206 results about "Lesion segmentation" patented technology

The invention discloses a medical image segmentation method and system based on guidance information and a multi-dimensional attention mechanism. The method comprises the following steps: collecting an original dermatoscope image for preprocessing; constructing a segmentation model, wherein the segmentation model comprises a double-path image encoder, a guide information encoder and a mask decoder; the two-way image encoder is used for extracting local detail features and global context semantic information in the image; the guide information encoder is used for converting a coarse segmentation mask predicted by the last round of network into guide feature information; the mask decoder fuses the image feature information and the guide feature information, gradually restores and refines the coarse-grained feature map, and finally outputs an accurate lesion segmentation mask; constructing a loss function, and training the segmentation model by using the preprocessed data; and inputting a to-be-segmented original dermatoscope image into the trained segmentation model, and outputting a lesion region segmentation mask of the image. According to the method, the segmentation precision and the model generalization ability can be improved, and the multi-scale lesion processing ability is enhanced.

The invention relates to the technical field of image processing, in particular to a clinical focus auxiliary segmentation system based on a nuclear magnetic resonance image. The system comprises an MRI image processing module, a lesion auxiliary segmentation module, a probability segmentation correction module and a lesion boundary smoothing module, a corresponding clinical nuclear magnetic resonance image set of a patient can be obtained, image position alignment and gray level adjustment processing can be carried out, and meanwhile a corresponding clinical image lesion segmentation model is constructed to carry out multi-scale fusion auxiliary segmentation. Generating a clinical focus region segmentation fusion image; obtaining a focus confidence probability corresponding to each pixel point in the segmentation image through the clinical focus region segmentation fusion image, and carrying out probability segmentation boundary correction on the clinical focus region segmentation fusion image to obtain a clinical focus region segmentation correction result image; and performing focus edge shape smoothing processing on the clinical focus region segmentation correction result map to generate a clinical focus edge shape segmentation optimization result. According to the invention, high-precision segmentation of the focus in the MRI image can be realized.

The invention belongs to the field of medical image processing, relates to a medical image focus segmentation and three-dimensional reconstruction method and system based on artificial intelligence, and aims to solve the problem of low model precision caused by mutual isolation of segmentation and reconstruction and unidirectional transmission of errors. The method comprises the following steps: fusing a multi-modal medical image; a segmentation network is adopted to generate a preliminary focus mask; constructing an initial three-dimensional geometric surface based on the mask, and performing physically-driven curved surface optimization; reversely projecting the optimization model to the feature space of the segmentation network, calculating the spatial inconsistency between the optimization model and network prediction, and generating an attention weight map; feeding back the attention weight map to the segmentation network, and iteratively updating network parameters to refine segmentation boundaries; and based on the final segmentation result after convergence, three-dimensional reconstruction guided by the network features is executed again. According to the method, a closed-loop feedback and collaborative optimization mechanism between segmentation and reconstruction is constructed, and the accuracy of focus segmentation and the geometric fidelity of a three-dimensional reconstruction model are remarkably improved.

The invention provides an automatic focus identification and grading method for a medical image, and the method comprises the steps: carrying out the standardization of an obtained multi-modal original image based on anatomical constraint, and obtaining a standardized image; generating semantic enhancement features through a cross-modal feature compensation network based on the standardized image and associated radiological text description; performing dynamic feature adaptation processing on the semantic enhancement feature to generate a modal adaptive feature; performing context reasoning through a multi-scale feature interaction algorithm based on the modal adaptive features to generate context reasoning features; and lesion identification decoding processing is carried out on the context inference feature map, a lesion segmentation mask is generated, and the lesion segmentation mask is used for extracting lesion area feature parameters to carry out lesion classification. By adopting the method, the adaptability to the missing mode can be enhanced, and the focus identification and grading precision can be improved.

