Shanghai United Imaging Intelligence Co., Ltd.

Yaozong Gao

Yiqiang Zhan

Yanbo Chen

Wenhai Zhang

Yu Zhang

Jiyong Wang

Ying Shao

Miaofei Han

Weiyang Xie

Dijia Wu

A method for lung nodule evaluation is provided. The method may include obtaining a target image including at least a portion of a lung of a subject. The method may also include segmenting, from the target image, at least one target region each of which corresponds to a lung nodule of the subject. The method may further include generating an evaluation result with respect to the at least one lung nodule based on the at least one target region.

Systems and methods for lung nodule evaluation

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.

Systems and methods for image segmentation

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.

Systems and methods for detecting region of interset in image

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.

Systems and methods for image processing

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.

System and method for classification determination

Method and system for displaying a medical image. For example, a computer-implemented method for displaying a medical image includes acquiring an original image of a target; obtaining a lesion region in the original image; selecting a region of interest in the original image based on at least the lesion region, the region of interest including the lesion region; obtaining a plurality of planar images of the region of interest from the original image of the target based on at least a predetermined setting; generating an animated display by grouping the plurality of planar images based on at least a predetermined order; and displaying the animated display related to the region of interest including the lesion region.

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Shanghai, China

Yaozong Gao