359 results about "Skin lesion" patented technology

The invention provides a medical image segmentation method and system based on spatial detail enhanced vision, and relates to the technical field of image processing. The method comprises the following steps: performing initial feature mapping on an input and preprocessed medical image to obtain an embedded feature map; the embedded feature map is input into an encoder for feature extraction, and multi-scale features are obtained; the encoder comprises a plurality of encoding stages, and the number of channels is doubled and the spatial resolution is halved through down-sampling operation between the encoding stages; the multi-scale features are subjected to up-sampling and spatial enhancement reconstruction step by step through a decoder, and a high-precision segmentation result is generated; wherein the decoder comprises a plurality of decoding stages, the decoding stages correspond to the encoding stages, and feature fusion is carried out between the corresponding stages of the encoder and the decoder through jump connection. According to the method, the boundary description precision and the segmentation robustness of the low-contrast image can be improved without increasing the linear complexity, and the method is suitable for medical image segmentation scenes of skin lesions, gastrointestinal polyps and the like.

The invention provides a dermatoscope image segmentation method based on an attention mechanism and multi-scale information interaction, and belongs to the technical field of medical image segmentation. The technical problems that a traditional segmentation method is inaccurate in skin lesion area boundary recognition, insufficient in multi-scale feature extraction capacity and large in noise interference influence are solved. According to the technical scheme, the method comprises the following steps of 1, constructing a high-precision skin disease image data set; step 2, performing enhancement and standardization processing on the data set; 3, constructing a segmentation model based on an attention mechanism and multi-scale information interaction; 4, training the segmentation network by using the preprocessed data set, and optimizing the model performance by using a BCE-Dice combination loss function; and 5, inputting a test set to evaluate the performance of the model, and finally outputting an optimal segmentation result. The method has the advantages of being high in segmentation precision, high in boundary recognition capability and excellent in model robustness.

The invention discloses a skin lesion area segmentation method based on Mama. The method belongs to the technical field of neural network target detection. The technical problems that an existing image segmentation model cannot consider global information of a medical image, and the segmentation performance is poor for target structures with large texture, shape and size differences among different patients are solved. The method comprises the following steps: S1, carrying out preprocessing and data enhancement operation on a skin lesion image, and dividing a data set; s2, constructing a skin lesion region segmentation model, wherein the segmentation model comprises a patch embedding layer, an encoder, a decoder, a jump connection module, a dynamic feature fusion module and a final projection layer; and S3, training the skin lesion region segmentation model by using the data set, and performing lesion region segmentation on the skin lesion image by using the trained skin lesion region segmentation model to obtain a segmentation result graph of the lesion region.

The invention provides a skin lesion auxiliary detection method and system based on multi-modal information fusion, and belongs to the technical field of computer vision and application. The method comprises the following steps: firstly, acquiring and constructing bimodal data including a dermatoscope image and a text of a patient, and preprocessing the data; secondly, respectively adopting an improved convolutional neural network and a text coding model, and extracting deep and high-dimensional features from the skin disease image and the self-described text of the patient; afterwards, an improved symmetric gating cross attention fusion module is constructed, fine-grained interaction and alignment between visual and text features are realized through a bidirectional cross attention mechanism, and a gating unit is utilized to adaptively adjust a fusion weight; and finally, inputting the deeply fused multi-modal feature vector into a classifier to obtain a confidence score of each skin lesion type so as to provide reference for dermatologists. According to the method, the diagnosis accuracy, robustness and interpretability are synergistically improved, and the method has great clinical application value.

A method of operation of a compute system includes: receiving a patient image including a skin lesion and a fixed ruler, generating a correlated ruler by segmenting the fixed ruler including translating pixel-to-millimeter to the patient image, calculating a skin lesion area by segmenting the skin lesion, and generating a skin lesion output report, including an image of the skin lesion, the correlated ruler and the skin lesion area, for displaying on a device.