The invention relates to the technical field of image processing, and discloses a pulmonary embolism focus segmentation method and system based on image recognition, and a storage medium. The method comprises the following steps: extracting a multi-level blood vessel topological structure of a CTPA image through blood vessel diameter gradient analysis; modeling blood vessel density distribution by using a Weibull mixed model to obtain embolism characteristic parameters; the pixel embolism probability is estimated through variational Bayesian reasoning, and a focus distribution diagram is generated; performing multi-scale feature fusion on the lesion probability graph to obtain a segmentation boundary; and obtaining a final embolism focus segmentation result based on the vascular connectivity constraint optimization boundary. The problems that blood vessel level differentiation processing cannot be achieved, and accurate probability modeling and anatomical constraint verification are lacked are solved. The accuracy of pulmonary embolism focus segmentation is improved.

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 medical image artifact identification and elimination method based on a big data technology, and relates to the technical field of medical image processing, and the method comprises the steps: carrying out the preprocessing of collected image data based on gray normalization, and then carrying out the semantic segmentation and ROI positioning of the image content; and performing lesion segmentation on the positioned image content, selecting lesion features for quantification and fusion, performing model verification, and performing distributed deployment on the verified lesion segmentation model. According to the method, the problems of high missed detection rate of small nodules and high missed diagnosis risk of malignant lesions are solved through the lesion detection model, the false positive rate is reduced, the recall rate of the malignant lesions is improved, and through the lesion segmentation model, the segmentation adaptability to lesions of different sizes is improved, clear segmentation boundaries are obtained, surgical planning is assisted, and boundary positioning errors are reduced.

The invention belongs to the field of medical image processing and artificial intelligence, and provides a colon cancer CT image segmentation method and system fusing individualized features, and the method comprises the steps: obtaining the abdomen CT images of a historical patient and a target patient; obtaining structured individual feature information related to the patient, wherein the individual feature information comprises age, gender, colon cancer family history and intestinal medical history information; preprocessing the abdomen CT image; and training an image segmentation model by using the preprocessed abdominal CT image of the historical patient, segmenting the preprocessed abdominal CT image of the target patient by using the trained image segmentation model meeting the segmentation precision requirement, and generating a colon cancer focus segmentation image conforming to the individual feature difference of the target patient. According to the method, the structured patient information is utilized to guide the model network to adjust the feature response, so that the segmentation precision and individual adaptability of the model are improved, and the segmentation accuracy is further improved.

The invention relates to the technical field of medical images, in particular to a medical image intelligent detection and auxiliary diagnosis system based on deep learning, and the system comprises a data access module which is used for obtaining medical image data and clinical text data of a patient; the data fusion module is used for generating a focus feature vector and a text feature vector, and performing cross-modal alignment and fusion to generate a fusion feature vector; the diagnosis analysis module is used for executing focus detection, focus segmentation and focus classification tasks, generating a diagnosis result and generating a diagnosis label based on the diagnosis result; the decision generation module is used for mapping the generated execution result to a preset medical knowledge base and performing deep reasoning to generate an auxiliary diagnosis decision; the decision auditing module is used for performing confidence scoring on the auxiliary diagnosis decisions and selecting the auxiliary diagnosis decision with the highest confidence score as the final auxiliary decision; and the data visualization module is used for carrying out visualization processing on the auxiliary decision and the diagnosis result.

The invention, which belongs to the technical field of medical image processing and computer vision, discloses a multispectral imaging-assisted post-radiotherapy skin injury early detection system comprising a multispectral data acquisition module, a deep tissue feature extraction module, a three-dimensional lesion segmentation module and a space-time tracking evaluation module. The subcutaneous 2.5 cm depth tissue information is obtained through multispectral imaging at the wave band of 400-1350nm, blood perfusion, melanin concentration, collagen structure and other physiological parameters are inversed based on the radiation transfer theory, a three-dimensional medical image segmentation algorithm is adopted to achieve three-dimensional accurate segmentation of an injury area, a spatio-temporal evolution model is established to predict the injury development trend, and the damage development trend is predicted. The radioactive skin injury can be detected in the subclinical period, the detection time window is advanced by 5.2 days on average, the occurrence rate of severe dermatitis is reduced by 65%, and a basis is provided for clinical timely intervention.

