Abstract: The present disclosure relates to an image registration method and a model training method thereof. The image registration method comprises obtaining a reference image and a floating image to be registered, performing image preprocessing on the reference image and the floating image, performing non-rigid registration on the preprocessed reference image and floating image to obtain a registration result image, and outputting the registration result image. The image preprocessing comprises performing, on the reference image and the floating image, coarse-to-fine rigid registration based on iterative closest point registration and mutual information registration. The non-rigid registration uses a combination of a correlation coefficient and a mean squared error between the reference image and the registration result image as a loss function.

Abstract: The present disclosure relates to a method, a system, and a storage medium for segmenting a lung image. The method for segmenting a lung image comprises: obtaining medical image data containing a lung region; performing lung lobe segmentation on the medical image data to generate a plurality of lung lobe data subsets; generating updated lung image data based on one or a plurality of lung lobe data subsets in the plurality of lung lobe data subsets; and performing nidus segmentation on the updated lung image data to generate a segmentation image that identifies a pneumonia nidus.

Abstract: The present disclosure relates to an image registration method and a model training method thereof. The image registration method comprises obtaining a reference image and a floating image to be registered, performing image preprocessing on the reference image and the floating image, performing non-rigid registration on the preprocessed reference image and floating image to obtain a registration result image, and outputting the registration result image. The image preprocessing comprises performing, on the reference image and the floating image, coarse-to-fine rigid registration based on iterative closest point registration and mutual information registration. The non-rigid registration uses a combination of a correlation coefficient and a mean squared error between the reference image and the registration result image as a loss function.

Abstract: The present approach relates to determining a reference value based on image data that includes a non-occluded vascular region (such as the ascending aorta in a cardiovascular context). This reference value is compared on a pixel-by pixel basis with the CT values observed in the other vasculature regions. With this in mind, and in a cardiovascular context, the determined FFR value for each pixel is the ratio of CT value in the vascular region of interest to the reference CT value.

Abstract: Embodiments of the present application provide a vascular plaque extraction apparatus and method. The method includes: acquiring a computed tomography angiography (CTA) image; performing preprocessing on the CTA image; performing a vascular lumen segmentation on the preprocessed CTA image to obtain a vascular lumen image; performing a dilation operation on the vascular lumen image to obtain a dilated region of interest (ROI), and performing a voxel-based radiomics feature extraction on the dilated ROI to obtain at least one voxel feature map; and extracting vascular plaques based on a preset threshold corresponding to the at least one voxel feature map and the at least one voxel feature map. According to the embodiments of the present application, the vascular plaques can be quickly and accurately extracted from the CTA image, providing a reference for an accurate quantitative analysis and auxiliary diagnosis and treatment.

Abstract: Provided in the present application are a method and a device for extracting a blood vessel wall, a medical imaging system, and a non-transitory computer-readable storage medium. The method for extracting a blood vessel wall comprises acquiring a medical image, determining at least one first-order feature in the medical image, and extracting, on the basis of the at least one first-order feature, a blood vessel wall image from the medical image.

Abstract: The present disclosure relates to a method, a system, and a storage medium for segmenting a lung image. The method for segmenting a lung image comprises: obtaining medical image data containing a lung region; performing lung lobe segmentation on the medical image data to generate a plurality of lung lobe data subsets; generating updated lung image data based on one or a plurality of lung lobe data subsets in the plurality of lung lobe data subsets; and performing nidus segmentation on the updated lung image data to generate a segmentation image that identifies a pneumonia nidus.

Abstract: Disclosed in the present disclosure are an image registration method and a model training method thereof. The image registration method comprises obtaining a reference image and a floating image to be registered, performing image preprocessing on the reference image and the floating image, performing non-rigid registration on the preprocessed reference image and floating image to obtain a registration result image, and outputting the registration result image. The image preprocessing comprises performing, on the reference image and the floating image, coarse-to-fine rigid registration based on iterative closest point registration and mutual information registration. The non-rigid registration uses a combination of a correlation coefficient and a mean squared error between the reference image and the registration result image as a loss function. Further disclosed in the present disclosure are an apparatus and a system for image registration and a computer-readable medium corresponding to the method.

Abstract: The present approach relates to determining a reference value based on image data that includes a non-occluded vascular region (such as the ascending aorta in a cardiovascular context). This reference value is compared on a pixel-by pixel basis with the CT values observed in the other vasculature regions. With this in mind, and in a cardiovascular context, the determined FFR value for each pixel is the ratio of CT value in the vascular region of interest to the reference CT value.

Abstract: Disclosed is a method for producing three-line hybrid wheat parent foundation seeds, including: determining whether sterile-line head progeny rows satisfy 100% sterile degree index of a sterile line or not by bagged selfing of the sterile-line head progeny rows, separately harvesting the bagged outcrossed heads satisfying the index to achieve cyclic breeding of sterile-line spike lines, and mixedly harvesting the sterile-line head progeny rows satisfying the index for sterile-line foundation seed plots; conducting test crossing on the separately harvested bagged outcrossed head seeds and the restorer-line head progeny rows, correspondingly numbering the restorer-line head progeny rows and bagged test-crossed heads, and separately harvesting bagged test-crossed seeds; conducting yield comparison on the separately harvested bagged test-crossed seeds, taking a part of preserved restorer-line head progeny row seeds meeting the requirement for cyclic breeding of restorer-line spike lines, and then planting the res