The invention provides a skin lesion image segmentation method and system based on multi-scale deformable aggregation, and the method comprises the steps: obtaining a skin lesion image data set, dividing the data set into a training set and a test set, constructing an improved U-shaped segmentation network model, optimizing model parameters through training, applying the trained model to the test set, and obtaining a skin lesion image segmentation result. And generating a high-precision segmented image. According to the model, an innovative convolution-parallel vision Mangbar module is adopted in an encoder, and extraction and fusion of local details and global long-range dependency of an image are realized through cooperation of a CNN and a state space model; a deformable convolution attention gate is put forward in a decoder, a key area is accurately focused by using a deformable convolution adaptive lesion shape, and cross-level feature fusion and multi-scale refining are realized. Through the multi-scale deformable aggregation mechanism, the segmentation precision and robustness of the model to complex and various skin lesions are significantly improved, and reliable assistance is provided for clinical diagnosis.

The invention relates to the technical field of skin lesion area reconstruction, in particular to a skin lesion area reconstruction system based on multi-modal image fusion. The system comprises a deep multi-scale feature extraction module, a skin thermal anomaly feature extraction module, a hemodynamic feature extraction module, a depth and boundary feature extraction module, a skin surface feature extraction module, a fluorescence intensity distribution and morphological feature extraction module, a first feature fusion module, a second feature fusion module and a lesion area reconstruction module. According to the method, appearance, heat, blood flow, structure and surface features are extracted from the multi-modal data, then the multi-modal image data are integrated, finally, comprehensive representation and reconstruction of skin lesion appearance, function and structure information are achieved according to the synergistic effect of the multi-modal information, and the accuracy and clinical reference value of lesion area reconstruction are effectively improved.

The invention provides a multi-enhancement semi-supervised medical image classification method based on a dynamic threshold value, and the method comprises the steps: obtaining a labeled image and a non-labeled image based on a skin disease image data set, building an initial semi-supervised image classification model through the labeled image and the non-labeled image based on deep learning, and carrying out the classification of the initial semi-supervised image. Respectively processing and merging the images with the labels and the images without the labels, obtaining a loss function based on a merging result, optimizing the initial semi-supervised image classification model based on the loss function to obtain a target semi-supervised image classification model, and evaluating and adjusting the target semi-supervised image classification model based on a performance evaluation index to obtain a target semi-supervised image classification model. The accuracy and generalization ability of the skin lesion image classification model are improved, and the dependence on large-scale manual annotation data is reduced by designing an effective learning mechanism, so that the annotation cost is reduced, the training efficiency is improved, the robustness of the model to the image data distribution change is enhanced, and the stable and consistent classification performance is kept.

The invention relates to the technical field of medical artificial intelligence and computer vision, in particular to a skin disease feature recognition method based on image processing, and the method comprises the following steps: S1, obtaining multi-modal data of a skin lesion region, including an RGB image, a thermal imaging image and a patient medical history text; s2, performing illumination correction, noise suppression and lesion region segmentation on the image data; the skin disease feature recognition system based on image processing comprises the following contents: a multispectral image acquisition module supporting synchronous acquisition of visible light, infrared and depth data; in the using process of the method and system, the diagnosis efficiency and accuracy of medical staff can be improved, the medical staff can be effectively assisted to analyze and confirm the type of the illness state, and meanwhile the doctor seeing efficiency of the medical staff can be effectively improved.

The invention provides an image-based skin treatment effect evaluation method, medium and system, and belongs to the technical field of skin treatment effect evaluation.A standardized image is collected, a focus area is recognized by applying a skin spectral analysis model, a three-level focus area division model is constructed, and a current boundary and a prediction boundary are calculated; determining a boundary transition index; pixel-level pigment analysis is carried out on the third-level focus area, a pigment distribution matrix is constructed, a pigment concentration representative center is extracted, and pigment distribution skewness is calculated; establishing a time sequence image library for comparison before and after, and calculating a focus area change rate, a boundary transition index change rate and a pigment concentration change rate; combining the indexes to calculate a comprehensive treatment effect score, and generating a thermodynamic diagram to display the treatment progress; a patient individualized evaluation model is established, evaluation precision and an individualized treatment scheme are optimized, and the technical problems that in the prior art, evaluation objectivity of the skin lesion treatment effect is insufficient, and accurate quantification is difficult are solved.