The invention provides a pneumonia CT image diagnosis model training method, diagnosis method and equipment, and the training method comprises the steps: inputting a 3D chest CT image into a multi-task deep learning model, enabling a shared encoder in the model to extract multi-scale feature data, and enabling a connection module and a decoder to obtain pneumonia focus region prediction result data according to the multi-scale feature data, the classification head obtains pneumonia diagnosis type prediction result data according to the multi-scale feature data; determining the joint loss of the model in the current iteration round and updating model parameters; and if the current multi-task deep learning model satisfies a training termination condition, outputting the current model as a pneumonia CT image diagnosis model. According to the method, the problems of low model feature utilization rate and low pneumonia diagnosis process efficiency caused by incapability of simultaneously completing focus segmentation and type classification due to task simplification of an existing pneumonia diagnosis model can be solved.

The invention discloses a medical image report automatic generation method and device based on artificial intelligence, relates to the technical field of artificial intelligence and medical image crossing, and aims to shorten the report generation period and improve the report generation efficiency while improving the accuracy and consistency of report diagnosis. The method comprises the following steps: carrying out cross-modal image space alignment processing on a PET image and a CT image; a pre-trained PET focus segmentation model is adopted, a high-metabolism focus part is identified and segmented in the processed PET image, and focus iconography parameters are acquired; segmenting an organ image in the processed CT image by adopting a pre-trained CT image organ segmentation model, and determining focus position information; sorting the lesion iconography parameters and the lesion position information to generate an initial report, and processing the initial report by adopting the fine-tuned large language model to obtain a medical image report and outputting the medical image report.

The invention belongs to the technical field of medical image processing, and particularly relates to a non-functional pancreatic neuroendocrine tumor preoperative index prediction method and system. According to the method, on the basis of abdomen CT image lesion segmentation, pathological grading (low risk / high risk) and lymph node metastasis (LNM) risks are jointly predicted through a multi-task deep learning framework; according to the method, tumor morphological features, radiomics features and clinical parameters (such as tumor size and position) extracted by a segmentation network are combined, and high-precision prediction of preoperative key indexes is realized by using a feature fusion module and a lightweight classification head; through a cross-modal attention mechanism and multi-center data verification, the model achieves the pathologic classification AUC of 0.75 on an internal verification set, achieves the LNM prediction AUC of 0.78, and is significantly superior to traditional clinical experience judgment. According to the method, reliable decision support is provided for preoperative precise operation planning and personalized treatment.

The invention provides a pneumonia medical data analysis and processing method, system and device and a storage medium, and belongs to the field of medical image processing. Multi-scale features of the lung CT image are extracted layer by layer through convolution, global pooling operation down-sampling is carried out based on the multi-scale features, and saliency features of lung lobe and lesion areas are captured from local details to global semantics; performing convolution and multi-scale pooling on the saliency features to generate a feature map fusing global and local information; and recovering the feature map through layer-by-layer up-sampling to obtain a segmentation result of the lung lobe and the focus. Quantitative indicators are calculated based on regions and pixels of the segmentation results of the lung lobes and lesions. And performing model processing based on the quantitative index, the segmentation result of the lung lobe and the focus and clinical data of the lung lobe and the patient to obtain pneumonia focus characteristic data. And a doctor can conveniently make an objective and reasonable treatment scheme according to a result of lesion segmentation and feature data calculation based on the pneumonia lesion feature data.

The invention discloses an esophageal anomaly detection method and system based on an endoscope image, and relates to the technical field of medical image processing, and the method comprises the steps: extracting a brightness component from an enhanced image, generating a smooth layer and a detail layer through weighted least square filtering, calculating a spectrum guide weight based on a dimension-reduced hyperspectral image, and carrying out the detection of the esophageal anomaly. Fusing the detail layer and the smooth layer to generate a de-noised brightness map, generating a de-noised image through color conversion, calculating spectral intensity characteristics based on the dimension-reduced hyperspectral image, generating an initial foreground mask, and generating an optimized mask through morphological closed operation optimization; through hyperspectral imaging, multi-modal feature fusion and local gamma value enhancement, the spectral feature capture capability of the lesion and the visualization effect of the lesion area are improved, and the accuracy and robustness of lesion segmentation and classification are significantly improved.

