Abstract: A method to automatically align magnetic resonance (MR) scans for diagnostic scan planning includes acquiring a three-dimensional (3D) localizer image of an anatomical object. One or more initial landmarks are identified in the 3D localizer image using a landmarking engine. One or more main axes associated with the anatomical object are identified based on the one or more initial landmarks. The 3D localizer image is registered to a canonical space based on the main axes associated to yield a registered 3D localizer image. The landmarking engine is applied to the registered 3D localizer image to yield one or more updated landmarks. A plurality of reference points for performing a MR scan are computed based on the one or more updated landmarks.

Abstract: Method and system for displaying one or more regions of interest of an original image. For example, a computer-implemented method for displaying one or more regions of interest of an original image includes: obtaining one or more detection results of one or more first regions of interest, each detection result of the one or more detection results corresponding to one first region of interest of the one or more first regions of interest, each detection result including image information and one or more attribute parameters for their corresponding first region of interest; and obtaining one or more attribute parameter thresholds provided by a user in real time, each attribute parameter threshold of the one or more attribute parameter thresholds corresponding to one attribute parameter of the one or more attribute parameters.

Abstract: A system for image segmentation is provided. The system may obtain a target image including an ROI, and segment a preliminary region representative of the ROI from the target image using a first ROI segmentation model corresponding to a first image resolution. The system may segment a target region representative of the ROI from the preliminary region using a second ROI segmentation model corresponding to a second image resolution. At least one model of the first and second ROI segmentation models may at least include a first convolutional layer and a second convolutional layer downstream to the first convolutional layer. A count of input channels of the first convolutional layer may be greater than a count of output channels of the first convolutional layer, and a count of input channels of the second convolutional layer may be smaller than a count of output channels of the second convolutional layer.

Abstract: Method and system for grading a medical image. For example, a system for grading a medical image comprising a grading network configured to provide a grading result corresponding to the medical image based on at least the medical image and/or a list of lesion candidates generated by a lesion identification network.

Abstract: Systems and methods for ossification center detection (OCD) and bone age assessment (BAA) may be provided. The method may include obtaining a bone age image of a subject. The method may include generating a normalized bone age image by preprocessing the bone age image. The method may include determining, based on the normalized bone age image, positions of a plurality of ossification centers using an ossification center localization (OCL) model. The method may include estimating, based on the normalized bone age image and information related to the positions of the plurality of ossification centers, a bone age of the subject using a bone age assessment (BAA) model.

Abstract: The present disclosure provides a region of interest (ROI) detection system. The system may be configured to acquire a target image and an ROI detection model, and perform ROI detection on the target image by applying the ROI detection model to the target image. The ROI detection model may be a trained cascaded neural network including a plurality of sequentially connected trained models. The plurality of trained models may include a trained first model and at least one trained second model downstream to the trained first model in the trained cascaded neural network. The plurality of trained models may be sequentially trained. Each of the trained second model may be trained using a plurality of training samples determined based on one or more trained models of the plurality of trained models generated before the generation of the trained second model.

Abstract: A method for assessing a condition of an organ or tissue of a target object is provided. The method may include: obtaining a target image of the target object; segmenting a target region from the target image, the target region of the target image corresponding to a sub-region of the organ or tissue; determining a morphological characteristic value of the target region in the target image; obtaining a reference standard associated with a sample organ or tissue of a plurality of sample objects, the sample organ or tissue being of a same type as the organ or tissue of the target object; and assessing the condition of the organ or tissue of the target object by comparing the morphological characteristic value of the target region in the target image with the reference standard.

Abstract: Systems and methods for ossification center detection (OCD) and bone age assessment (BAA) may be provided. The method may include obtaining a bone age image of a subject. The method may include generating a normalized bone age image by preprocessing the bone age image. The method may include determining, based on the normalized bone age image, positions of a plurality of ossification centers using an ossification center localization (OCL) model. The method may include estimating, based on the normalized bone age image and information related to the positions of the plurality of ossification centers, a bone age of the subject using a bone age assessment (BAA) model.

Abstract: A system for image segmentation is provided. The system may obtain a target image including an ROI, and segment a preliminary region representative of the ROI from the target image using a first ROI segmentation model corresponding to a first image resolution. The system may segment a target region representative of the ROI from the preliminary region using a second ROI segmentation model corresponding to a second image resolution. At least one model of the first and second ROI segmentation models may at least include a first convolutional layer and a second convolutional layer downstream to the first convolutional layer. A count of input channels of the first convolutional layer may be greater than a count of output channels of the first convolutional layer, and a count of input channels of the second convolutional layer may be smaller than a count of output channels of the second convolutional layer.

Abstract: The present disclosure may provide a method. The method may include processing an image of a subject using a detection model to generate one or more detection results corresponding to one or more objects in the image; and generating an image metric of the image based on the one or more detection results corresponding to the one or more objects.