The invention relates to a medical image segmentation system and method based on context and frequency guidance and a storage medium, and the system employs an encoder-decoder architecture, and integrates three core modules: a visual state space module which captures global context information with linear complexity based on a selective state space model; the frequency-guided representation module is used for explicitly separating and enhancing structure and boundary characteristics through frequency domain transformation and complex weight modulation; and the multi-scale adaptive context aggregation module is used for integrating multi-scale semantics through parallel multi-branch convolution and dual Top-K sparse attention and focusing a key region. According to the method, the technical problems that global modeling and boundary details are difficult to consider, the calculation complexity is high and the adaptability to multi-scale targets is poor in the prior art are solved, the segmentation precision and the boundary description accuracy are remarkably improved in various medical image segmentation tasks such as heart MRI, polyp, skin lesion and pathological sections, and the medical image segmentation efficiency is improved. And meanwhile, the calculation efficiency is ensured.

The invention discloses a skin lesion image segmentation method, system and related device, and the method comprises the steps: inputting medical image data into a segmentation model, and obtaining a segmentation result through the processing of an encoder and a decoder; in an encoder, sampling is carried out in a first layer and a second layer in a pooling and convolution mode to reduce the space size of input data, comprehensive feature extraction is carried out in a third layer to a seventh layer, sampling is carried out in a eighth layer in a pooling and convolution mode to capture abstract features of a higher layer, and context information is enriched in a ninth layer to a twelfth layer. Capturing a remote dependency relationship and multi-channel space representation at the thirteenth layer; deconvolution up-sampling is used in the decoder, high-resolution details are recovered in a self-adaptive mode, the size of the feature map is recovered to the original input size, and a segmentation result is obtained; serious loss of detailed information caused by frequent down-sampling can be reduced, and an accurate segmentation result is more easily generated from features extracted by an encoder.

A medical skin lesion segmentation method based on a multi-scale feature matrix yoke belongs to the technical field of image processing, and comprises the following steps: 1, carrying out preprocessing such as denoising on a skin lesion image, obtaining a plurality of image features by using a gray level co-occurrence matrix method, and constructing a three-dimensional multi-scale image feature matrix; 2, carrying out yoke transformation on the three-dimensional multi-scale characteristic matrix to construct a multi-scale characteristic matrix yoke according with the characteristics of a yoke process; 3, modeling a two-dimensional Markov random field model, and creating an energy function based on a multi-scale feature matrix yoke; and 4, performing post-processing operations such as morphology on the obtained result. According to the method, the features obtained from the gray-level co-occurrence matrix are constructed into the three-dimensional multi-scale feature matrix yoke, and the new Markov random field energy function is constructed on the basis, so that a better segmentation effect can be obtained.

The invention discloses a skin lesion segmentation method and system based on dual-path perception multi-stage fusion, and relates to the technical field of medical image processing, and the method comprises the steps: carrying out the block division of a skin image, inputting into an encoder, and filtering a key lesion region through employing a region sparse attention mechanism; performing down-sampling on the feature map by using a block merging operation, and constructing a multi-scale feature pyramid; skin image features are processed by using a dual-path sensing multi-stage fusion network, deepest features generated by an encoder are processed by a spatial frequency dual-path cascade sensing module, and two-stage space-frequency collaborative optimization is executed; the method comprises the following steps: establishing jump connection between an encoder and a corresponding decoder through a space channel double-path parallel sensing module, recovering spatial resolution through block expansion operation in combination with modulated encoder characteristics, and generating a pixel-level lesion probability graph. According to the method, the context sensing capability across the space domain and the frequency domain is remarkably enhanced, and an accurate and detailed skin lesion segmentation result is obtained.