The invention discloses a spinal degenerative disease segmentation and reconstruction method based on anatomical cognition, and relates to the technical field of medical image segmentation of spines. The method comprises the following steps: firstly, encoding multi-level anatomical knowledge of a degenerative lesion into probability representation, and capturing a complex relationship among an organ, an original structure and a lesion; secondly, a deep logical reasoning method simulates the reasoning process of a doctor by fusing an observation result with low resolution but high confidence with anatomical knowledge; according to the method, the accuracy and interpretability of segmentation based on deep learning are improved, comprehensive 3D indexes are provided for lesion assessment, and the method can be applied to early recognition and personalized management of spinal degenerative lesions.

The invention discloses a focus segmentation model based on a mask-guided multi-scale feature fusion technology and a training method thereof, and relates to the field of focus segmentation. By introducing a mask-guided multi-scale feature fusion mechanism, the joint modeling capability of the model on global context information and local detail features is effectively enhanced. The encoder extracts multi-scale features by using the Swin Transform, down-samples original guide masks through the mask guide module and adapts the original guide masks to feature spaces of all levels, feature fusion of target perception is achieved, and the characterization capacity of a complex focus structure is remarkably improved. In the decoder, a pixel decoder uniformly upsamples fusion features into a high-resolution feature map, and a Transform decoder is combined with a set prediction normal form to realize accurate positioning and segmentation of a focus instance through layer-by-layer interaction of a learnable query vector and the high-resolution features. According to the structure, redundancy prediction and post-processing dependence caused by pixel-by-pixel classification in a traditional method is avoided, and the recognition sensitivity and segmentation precision of small focuses are improved.

The invention relates to the technical field of medical informatics, and provides an interstitial lung disease HRCT image analysis method and system based on artificial intelligence. The method comprises the following steps: performing pulmonary parenchyma segmentation processing on an input HRCT image to obtain a pulmonary parenchyma region; performing multi-category lesion segmentation on the pulmonary parenchyma region to obtain a segmentation mask; performing quantitative calculation on lesion distribution characteristics according to the segmentation mask to obtain lesion distribution parameters; and carrying out image mode classification according to the segmentation mask and the lesion distribution parameters to obtain a classification result. According to the method, the efficiency and precision of HRCT image analysis are improved, and the application range of HRCT image analysis is expanded.

The application discloses a liver lesion image description method based on a semantic segmentation network, comprising the following steps: building a Unet semantic segmentation network based on a lightweight network GhostNet, and constructing a liver lesion segmentation model; acquiring a historical liver lesion ultrasound image dataset, inputting the liver lesion segmentation model for training, and obtaining an optimal segmentation model; and describing the features of the lesion image based on the segmentation result. The application segments the liver lesion by constructing a lesion segmentation model, and describes based on the segmentation result, thereby improving the accuracy and reliability of liver disease diagnosis, reducing misjudgment and subjective bias; by combining the advantages of deep learning and traditional image processing, the characteristics of both are fully utilized, the performance and stability of the algorithm are improved; automatic processing and analysis of the ultrasound image are realized, the lesion description result provides accurate disease positioning and type judgment, and a better treatment scheme is provided for doctors, thereby improving the treatment effect and treatment experience of patients.

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 present invention provides a flexible, light-weighted and efficient multistream fusion encoder which can be easily integrated into multistream convolutional neural networks to perform segmentation and classification tasks on MRI images registered with different modalities. The encoder allows fusion of extracted feature maps in multiple streams on a layer-by-layer basis and generates the output of each stream by adding the corresponding convolutional output with an adaptively weighted fusion map computed from outputs of all streams. Adaptive weighting of fusion maps at each layer allows flexibility in highlighting different image modalities according to their relative influence on the segmentation / classification performance. The fusion encoder can also play an important role in the segmentation-classification workflow in biopsy and focal therapy planning.

