Abstract: A method for three-dimensional edge detection includes obtaining, for each of plural two-dimensional slices of a three-dimensional image, a two-dimensional object detection result and a two-dimensional edge detection result, stacking the two-dimensional object detection results into a three-dimensional object detection result, and stacking the two-dimensional edge detection results into a three-dimensional edge detection result. The method also includes performing encoding according to a feature map of the three-dimensional image, the three-dimensional object detection result, and the three-dimensional edge detection result, to obtain an encoding result, and performing decoding according to the encoding result, the three-dimensional object detection result, and the three-dimensional edge detection result, to obtain an optimized three-dimensional edge detection result of the three-dimensional image.

Abstract: This application relates to an image data inspection method and apparatus in the field of artificial intelligence (AI) technologies. The method includes obtaining an image to be inspected, the image to be inspected comprising a sequence of slice images; determining a corresponding group of slice images for each target image in the sequence of slice images; extracting a corresponding slice feature map for each slice image in the group of slice images; aligning the slice feature maps extracted corresponding to the group of slice images; aggregating context information of each slice image in the group of slice images by using an aligned feature map; and performing target region inspection on an aggregated feature map, to obtain an inspection result corresponding to the target image, and combining the inspection result corresponding to each target image, to generate an inspection result corresponding to the image to be inspected.

Abstract: In an image processing method, style conversion is performed on a sample image by using a generation network, to obtain a reference image. Style recognition is performed on the reference image by using an adversarial network, to determine a style loss between the reference image and the sample image. Image content recognition is performed on the reference image and the sample image, to determine a content loss between the reference image and the sample image. The generation network is trained based on the style loss and the content loss, to obtain a trained generation network.

Abstract: An image processing method includes: obtaining, through a plurality of radio frequency coils, a plurality of pieces of corresponding undersampled frequency-domain data respectively; and performing, by using a plurality of image processing networks that are cascaded, an information supplement operation respectively on the plurality of pieces of frequency-domain data to obtain a plurality of corresponding target restored images, and determining a target reconstructed image based on the plurality of target restored images, a piece of frequency-domain data being configured for obtaining one target restored image, and an image processing network including an image restoring network, a frequency-domain complement network, and a susceptibility estimation network.

Abstract: For three-dimensional segmentation from two-dimensional intracardiac echocardiography imaging, the three-dimension segmentation is output by a machine-learnt multi-task generator. Rather than the brute force approach of training the generator from 2D ICE images to output a 2D segmentation, the generator is trained from 3D information, such as a sparse ICE volume assembled from the 2D ICE images. Where sufficient ground truth data is not available, computed tomography or magnetic resonance data may be used as the ground truth for the sample sparse ICE volumes. The generator is trained to output both the 3D segmentation and a complete volume (i.e., more voxels represented than in the sparse ICE volume). The 3D segmentation may be further used to project to 2D as an input with an ICE image to another network trained to output a 2D segmentation for the ICE image. Display of the 3D segmentation and/or 2D segmentation may guide ablation of tissue in the patient.

Abstract: This application provides a training method and apparatus for an image processing model, an electronic device, and a storage medium.

Abstract: This application provides a method and an apparatus for training an artifact removal model. The method includes obtaining a reference image and a corresponding artifact image; inputting the artifact image into a plurality of sample removal models to obtain artifact removal results corresponding to the artifact image respectively output by the plurality of sample removal models; determining predicted loss values respectively corresponding to the plurality of sample removal models based on pixel differences between the artifact removal results and the reference image; inputting the predicted loss values respectively corresponding to the plurality of sample removal models into a sample weight model to generate weight parameters respectively corresponding to the plurality of predicted loss values; and training the plurality of sample removal models based on the predicted loss values and the weight parameters to obtain an artifact removal model.

Abstract: A training method includes: obtaining a first sample image and at least two types of second sample images; respectively adding at least two damage feature corresponding to the second sample images to the first sample image, to generate at least two types of single degradation images; fusing the single degradation images, to obtain a multiple degradation image corresponding to the first sample image; performing image reconstruction processing on the multiple degradation image, to generate a predicted reconstruction image corresponding to the multiple degradation image; calculating a loss function value based on the second sample images, the single degradation images, the first sample image, and the predicted reconstruction image; and updating a model parameter of the model based on the loss function value.

Abstract: A method for training an image classification model performed by an electronic device and includes: obtaining a plurality of sample source-domain images, a plurality of sample target-domain images, modal tagging results of the sample source-domain images, and category tagging results of the sample source-domain images; determining first category prediction results of the sample source-domain images by using a neural network model; determining first category prediction results of the sample target-domain images by using the neural network model; for a category tagging result, determining a first loss of the category tagging result based on source-domain image feature pairs corresponding to the category tagging result; and training the neural network model based on first losses of category tagging results, the first category prediction results of the sample source-domain images, and the first category prediction results of the sample target-domain images, to obtain an image classification model.

Abstract: An image processing method includes obtaining a sample image and a generative adversarial network (GAN), including a generation network and an adversarial network, and performing style conversion on the sample image, to obtain a reference image. The method further includes performing global style recognition on the reference image, to determine a global style loss between the reference image and the sample image, and performing image content recognition on the reference image and the sample image, to determine a content loss between the reference image and the sample image. The method also includes performing local style recognition on the reference image and the sample image, to determine a local style loss of the reference image and a local style loss of the sample image, training the generation network to obtain a trained generation network, and performing style conversion on a to-be-processed image by using the trained generation network.