The invention discloses an extracellular vesicle derived from rhodomonas mucilaginosa and a hydrogel composition of the extracellular vesicle. The extracellular vesicle is obtained by carrying out ultrafiltration concentration and gradient ultracentrifugation on a culture bacterium solution. Compared with an original culture solution or a viable bacterium preparation thereof, the treatment efficiency and the safety of the extracellular vesicles are remarkably improved. Based on the extracellular vesicle, the invention also provides a hydrogel composition loaded with the extracellular vesicle, and the hydrogel composition is constructed from the extracellular vesicle, type I collagen, carboxymethyl chitosan and four-arm polyethylene glycol benzaldehyde in a chemical crosslinking manner, and has good adhesiveness, self-repairing ability and injectability. The extracellular vesicle, the hydrogel composition and the hydrogel preparation of the extracellular vesicle can serve as a medicine or a medicine carrier to be applied to atopic dermatitis, can specifically target 2-type inflammatory factors, and relieve pathological changes such as skin lesion phenotype, epidermis thickening, mast cell infiltration and inflammatory factor expression increase of an MC903-induced atopic dermatitis mouse model.

The invention provides a progress tracking method for skin lesion area intelligent measurement and three-dimensional reconstruction, which relates to the technical field of medical image processing, and comprises the steps of setting reference mark points at the periphery of a lesion area, collecting multi-angle image data, positioning the edge of the lesion area by combining an edge weight positioning algorithm with the reference mark points, a three-dimensional structure is reconstructed by adopting grid construction and a self-adaptive subdivision algorithm, and spatial registration is carried out by utilizing a reference mark point, so that precise tracking of the progress of a lesion area at different time points is realized, and the monitoring precision and reliability of the morphological change of the skin lesion area are improved.

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.

A method may include using a handheld device having a magnifying lens configured to produce a magnified image of an abnormal skin lesion. The magnifying lens comprises a vertical crosshair and a horizontal crosshair intersecting in a perpendicular configuration to segment the magnified image into quadrants, the crosshairs each having a linear scale disposed thereon. The method includes accessing qualities of a skin lesion observed in each quadrant.

The invention discloses a medical image segmentation method and device, equipment and a medium, and relates to the technical field of medical image segmentation, and the method comprises the steps: obtaining to-be-processed population data; the population data to be processed comprises multiple groups of skin lesion images, the skin lesion images comprise candidate key point sets, and each group of candidate key point sets is used for representing a boundary region of the skin lesion image; preprocessing each group of candidate key point sets to obtain a boundary diagram containing a boundary region; processing the boundary image and the corresponding skin lesion image through a trained image segmentation model, and outputting an image segmentation result; the image segmentation model comprises an encoder, a fusion module and a decoder; the encoder comprises a lightweight dynamic fusion module, a local edge guiding module and an attention module which are connected in sequence. According to the method, the parameter quantity is reduced while the model segmentation precision is ensured, the method can be conveniently deployed on edge equipment such as a mobile terminal and portable equipment, and the real-time performance of image segmentation processing is improved.

The invention relates to the technical field of deep learning and skin lesion recognition, in particular to a skin lesion image segmentation method based on edge guidance and an attention mechanism, and the method comprises the steps: inputting a to-be-segmented dermatoscope image into an image segmentation model for segmentation, and outputting a skin lesion segmented image; wherein the image segmentation model performs edge feature extraction on a dermatoscope image through an EEM module; performing multi-scale hole convolution processing on the semantic feature map through a DASPP module; adaptive fusion of channel and space attention is carried out on the segmented feature map through a CSAM module, a channel dimension and a space dimension are processed in sequence by adopting a serial design of channel priority and space postposition, and edge information of an edge prior feature map is introduced to carry out feature enhancement. According to the invention, the marginal region of the skin lesion is subjected to refined learning, and the marginal information of the lesion region is fully utilized to realize accurate positioning of the skin lesion, so that the accuracy and effect of skin lesion image segmentation are improved.

