An automatic delineation method for tumor target area images of esophageal cancer based on an improved spatial pyramid model
A space pyramid and esophageal cancer technology, which is applied in image data processing, biological neural network model, image analysis, etc., can solve problems such as automatic delineation of difficult esophageal cancer tumor target images, and achieve improved segmentation effect and accurate automatic segmentation.
Active Publication Date: 2022-05-10
ANHUI MEDICAL UNIV +1
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[0007] The purpose of the present invention is to solve the defect in the prior art that it is difficult to automatically delineate esophageal cancer tumor target images of small data sets, and to provide an automatic delineation method for small data set esophageal cancer tumor target images based on an improved spatial pyramid model to solve the above problems
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[0087] In order to have a further understanding and understanding of the structural features of the present invention and the achieved effects, the preferred embodiments and accompanying drawings are used for a detailed description, as follows:
[0088] Such as figure 1 As shown, a method for automatically delineating tumor target area images of esophageal cancer in a small data set based on an improved spatial pyramid model according to the present invention comprises the following steps:
[0089] The first step is the acquisition and preprocessing of the small data training set: obtain the CT image data of patients with esophageal cancer who have undergone intensity-modulated radiotherapy, and export the tumor target area as a Mask file; perform normalization and data enhancement preprocessing on the CT image data . The specific steps are as follows:
[0090] (1) Read the original CT image in Dicom format.
[0091] (2) Generate the Mask image of the tumor target area of ...
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The invention relates to an automatic delineation method for small data set esophageal cancer tumor target area images based on an improved spatial pyramid model, which solves the defect that it is difficult to automatically delineate esophageal cancer tumor target area images for small data sets compared with the prior art. The present invention comprises the following steps: acquisition and preprocessing of small data training set; establishment of automatic delineation model of esophageal cancer tumor target area image; training of automatic delineation model of esophageal cancer tumor target area image; acquisition of image to be delineated; Obtaining the result of area image delineation. The present invention can effectively solve the problem of over-fitting caused by small data sets, and the low segmentation accuracy caused by the small tumor target area and variable shape of CT images of esophageal cancer, thereby improving the segmentation effect of the target target area and making it more accurate The automatic segmentation of esophageal cancer tumor target area makes the delineation of esophageal cancer tumor target area more universal.
Description
technical field [0001] The invention relates to the technical field of medical image processing, in particular to a method for automatically delineating images of tumor target areas of esophageal cancer in small data sets based on an improved spatial pyramid model. Background technique [0002] Esophageal cancer (esophageal cancer, EC) is a highly aggressive malignant tumor, and its incidence is on the rise worldwide, especially in China. Currently, the 5-year survival rate is only 15% to 25%. Surgical resection is the treatment of choice for esophageal cancer, but the recurrence rate after radical resection is still high. Local recurrence is the main reason for treatment failure, and postoperative radiotherapy is the main treatment to control local recurrence and prolong survival. [0003] The basic principle of computer tomography (Computed Tomography, CT) is image reconstruction. According to the characteristic of X-ray absorption of various human tissues (including nor...
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IPC IPC(8): G06T7/11G06T7/73G16H30/20G06N3/04
CPCG06T7/11G06T7/73G16H30/20G06T2207/10081G06T2207/20016G06T2207/30096G06T2207/20081G06T2207/20084G06N3/045
Inventor 黄晓雨黄勇汪琦张梅吴齐兵
Owner ANHUI MEDICAL UNIV