Pulmonary nodule detection and analysis method and system adaptive to layer thickness
An adaptive layer and analysis method technology, applied in the medical field, can solve one or more problems that cannot guarantee the detection accuracy, and achieve the effect of enriching nodule information
Pending Publication Date: 2021-01-19
SHENZHEN IMSIGHT MEDICAL TECH CO LTD
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[0006] In view of the above technical problems, the embodiment of the present invention provides an adaptive layer thickness analysis method and system to solve one or more problems that traditional CT images cannot guarantee the detection accuracy when the thickness difference is large
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[0023] In order to facilitate the understanding of the present invention, the present invention will be described in more detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that when an element is said to be "fixed" to another element, it may be directly on the other element, or there may be one or more intervening elements therebetween. When an element is referred to as being "connected to" another element, it can be directly connected to the other element or one or more intervening elements may be present therebetween. The orientation or positional relationship indicated by the terms "upper", "lower", "inner", "outer", and "bottom" used in this specification is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the The invention and the simplified description do not indicate or imply that the device or element referred to must have a specific orientation...
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The invention relates to the technical field of medical treatment, in particular to a pulmonary nodule detection and analysis method and system adaptive to the layer thickness, and the method comprises the following steps: reading a CT image of a lung, and determining a target deep learning network model according to the thickness of each layer in the CT image; training a target deep learning network model, and inputting a tested CT image into the pulmonary nodule segmentation and detection model to obtain an initial pulmonary nodule detection result of the first stage; and removing false positive pulmonary nodules by using a 3D deep learning classifier based on deep learning to obtain a final pulmonary nodule detection result of the second stage. According to the nodule detection method and system based on deep learning provided by the embodiment of the invention, the 3D and 2D two-stage deep learning neural network models are respectively selected according to the thickness characteristics of each layer in the CT image, and the image characteristics of the CT images with different layer thicknesses can be better utilized, so that accurate detection and segmentation of various layer thickness data are achieved.
Description
technical field [0001] The invention relates to the field of medical technology, in particular to an adaptive layer thickness detection and analysis method and system for pulmonary nodules. Background technique [0002] For the detection of pulmonary nodules, traditional methods use morphological segmentation algorithms and manual feature extraction algorithms to detect and segment pulmonary nodules. However, due to the influence of different nodule shapes and densities, the detection and segmentation accuracy cannot meet the needs of clinical use. . [0003] For example, patent CN102842132B proposes a CT pulmonary nodule detection method, which first uses edge detection and spherical and current enhancement filters to enhance nodules and blood vessel regions, and then uses threshold segmentation to obtain initial candidate nodule images. Calculate 3D and 2D features for the initial nodule image, such as shape, grayscale, curvature, eccentricity, Fourier descriptor, etc., a...
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IPC IPC(8): G06T7/00G06T7/11G06T7/136G06T7/155G06T7/62G06N3/04
CPCG06T7/0012G06T7/11G06T7/62G06T7/136G06T7/155G06T2207/30064G06T2207/10081G06N3/045
Inventor 陈浩李雁雯王春永
Owner SHENZHEN IMSIGHT MEDICAL TECH CO LTD