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124 results about "Multi organ" patented technology

Multi-organ transplants are surgical procedures in which two or more failing organs are replaced with healthy ones, usually — but not always — from the same deceased donor and in one continuous series of operations.

Abdomen multi-organ nuclear magnetic resonance image segmentation method and system based on FCN and medium

The invention discloses an abdominal multi-organ nuclear magnetic resonance image segmentation method and system based on FCN, and a medium. The abdominal multi-organ nuclear magnetic resonance imagesegmentation method comprises the following implementation steps: acquiring an input image and carrying out data preprocessing and image normalization operation; inputting the normalized abdominal multi-organ nuclear magnetic resonance image into a trained high-resolution full convolutional neural network model to obtain a final prediction image, wherein the high-resolution full convolutional neural network model is pre-trained to establish a mapping relationship between the normalized abdominal multi-organ nuclear magnetic resonance image and the corresponding final prediction image; and activating the final prediction graph by using an activation function to obtain a prediction score graph, and taking a category with the highest prediction score at each pixel position as a prediction label category of the pixel position to obtain a final segmentation prediction graph. According to the abdominal multi-organ nuclear magnetic resonance image segmentation method, automatic segmentation of the abdominal multi-organ nuclear magnetic resonance image can be realized, for example, the abdominal multi-organ MR image is segmented according to five different region types of an organ-free region, a liver region, a right kidney region, a left kidney region and a spleen region.
Owner:SUN YAT SEN UNIV

Contour perception multi-organ segmentation network construction method based on class-by-class convolution operation

ActiveCN112465827AGood for Semantic SegmentationGood for edge detectionImage enhancementImage analysisData setMulti organ
The invention discloses a contour perception multi-organ segmentation network construction method based on class-by-class convolution operation. The contour perception multi-organ segmentation networkconstruction method comprises the following steps: 1, performing region coarse segmentation and edge detection on multiple organs of the abdomen; 2, introducing a semantic-guided class-by-class multi-scale attention mechanism; step 3, performing class-by-class fusion of multi-branch information; step 4, performing introduction of multi-task loss. According to the invention, the advantages of a convolutional neural network and a gated recurrent neural unit are utilized, and for the characteristics and difficulties of a multi-organ segmentation task, via the contour information assisted multi-scale feature extraction, a class-by-class multi-scale semantic attention mechanism, a class-by-class cavity convolution fusion mechanism and a plurality of loss functions can be introduced to relievethe inter-class imbalance problem of organs; multi-organ segmentation is performed on a three-dimensional CT image more efficiently and accurately, and the advantages of the invention are verified ona data set containing 14 types of organ labels; the invention can be widely applied to computer-aided diagnosis and treatment application, such as endoscopic surgery, interventional therapy and radiotherapy plan making.
Owner:BEIHANG UNIV

Multi-organ segmentation method based on self-supervised feature small sample learning

PendingCN113706487ASolve the problem of not being able to trainReduce distractionsImage enhancementImage analysisDiseaseData set
The invention discloses a multi-organ segmentation method based on self-supervised feature small sample learning, and mainly solves the problem of poor multi-organ segmentation effect by using a small sample learning segmentation method in the prior art. According to the scheme, the method comprises the steps of: using a superpixel segmentation method for generating a large amount of data containing pseudo labels from an initial data set, and selecting images and the pseudo labels from the data to serve as a support set; generating a query set by adopting a data enhancement method; extracting image features of the support set and the query set through a pre-trained encoder by using self-supervised feature learning, and then calculating the similarity of the image features to obtain foreground information and prior probability auxiliary information feature maps; constructing a segmentation network to carry out feature refining on the foreground information to obtain a support set prototype; and calculating a classification probability according to the support set prototype and the prior probability auxiliary information feature map to obtain a segmentation result. The method reduces over-segmentation and under-segmentation phenomena of large target organs, improves recognition of small target organs, can be used for multi-organ segmentation of medical images, and assists doctors in diagnosing diseases.
Owner:XIDIAN UNIV
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