The invention discloses a hydrogel dressing for treating atopic dermatitis and a preparation method and application thereof, and belongs to the technical field of skin medical materials. The dressing is prepared by taking a glycerol / water / ethanol ternary mixed solvent as a dissolving system, dissolving polyvinyl alcohol, tannic acid and aluminum trichloride to construct a polymer skeleton matrix, and then adding traditional Chinese medicine active ingredients. The preparation does not need complex temperature control curing, the ternary solvent can realize uniform dissolution and stable dispersion of the active ingredients, the effective ingredients are prevented from being damaged by high temperature, the encapsulation efficiency of the traditional Chinese medicine extract is more than 90%, and the medicine can be slowly released for 72 hours. The dressing has a multi-target anti-inflammatory effect, can reduce the level of TNF-alpha at a skin lesion and promote proliferation of keratinocytes, is excellent in tensile and torsional stability, and can be attached to the skin for a long time in a dynamic environment. The preparation method is simple in process, controllable in raw material cost, high in production efficiency, suitable for large-scale production, good in dressing biocompatibility, high in safety, suitable for treatment of atopic dermatitis of different severity degrees and large in clinical transformation potential.

The invention discloses a skin lesion image segmentation method, which belongs to the field of medical image processing and segmentation, and comprises the following steps: preprocessing a collected skin lesion image data set, dividing the skin lesion image data set into a training set and a test set, and carrying out data enhancement processing on the training set; constructing an edge optimization segmentation model fusing double attention and dynamic multi-scale; training a segmentation model of the skin lesion image; importing a test set into the model; and generating a segmentation result. Feature extraction is realized through a U-shaped encoder decoder structure; through a multi-scale void feature fusion module, the extraction of image multi-scale features is enhanced; through a double attention module, the tiny focus effect is enhanced; and the integrity of focus information is reserved through an edge perception cross-scale feature fusion module. The method is used for improving the segmentation effect of the model on the skin lesion image.

The invention discloses a medicine composition for treating atopic dermatitis, a preparation method and application, the medicine composition comprises inactivated malassezia sphaeroides, and the strain preservation number of the malassezia sphaeroides is ATCC 96807. The cream composition disclosed by the invention is externally applied to local treatment of skin lesions of specific dermatitis, supplements skin symbiotic bacteria, assists in recovering skin barriers of patients, remarkably inhibits growth and proliferation of staphylococcus aureus at the skin lesions of dermatitis and exocrine toxins, remarkably relieves inflammatory response, reduces the thickness of cuticles and reduces inflammatory cell infiltration; the traditional Chinese medicine composition assists the treatment of atopic dermatitis through the effects of the proinflammatory factors and the expression and secretion of the proinflammatory factors, has a remarkable treatment effect on the atopic dermatitis, is quick to take effect, small in toxic and side effects and good in safety, and has a good medicinal prospect.

A use of liquiritin in the preparation of a drug for treating atopic dermatitis. Animal experiments show that liquiritin can remarkably lessen the skin lesion severity of an atopic dermatitis mouse model, reduce the ear thickness and the epidermal thickness, and reduce the infiltration degree of dermal lymphocytes, thereby demonstrating that liquiritin has a significant therapeutic effect on atopic dermatitis, has no toxic and side effects on the liver, the spleen and the kidney, and has good clinical application prospects and enormous social benefits, and expanding a new application field for a conventional leguminous plant liquorice extract, i.e., liquiritin.

