Abstract: A method and system for segmenting three-dimensional images of pancreases and tumors, including: acquiring 3D images of the pancreas and the tumor and preprocessing same with a soft tissue window to control the intensity value of the image within a set range; cropping all images into block-shaped regions of a set size and feeding same into a trained convolutional neural network model, and when training a network, dynamically adjusting the learning weight between the pancreas and the tumor under the guidance of temperature to learn the features of the pancreas and the tumor; and after performing data preprocessing on the 3D images of the pancreas and labels, using the trained network model for online testing and evaluating, and outputting the segmentation result.

Abstract: A system for pancreatic cancer image segmentation based on a multi-view feature fusion network, comprising inputting acquired computed tomography (CT) image into network model, extracting shallow features of CT image to obtain shallow feature map; reconstructing the shallow feature map into code sequence and inputting into lightweight Transformer for extracting global dependency relationship; carrying out operations on the shallow feature map to obtain first feature map; fusing the global dependency relationship and the first feature map to obtain second feature map; discarding redundant information from feature map obtained through convolution operation of each layer of the network, and regarding top-level feature map with discarded redundant information as third feature map; and carrying out pooling operations on the third feature map to obtain predicted boundary circle of target region, adding regularization item to the predicted boundary circle to obtain reference boundary circle, then outputting result.

Abstract: Disclosed are a method, system, and device for removing smoke from laparoscope images based on a conditional diffusion model. The method includes: segmenting a video of a laparoscopic surgery according to the number of frames to form a data set; performing smoke rendering on the obtained laparoscope smokeless images, and synthesizing paired smoky images to obtain a synthetic data set containing the smokeless images and the smoky images; inputting the smokeless images into the conditional diffusion model for forward noise addition, and continuously adding noise until the smokeless images are completely noised; inputting the smoky images into a smoke sensing module to obtain smoke concentration and position information, then training a neural network, and continuously performing reverse denoising on the completely noised images using the trained neural network until clear smokeless images are outputted; and optimizing a smoke removal model through a multi-loss function fusion strategy.

Abstract: Disclosed are a method, system, and device for removing smoke from laparoscope images based on a conditional diffusion model. The method includes: segmenting a video of a laparoscopic surgery according to the number of frames to form a data set; performing smoke rendering on the obtained laparoscope smokeless images, and synthesizing paired smoky images to obtain a synthetic data set containing the smokeless images and the smoky images; inputting the smokeless images into the conditional diffusion model for forward noise addition, and continuously adding noise until the smokeless images are completely noised; inputting the smoky images into a smoke sensing module to obtain smoke concentration and position information, then training a neural network, and continuously performing reverse denoising on the completely noised images using the trained neural network until clear smokeless images are outputted; and optimizing a smoke removal model through a multi-loss function fusion strategy.

Abstract: A laparoscopic image smoke removal method based on a generative adversarial network, and belongs to the technical field of computer vision. The method includes: processing a laparoscopic image sample to be processed using a smoke mask segmentation network to acquire a smoke mask image; inputting the laparoscopic image sample to be processed and the smoke mask image into a smoke removal network, and extracting features of the laparoscopic image sample to be processed using a multi-level smoke feature extractor to acquire a light smoke feature vector and a heavy smoke feature vector; and acquiring, according to the light smoke feature vector, the heavy smoke feature vector and the smoke mask image, a smoke-free laparoscopic image by filtering out smoke information and maintaining a laparoscopic image by using a mask shielding effect. The method has the technical effects of robustness and ability of being embedded into a laparoscopic device for use.

Abstract: A laparoscopic image smoke removal method based on a generative adversarial network, and belongs to the technical field of computer vision. The method includes: processing a laparoscopic image sample to be processed using a smoke mask segmentation network to acquire a smoke mask image; inputting the laparoscopic image sample to be processed and the smoke mask image into a smoke removal network, and extracting features of the laparoscopic image sample to be processed using a multi-level smoke feature extractor to acquire a light smoke feature vector and a heavy smoke feature vector; and acquiring, according to the light smoke feature vector, the heavy smoke feature vector and the smoke mask image, a smoke-free laparoscopic image by filtering out smoke information and maintaining a laparoscopic image by using a mask shielding effect. The method has the technical effects of robustness and ability of being embedded into a laparoscopic device for use.

Abstract: A method and system for segmenting three-dimensional images of pancreases and tumors, including: acquiring 3D images of the pancreas and the tumor and preprocessing same with a soft tissue window to control the intensity value of the image within a set range; cropping all images into block-shaped regions of a set size and feeding same into a trained convolutional neural network model, and when training a network, dynamically adjusting the learning weight between the pancreas and the tumor under the guidance of temperature to learn the features of the pancreas and the tumor; and after performing data preprocessing on the 3D images of the pancreas and labels, using the trained network model for online testing and evaluating, and outputting the segmentation result.