voxels in the received image for defect classification; and classifying the voxels of the received digital image by the activated classifier system, wherein the voxels of the received digital image and the voxels of the second reference image are used as input data to the classifier system.

The invention discloses a citrus X-ray image rapid reconstruction and defect segmentation method based on sparse point cloud and 3DGS, and relates to image processing, and the method comprises the following steps: S1, obtaining X-ray image data in a citrus under a sparse view angle; s2, performing three-dimensional reconstruction on the X-ray image data by adopting a three-dimensional Gaussian point cloud reconstruction method based on adaptive density control to obtain a reconstructed three-dimensional body; s3, performing automatic defect segmentation on the reconstructed three-dimensional body through the three-dimensional segmentation model to obtain two-dimensional defect mask slices and three-dimensional defect voxel data; and S4, according to the two-dimensional defect mask slices and the three-dimensional defect voxel data, performing comprehensive evaluation on the internal defects of the citrus, and outputting an internal quality report of the citrus. According to the method, full-process automatic processing from sparse view angle X-ray image data acquisition to three-dimensional volume reconstruction to automatic defect segmentation and type identification is realized, and the purpose of efficiently, accurately and losslessly identifying the internal defects of the citrus is achieved.

The application discloses a nasopharyngeal carcinoma prognosis feature determination method, system and device and a storage medium. The method comprises the following steps: pathological image preprocessing, color normalization based on dye separation is used to standardize the dyeing of the pathological image; a segmentation network is used to automatically segment the lesion area of the preprocessed pathological image to obtain a segmented image; the segmented image is cropped to obtain a target image block; principal component analysis is used to reduce the dimension of the target image block to obtain reduced dimension data; a clustering algorithm is used to perform unsupervised autonomous learning on the reduced dimension data to obtain pathological image features; and finally, the pathological image features are screened through feature inspection to determine a prognosis pathological feature set. The application can obtain and screen key image features of pathological images closely related to local area recurrence and distant metastasis of nasopharyngeal carcinoma from pathological images to assist in prognosis prediction of nasopharyngeal carcinoma, and can be widely applied to the technical field of image processing.

The invention belongs to the field of medical image processing, and provides an improved deep learning model based on U-Net for a placenta automatic segmentation task in a magnetic resonance imaging (MRI) image. Technical improvement key points are as follows: a convolutional block attention mechanism module (CBAM) is embedded in a down-sampling path, and the key channel and spatial feature extraction capability is enhanced; meanwhile, an edge perception enhancement module (BPM) is embedded, so that the edge distinction degree of the placenta and the uterus tissue is enhanced; in addition, an attention gating module (AG) is additionally arranged on an up-sampling path, the semantic distinguishing capability of a placenta target area and a background is improved, and background noise interference is reduced. Experimental verification shows that the segmentation precision of the model in a placenta MRI automatic segmentation task is obviously superior to that of an existing method.