Abstract: Systems and methods for ossification center detection (OCD) and bone age assessment (BAA) may be provided. The method may include obtaining a bone age image of a subject. The method may include generating a normalized bone age image by preprocessing the bone age image. The method may include determining, based on the normalized bone age image, positions of a plurality of ossification centers using an ossification center localization (OCL) model. The method may include estimating, based on the normalized bone age image and information related to the positions of the plurality of ossification centers, a bone age of the subject using a bone age assessment (BAA) model.

Abstract: The present disclosure provides systems and methods for image processing. The method may include obtaining an initial image; obtaining an intermediate image corresponding to the initial image, the intermediate image including pixels or voxels associated with at least a portion of a target object in the initial image; obtaining a trained processing model; and generating, based on the initial image and the intermediate image, a target image associated with the target object using the trained processing model.

Abstract: A system for image segmentation is provided. The system may obtain a target image including an ROI, and segment a preliminary region representative of the ROI from the target image using a first ROI segmentation model corresponding to a first image resolution. The system may segment a target region representative of the ROI from the preliminary region using a second ROI segmentation model corresponding to a second image resolution. At least one model of the first and second ROI segmentation models may at least include a first convolutional layer and a second convolutional layer downstream to the first convolutional layer. A count of input channels of the first convolutional layer may be greater than a count of output channels of the first convolutional layer, and a count of input channels of the second convolutional layer may be smaller than a count of output channels of the second convolutional layer.

Abstract: A method for assessing a condition of an organ or tissue of a target object is provided. The method may include: obtaining a target image of the target object; segmenting a target region from the target image, the target region of the target image corresponding to a sub-region of the organ or tissue; determining a morphological characteristic value of the target region in the target image; obtaining a reference standard associated with a sample organ or tissue of a plurality of sample objects, the sample organ or tissue being of a same type as the organ or tissue of the target object; and assessing the condition of the organ or tissue of the target object by comparing the morphological characteristic value of the target region in the target image with the reference standard.

Abstract: The present disclosure provides a region of interest (ROI) detection system. The system may be configured to acquire a target image and an ROI detection model, and perform ROI detection on the target image by applying the ROI detection model to the target image. The ROI detection model may be a trained cascaded neural network including a plurality of sequentially connected trained models. The plurality of trained models may include a trained first model and at least one trained second model downstream to the trained first model in the trained cascaded neural network. The plurality of trained models may be sequentially trained. Each of the trained second model may be trained using a plurality of training samples determined based on one or more trained models of the plurality of trained models generated before the generation of the trained second model.

Abstract: The present disclosure provides a system and method for classification determination of a structure. The method may include obtaining image data representing a structure of a subject. The method may also include determining a plurality of candidate classifications of the structure and their respective probabilities by inputting the image data into a classification model. The classification model may include a backbone network for determining a backbone feature of the structure, a segmentation network for determining a segmentation feature of the structure, and a density classification network for determining a density feature of the structure. The method may further include determining a target classification of the structure based on at least a part of the probabilities of the plurality of candidate classifications.

Abstract: A method for assessing a condition of an organ or tissue of a target object is provided. The method may include: obtaining a target image of the target object; segmenting a target region from the target image, the target region of the target image corresponding to a sub-region of the organ or tissue; determining a morphological characteristic value of the target region in the target image; obtaining a reference standard associated with a sample organ or tissue of a plurality of sample objects, the sample organ or tissue being of a same type as the organ or tissue of the target object; and assessing the condition of the organ or tissue of the target object by comparing the morphological characteristic value of the target region in the target image with the reference standard.

Abstract: Method and system for grading a medical image. For example, a system for grading a medical image comprising a grading network configured to provide a grading result corresponding to the medical image based on at least the medical image and/or a list of lesion candidates generated by a lesion identification network.

Abstract: Method and system for reducing a number of eigenvectors. For example, a computer-implemented method for reducing a number of eigenvectors, the method comprising: obtaining a plurality of to-be-processed matrices; mapping the plurality of to-be-processed matrices to a space of symmetric positive definite matrices to form a Riemannian manifold corresponding to a Riemannian kernel function; obtaining a kernel-function matrix by using at least a principal component analysis to calculate one or more inner products of the mapped plurality of matrices based on at least the Riemannian kernel function; calculating a first group of eigenvectors of the kernel-function matrix, the first group of eigenvectors including a first number of eigenvectors; and selecting one or more eigenvectors from the first group of eigenvectors to obtain a second group of eigenvectors, the second group of eigenvectors including a second number of eigenvectors; wherein the second number is less than the first number.

Abstract: Method and system for grading a tumor. For example, a system for grading a tumor comprising: an image obtaining module configured to obtain a pathological image of a tissue to be examined; a snippet obtaining module configured to obtain one or more snippets having one or more sizes from the pathological image; an analyzing module configured to obtain one or more classification features based on at least analyzing the one or more snippets using one or more selected trained detection models of the analyzing module, wherein each selected trained detection model is configured to identify one or more classification features; and an outputting module configured to determine a tumor identification result based on at least the one or more classification features and output the tumor identification result.