The invention provides a cerebral apoplexy focus segmentation method and system based on edge enhancement and feature fusion. The method comprises the following steps: constructing a lesion segmentation network, specifically, adopting a two-dimensional U-Net as a basic framework, replacing lower three layers of an encoder of the two-dimensional U-Net with LE-Swin, replacing upper three layers of a decoder of the two-dimensional U-Net with an edge sensing decoder, and adding a channel attention fusion module CAF at each layer jump joint of the two-dimensional U-Net, the features of the encoder and the decoder are subjected to weighted fusion through channel attention; wherein the LE-Swin represents a local feature enhanced Swin Transform, and the LE-Swin represents a local feature enhanced Swin Transform; training the focus segmentation network to obtain a trained focus segmentation model; and inputting a target cerebral apoplexy MRI image into the trained lesion segmentation model to obtain a lesion area.

A kind of lymphoma lesion segmentation method based on convolutional neural network multimodal fusion, the method comprises the following steps: step 1. Constructing double-branch convolutional neural network model containing feature fusion module;Step 2. PET and CT images are respectively input into the PET branch and CT branch of convolutional neural network, and intermediate feature map is obtained;Step 3. PET and CT intermediate feature maps with the same size and channel number are input into the fusion module after concatenate, and the fused feature map is obtained;Step 4. The fused feature map is input into the corresponding decoder layer, and segmentation prediction is carried out.The performance of lymphoma lesion automatic identification segmentation is improved by the additional fusion module, which fuses the intermediate feature maps of PET and CT.

The application discloses a liver cancer lesion segmentation method based on dynamic feature fusion, relates to the technical field of liver cancer diagnosis assistance, and comprises the steps of multi-source data acquisition, feature extraction, dynamic fusion, segmentation output and result verification. 4D images, multi-posture liver images and synchronous physiological signals are acquired through a multi-source data acquisition module, features are extracted through static and dynamic feature extraction modules, weights are allocated by a dynamic fusion module, and then the segmentation output and result verification module is processed. Through the cooperative processing of the above-mentioned multi-source data acquisition, feature extraction and other modules, the application can accurately distinguish the real lesion boundary from the motion artifact, effectively reduces the missed detection situation caused by the fuzzy boundary of the micro lesion, greatly reduces the missed detection rate caused by the atypical single posture feature, improves the identification accuracy by capturing the difference in the morphological stability of benign and malignant lesions, and finally realizes the accurate segmentation of the liver cancer lesion, thereby providing a reliable basis for clinical diagnosis and treatment decision.

The application provides a medical image segmentation method and system based on concept guidance and cross-modal alignment, and belongs to the field of medical image processing. The method comprises the following steps: obtaining a medical image to be segmented and its corresponding clinical text description; generating clinical knowledge concepts related to the target disease by using a large language model, and constructing a concept set through clinical review; inputting the medical image, the clinical text description and the concept set into a trained concept-guided segmentation model to extract visual features, text features and concept labels; generating concept features aligned with the visual features through a concept-visual alignment module; dynamically adjusting the normalization process of the visual features through a concept modulation decoder, combining the features through multi-head cross attention, and outputting the final image segmentation result by using a segmentation head. The application effectively solves the problems of lack of effective clinical prior guidance in existing medical image segmentation, poor cross-modal feature alignment, and insufficient lesion segmentation accuracy.

The invention discloses an analysis method and system for a multi-tracer image, a storage medium and computer equipment, and the method comprises the steps: obtaining PET images and CT images of a target object under each tracer, carrying out the lesion segmentation of each PET image to obtain a lesion segmentation result, and carrying out the organ segmentation of each CT image to obtain an organ segmentation result; performing registration processing on the CT image and the PET image through a unified CT coordinate system to obtain a primary registration result, and performing secondary registration on the primary registration result based on each organ segmentation result to obtain a final registration result; determining a development overlapping region of the final registration result, and calculating a first index parameter according to the development overlapping region, each focus segmentation result and each organ segmentation result; calculating a second index parameter according to the tracer developing area of each PET image, the focus segmentation result and the matched organ segmentation result; and performing state analysis on the target object according to the first index parameter and each second index parameter.

