Pancreas segmentation method and system based on deep convolutional neural network

Abstract

The invention provides a pancreas segmentation method based on a deep convolutional neural network. The pancreas segmentation method comprises the following steps: acquiring a computed tomography image at the pancreas of a patient; preprocessing a computed tomography image, then inputting the preprocessedcomputed tomography image into the trained deep convolutional neural network model, and enabling the trained deep convolutional neural network model to carry out automatic segmentation and obtain a preliminary pancreas segmentation result; carrying out implicit contour simulation on the preliminary pancreas segmentation result through a distance regularization level set, determining a final pancreas boundary through an optimization algorithm, and obtaining a final pancreas segmentation result after noise reduction and normalization processing are conducted; sending a region of interest into three neural networks for preliminary segmentation, performing data enhancement on a two-dimensional pancreas image to obtain enough training and verification data, and obtaining a preliminary segmentation result of a pancreas region, so that a segmentation boundary of a pancreas is clear and smooth, and prior knowledge of anatomy can be simply and conveniently added into a segmentation model.

Description

technical field [0001] The present disclosure relates to the field of medical image processing, in particular to a pancreas segmentation method and system based on a deep convolutional neural network. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art. [0003] The pancreas is a glandular organ located deep in the abdominal cavity that plays a central role in both digestion and glucose metabolism. Pancreatic diseases can be divided into exocrine pancreatic diseases and endocrine pancreatic diseases. It is estimated that approximately 40,000 pancreatic cancer deaths and 50,000 new cases occur each year in the United States alone. The prognosis of patients with pancreatic cancer is also dismal - the 5-year survival rate is less than 5%. In medical images, automatic segmentation of the pancreas is a prerequisite for many medical applications, such as...

AI Technical Summary

Problems solved by technology

[0006] Although Deep Convolutional Neural Network (DCNN) can be used to efficiently segment medical images such as pancreas and achieve good segmentation results, the following problems still need to be solved: 1) The segmentation boundary of pancreas is rough or not smooth enough ; 2) It is difficult to add prior knowledge of anatomy to the segmentation model

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