Abstract: An image recognition method includes: obtaining a target three-dimensional (3D) image; inputting the target 3D image to a first recognition model; and obtaining the image type of the target 3D image outputted by the first recognition model. The first recognition model is configured to perform image recognition on the target 3D image to obtain an image type of the target 3D image. A convolutional block of the first recognition model is the same as a convolutional block of a second recognition model, and configured to perform image recognition on the target 3D image. The second recognition model is obtained by training an original recognition model using a target training sample, the target training sample including cubes obtained by rotating and sorting N target cubes obtained from a 3D sample image, N being a natural number greater than 1.

Abstract: The present disclosure provides methods, devices, apparatus, and storage medium for determining a target image region of a target object in a target image. The method includes: obtaining a target image comprising a target object; obtaining an original mask and an image segmentation model, the image segmentation model comprising a first unit model and a second unit model; downsampling the original mask based on a pooling layer in the first unit model to obtain a downsampled mask; extracting region convolution feature information of the target image based on a convolution pooling layer in the second unit model and the downsampled mask; updating the original mask according to the region convolution feature information; and in response to the updated original mask satisfying an error convergence condition, determining a target image region of the target object in the target image according to the updated original mask.

Abstract: Disclosed are a medical image segmentation method and apparatus, a device, a storage medium, and a program product, which relate to the field of artificial intelligence (AI). The method includes: performing image segmentation on a sample medical image through a source domain segmentation model, to obtain a first segmentation result, the source domain segmentation model being obtained through training based on image data in a source domain, the sample medical image being an unannotated image in a target domain; performing image segmentation on the sample medical image through a target domain segmentation model, to obtain a second segmentation result; correcting the first segmentation result based on the second segmentation result and a segmentation confidence level of the target domain segmentation model, to obtain a corrected segmentation result; and updating training on the target domain segmentation model based on the second segmentation result and the corrected segmentation result.

Abstract: A method for determining a target image to be labeled includes: obtaining an original image and an autoencoder (AE) set, the original image being an image having not been labeled, the AE set including N AEs; obtaining an encoded image set corresponding to the original image by using the AE set, the encoded image set including N encoded images, the encoded images being corresponding to the AEs; obtaining the encoded image set and a segmentation result set corresponding to the original image by using an image segmentation network, the image segmentation network including M image segmentation sub-networks, and the segmentation result set including [(N+1)*M] segmentation results; determining labeling uncertainty corresponding to the original image according to the segmentation result set; and determining whether the original image is a target image according to the labeling uncertainty.

Abstract: The present disclosure provides methods, devices, apparatus, and storage medium for determining a target image region of a target object in a target image. The method includes: obtaining a target image comprising a target object; obtaining an original mask and an image segmentation model, the image segmentation model comprising a first unit model and a second unit model; downsampling the original mask based on a pooling layer in the first unit model to obtain a downsampled mask; extracting region convolution feature information of the target image based on a convolution pooling layer in the second unit model and the downsampled mask; updating the original mask according to the region convolution feature information; and in response to the updated original mask satisfying an error convergence condition, determining a target image region of the target object in the target image according to the updated original mask.

Abstract: A CT image generation method and apparatus, a computer device, and a computer-readable storage medium are presented. The method includes obtaining a first X-ray image and a second X-ray image, the first X-ray image and the second X-ray image being X-ray images acquired for a target object from two orthogonal viewing angles; calling a generator to perform three-dimensional reconstruction on the first X-ray image and the second X-ray image, to obtain a three-dimensional model of the target object; and obtaining a CT image of the target object according to the three-dimensional model of the target object.

Abstract: An image segmentation method is provided for a computing device. The method includes obtaining a general tumor image, performing tumor localization on the tumor image to obtain a candidate image indicating a position of a tumor region in the general tumor image, inputting the candidate image to a cascaded segmentation network constructed based on a machine learning model, and performing image segmentation on the general tumor region in the candidate image using a first-level segmentation network and a second-level segmentation network in the cascaded segmentation network to obtain a segmented image.

Abstract: The present disclosure provides an artificial intelligence-based (AI-based) medical image processing method performed by a computing device, and a non-transitory computer-readable storage medium. The AI-based medical image processing method includes: processing a medical image to generate an encoded intermediate image; processing the encoded intermediate image, to segment a first feature and generate a segmented intermediate image; processing the encoded intermediate image and the segmented intermediate image based on an attention mechanism, to generate a detected intermediate input image; and performing second feature detection on the detected intermediate input image, to determine whether an image region of the detected intermediate input image in which the first feature is located comprises a second feature.

Abstract: An image generation method includes obtaining a modality image corresponding to a first modality, and performing modality conversion on the modality image through a first candidate network to obtain a generated image corresponding to a second modality different from the first modality. The generated image is a three-dimensional image. The method further includes performing modality restoration on the generated image through a second candidate network to obtain a restored image corresponding to the first modality and obtaining a constraint loss value based on a modality conversion effect of the generated image and a modality restoration effect of the restored image. The constraint loss value indicates a mapping loss in mapping the modality image to a three-dimensional image space by the first candidate network. The method also includes training the first candidate network based on the constraint loss value to obtain an image conversion network.

Abstract: The disclosure relates to methods, devices, systems, and computer storage medium for performing medical image segmentation. The method includes: The method includes: obtaining, by a device, a first medical image and a second medical image with a labeled region; performing, by the device, feature extraction on the first medical image and the second medical image respectively, to obtain first feature information of the first medical image and second feature information of the second medical image; obtaining, by the device, optical flow motion information from the second medical image to the first medical image according to the first feature information and the second feature information; and segmenting, by the device, the first medical image according to the optical flow motion information and the labeled region, to obtain a segmentation result of the first medical image.