A Pancreatic CT Image Segmentation Method Based on Integrated Deep Convolutional Neural Network

Abstract

The invention discloses a pancreas CT image segmentation method based on an integrated deep convolutional neural network, which uses a coarse-to-fine two-stage segmentation framework to accurately segment the pancreas in the CT image. First, a CNN network with a three-dimensional U-shaped encoding-decoding structure that introduces attention modules and cross-level dense connections is constructed, that is, the Unet model is used as a recognition network in two stages of pancreas image segmentation; in the rough segmentation stage, the original image is reduced. Sampling and normalization preprocessing, and then randomly select several data blocks as the input of the network for training, and obtain the rough segmentation result of the pancreas; in the fine segmentation stage, use the bounding box to contain the pancreas area, and take image blocks in the bounding box area for training; During identification, the region of the pancreas is determined using the coarse segmentation results, and then the fine segmentation is used for prediction to obtain the fine segmentation results. Finally, the results of the two stages are voted to obtain the segmentation results. The invention overcomes the problem of manual labeling and obtains a relatively ideal segmentation result.

Description

technical field [0001] The invention belongs to the medical field, and in particular relates to a CT image segmentation method. Background technique [0002] Pancreatic cancer is a highly malignant tumor of the digestive system. Its early clinical symptoms are relatively hidden, and most of them are discovered at an advanced stage, so the prognosis is often poor. Although the overall incidence of pancreatic cancer is low among all malignant tumors, But the mortality rate is in the forefront, and the incidence rate has an upward trend in recent years. Using the existing computer-aided diagnosis system to automatically and accurately segment the pancreas in CT images can greatly facilitate the assessment of pancreatic diseases. However, due to the particularity of the pancreas, the segmentation effect is often far from the ideal situation. The difficulty of pancreas segmentation mainly lies in: the serious category imbalance problem, that is, the proportion of pancreas in the...

AI Technical Summary

Problems solved by technology

However, due to the particularity of the pancreas, the segmentation effect is often far from the ideal situation. The difficulty of pancreas segmentation mainly lies in: the serious category imbalance problem, that is, the proportion of pancreas in the entire CT image is often less than 1%. At the same time, the anatomy of the pancreas is relatively complex, and there is a visually blurred interclass boundary relative to other tissues

For example, Roth et al. used the high representation ability of the CNN model to image features to effectively segment the pancreatic tissue in the CT image. They took the lead in using the Full Convolution Network (FullConvolution Network, FCN) for the segmentation of the pancreas, and demonstrated the CNN model. Potential in medical image segmentation, but whether it is a CNN model based on 2D convolution or 3D convolution, there are certain defects

The CNN model based on 2D convolution can only process two-dimensional CT image slices, and cannot learn the three-dimensional information contained in the overall CT image.

The CNN model based on 3D convolution uses 3D data blocks as input. This method fully pays attention to the context information in 3D CT images, but 3D image features often need to occupy a huge video memory, thus limiting the size of the input data. At the same time, due to the unbalanced category of pancreas segmentation, randomly selected 3D image blocks often cannot contain enough positive samples to effectively train the network.

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