Point interactive medical image segmentation method based on deep neural network

Abstract

The invention provides a point interaction deep learning segmentation algorithm specially for solving the kidney tumor segmentation problem in a medical image. The algorithm is composed of a point interaction preprocessing module, a bidirectional ConvRNN unit and a core deep segmentation network. The algorithm starts from a tumor center position provided by an expert; in 16 directions with uniformintervals, 16 image blocks with the size of 32 * 32 are intensively collected from inside to outside according to the step length of 4 pixels to form an image block sequence, a deep segmentation network with sequence learning is used for learning the inside and outside change trend of a target, the edge of the target is determined, and segmentation of the kidney tumor is achieved. The method canovercome the influences of low contrast, variable target positions and fuzzy target edges of medical images, and is suitable for organ segmentation and tumor segmentation tasks. Compared with the prior art, the method has the following characteristics: 1) the interaction mode is simple and convenient; (2) a Sequence Patch Learning concept is provided, and a sequence image block is used for capturing a long-range semantic relationship, so that a relatively large receptive field can be obtained even in a relatively shallow network; and 3) a brand-new ConvRNN unit is provided, the inside and outside change trend of the target is learned, the interpretability is relatively high, the actual working mode of doctors is met, and the final model is high in precision and strong in applicability.

Description

technical field [0001] The invention relates to a point-interactive kidney tumor CT segmentation method based on a deep neural network, which belongs to the field of computer applications. Background technique [0002] The kidney is an important organ of the human body. Once the kidney function is damaged, a variety of metabolic end products will accumulate in the body, which will affect life safety. Among various kidney diseases, renal tumor is the number one threat to kidney health. At present, CT imaging examination is one of the main examination methods for kidney diseases such as renal tumors. According to the size of the renal tumor, doctors can grade the severity of the tumor and formulate corresponding treatment methods; at the same time, localizing the renal tumor, analyzing its shape and size, and accurately delineating and delineating the tumor target area are also important steps in the radiation therapy process . [0003] Manually depicting and delineating re...

AI Technical Summary

Problems solved by technology

The difficulty of this type of method is that the collected image blocks are small, the relationship between image blocks is not considered, and the semantic information in a larger field of view is ignored, resulting in poor segmentation results.

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