The invention discloses an oral disease detection method based on an artificial intelligence large model, and relates to the technical field of oral detection, and the method comprises the following steps: achieving oral image enhancement through adaptive reflection suppression and non-uniform illumination correction; pathological and environmental features are extracted by using a double-flow network, and orthogonal constraint is applied to enforce feature independence; a gradient inversion layer is introduced into the multi-scale environment discriminator, and feature separation is realized through confrontation loss; the pathological features and the environmental suppression features are fused, and a lesion area is enhanced in combination with cross-scale attention weight; and generating a disease probability and lesion segmentation mask through classification and positioning double branches. Through a feature space decoupling mechanism, pathological features and environmental interference features are orthogonally separated in a potential space, and errors caused by light reflection, saliva shielding or instrument interference can be prevented; the two-channel adversarial training forces the network learning environment to be unchanged pathological representation, so that the model keeps stable recognition performance in a real clinical scene.

The present application relates to the technical field of image processing, and more particularly to a lesion segmentation method of adaptive dynamic text prompt, comprising: acquiring a lesion image, and generating an adaptive dynamic text prompt based on the lesion image; constructing a multi-modal enhanced fusion text prompt adapter based on a FiLM global channel recalibration module, a spatial cross-attention interaction module and a zero initialization gate nonlinear integration module cascade; the global channel recalibration module of FiLM utilizes a feature linear modulation mechanism to perform channel-level weighting on a visual feature map; before the feature enters spatial interaction, according to the text semantic enhancement band response, the activation of the background noise channel is inhibited. The present application overcomes the problems of insufficient utilization of multi-modal prompts, dependence on artificial static labeling of text prompts, serious dependence on prior information in the reasoning stage and difficulty in multi-modal feature fusion in a low-contrast environment in the existing medical image segmentation technology.

The invention provides a lesion image segmentation method and system based on double-model fusion, a terminal, a medium and a program product, and the method comprises a convolution encoder which is used for carrying out the multi-layer feature extraction of an input lesion image, and obtaining the detail feature information under different resolutions; the Transform encoder is used for extracting global feature information on the basis of the detail feature information extracted by the convolution encoder; and the deconvolution decoder is used for fusing the convolution coding result, the Transform coding result and the deconvolution decoding result under the same scale, and obtaining a lesion segmentation result after the original scale of the lesion image is recovered through decoding. The segmentation network algorithm realizes a two-stage lesion segmentation network (applied to thymoma image segmentation), combines the advantages of a convolutional neural network and a Transform model, better learns the spatial features of lesions (such as thymoma), obtains a better segmentation effect, and assists a doctor in obtaining a better clinical auxiliary diagnosis effect. The auxiliary diagnosis system is more accurate and convenient to operate and convenient for medical staff to quickly use, and the learning cost is greatly reduced. By introducing a CT image of a patient into the system, the system can automatically judge whether a lesion (such as thymopathy) exists or not.

The application discloses a stroke lesion segmentation method based on a progressive fusion network, and comprises the following steps: a three-layer U-Net network is used to construct a multi-stage segmentation network, the multi-stage segmentation network is respectively a first-stage segmentation subnetwork, a second-stage segmentation subnetwork and a third-stage segmentation subnetwork, a stroke MRI image to be segmented is subjected to a shuffle operation to obtain three-level pyramid images with different resolutions, and the three-level pyramid images are used as input images of each stage; the input images of each stage are input into the segmentation subnetworks of each stage in turn for iteration, feature extraction is performed on the input images in the iteration process, and finally a segmentation image is output. The multi-stage segmentation network constructed by the application sets an encoding end cross-stage shared feature learning module and a decoding end terminal progressive adaptive fusion module, enhances the correlation between the segmentation subnetworks of each stage, improves the utilization rate of features extracted by the multi-stage structure and a segmentation result, and thus the precision of medical image segmentation is improved.