Presented herein are systems and methods for detecting basal-cell carcinoma (BCC) in biomedical images of skin lesions. A computing system may identify, for a first subject at risk of BCC in a first lesion on a region of an epidermis: (i) a first biomedical image of an outer layer of the region on the epidermis of the first subject acquired in accordance with dermoscopy, and (ii) a plurality of second biomedical images of at least one inner layer in the region on the epidermis of the first subject acquired in accordance with reflection confocal microscopy (RCM). The computing system may apply the first biomedical image and the plurality of second biomedical images to a model architecture. The computing system may generate, based on applying to the model architecture, a score indicating a likelihood of BCC in the first lesion.

The invention discloses a pharmaceutical composition for treating atopic dermatitis and a preparation method and application of the pharmaceutical composition for treating atopic dermatitis. The pharmaceutical composition comprises matrix vaseline, a humectant, a moistening agent, a repairing agent and an antioxidant. Two lipid components (gamma-linolenic acid and linoleic acid) are supplemented, so that eczema-like dermatitis can be obviously relieved, skin lesion inflammatory cell infiltration is reduced, the content of fatty acid in a skin barrier is recovered, treatment of atopic dermatitis is assisted, and the treatment effect on the atopic dermatitis is remarkable; the pharmaceutical composition disclosed by the invention has the advantages of quick effect on atopic dermatitis, small toxic and side effects, good safety and good medicinal prospect.

The invention provides a dermatoscope image segmentation method, system and device based on Mama and a medium, and belongs to the technical field of image processing and computer vision, and the method comprises the steps: obtaining an original dermatoscope image, carrying out the preprocessing of the original dermatoscope image, and obtaining a preprocessed dermatoscope image set; acquiring a skin lesion label image corresponding to the original dermatoscope image, pairing the preprocessed dermatoscope image with the corresponding skin lesion label image, constructing a dermatoscope image data set with a label, and dividing the dermatoscope image data set into a dermatoscope image training set and a dermatoscope image test set; a neural network model based on Mama is constructed; feeding the dermatoscope image training set into a neural network model in batches for training to obtain a trained neural network model; and inputting a to-be-segmented original dermatoscope image into the trained neural network model to obtain a corresponding focus region segmentation result. According to the method, the accuracy of skin lesion segmentation is remarkably improved.

The application provides a skin lesion three-dimensional reconstruction system based on a flexible electrode array. The system is characterized by comprising a measuring belt (1), an excitation generator (3), a signal processing module (4), a microprocessor (5) and an imaging module (6). The application can be used for detecting uneven skin lesion areas and constructing three-dimensional shapes of the lesions, and can be widely used in the field of uneven human subcutaneous tissue health detection.

The present application relates to polypeptides and their use in repairing mucosal damage or skin damage, the polypeptides of the present application have no homology with known polypeptides, and can stably exist in vivo and in vitro, have the effect of regulating stem cell proliferation and differentiation to repair mucosal damage or skin damage; and relate to the use of the novel polypeptides to repair mucosal damage or skin damage by regulating stem cell proliferation and differentiation, and the use for preventing, alleviating or treating gastrointestinal diseases.

The utility model relates to a hydropathic compress instrument for skin disease nursing. The hydropathic compress instrument comprises a bridle and an atomization main body, a barrel shell is mounted in the middle of the length of the bridle, a dividing disc is fixedly arranged in the barrel shell with an open structure, and the barrel shell is divided into a containing space and an atomizing space communicated with the open structure by the dividing disc; the bridle is bound on a body part of a patient, the open structure is buckled on a skin lesion part area to drive the atomization main body and the spray gun to start working, the atomization main body atomizes a solution containing a medicament into gas, the gas is sprayed to the skin lesion part area, and then skin disease wet compress nursing is achieved. By means of the structure, the situation that a spray gun is manually held by hand is avoided, a fixed structure is adopted to replace the situation that the spray gun is manually held by hand, an atomized solution can be accurately sprayed to a skin lesion part, the solution is evenly distributed, and the situation that the solution in an edge area is insufficient possibly due to manual holding is avoided.