4results about How to "Improve segmentation results" patented technology

The application provides a novel feature up-sampling method for semantic segmentation, which improves the resolution of an image by combining pixel reorganization Pixel Shuffle with an attention mechanism, prevents a chessboard effect, and avoids a 0 filling operation on unknown pixel points compared with a previous traditional up-sampling module; secondly, the attention mechanism is introduced to combine global information with local information, obtain a feature map with a larger receptive field and more detailed information, increase effective feature information, and realize the function of effectively improving the segmentation performance of the network. The application has universality, eliminates the limitations of previous methods by simply replacing all up-sampling modules, obtains more representative feature mappings, obtains an enlarged image with good features, makes the restored image obtain more feature information, and is beneficial to improving the segmentation result of the network model.

The application is a medical image segmentation method based on multi-modal self-supervision. First, the multi-modal medical image of the lesion tissue is obtained, including A-mode image and B-mode image, and the image is preprocessed. Then, a cycle-consistent modal contrast domain translation network is constructed, including two generators and two discriminators. The generator is used to convert the image of one mode into the image of another mode, including an encoder, an intermediate shared module and a decoder. The discriminator is used to judge the source of the input. Then, the cycle-consistent modal contrast domain translation network is pre-trained, the training loss is calculated, and the loss function includes multi-modal semantic consistency loss, adversarial loss, cross-domain translation loss and cycle consistency loss. Finally, the A-mode segmentation network and the B-mode segmentation network are constructed, the pre-trained weights are migrated to the two segmentation networks, and the trained two segmentation networks are respectively used for medical image segmentation of the corresponding mode. The contrast cross-domain translation is used as a multi-modal self-supervised pre-training task to learn more comprehensive modal features, promote the network to better learn modal characteristics and common knowledge, and improve the segmentation ability.

The invention discloses an acute respiratory distress syndrome (ARDS) prediction method and system based on multi-modal data and deep learning, relates to the technical field of computer-aided diagnosis, and provides a computer algorithm for intelligently predicting the severity of an ARDS patient based on computed tomography (CT) imaging and clinical indexes. Therefore, doctors can be assisted in early intervention and treatment. According to the technical key points, inflammation can be effectively segmented by using a 2.5 D U-Net model and a sub-visual recognition algorithm; according to the method, the ARDS is predicted by introducing an Extreme Graduent Boosting (XGBoost) algorithm after the segmented inflammation features are extracted, so that the ARDS prediction based on the imaging features is realized; the system can shorten the diagnosis time and reduce the death rate, and is of great significance to early diagnosis of ARDS. Experimental results show that the method provided by the invention has high accuracy in predicting the severity of the ARDS, the Dice coefficient of lung inflammation segmentation is 90.2%, and the accuracy of predicting the severity of the ARDS is 86.5%.

This invention relates to the fields of image segmentation and medical imaging technology, and particularly to a method, system, device, and medium for ultrasound image segmentation based on deep learning. The method includes the following steps: extracting multiple feature maps with different spatial resolutions from the current ultrasound image to be detected; optimizing the missing feature information in each feature map layer using complementary information between the different layers, resulting in optimized multi-layer feature maps; and performing an upsampling convolution operation on the optimized multi-layer feature maps to obtain an ultrasound image segmentation map at the same scale as the RGB image of the current ultrasound image to be detected. This method fully utilizes the complementary information between different layers of feature maps, performing targeted optimization on each layer to improve the segmentation results, and achieving higher detection accuracy.