Time series based treatment effect evaluation method and electronic device

A treatment effect, time series technology, applied in the field of imaging, can solve the problem of low artificial diagnosis rate and save time and energy

Inactive Publication Date: 2018-11-27
中山仰视科技有限公司
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AI Technical Summary

Problems solved by technology

[0004] In order to overcome the deficiencies in the prior art, one of the purposes of the present invention is to provide a time-series-based therapeutic effect evaluation method, which can solve the problem o

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  • Time series based treatment effect evaluation method and electronic device

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Embodiment Construction

[0058] Below, in conjunction with accompanying drawing and specific embodiment, the present invention is described further:

[0059] like figure 1 As shown, the present invention provides a method for evaluating the therapeutic effect based on time series, which specifically includes the following steps:

[0060] S1: Obtain lung CT image data, the lung CT image data is composed of several lung slices;

[0061] The lung CT image data used in the present invention include LUNAdata and Data Science Bowl2017stage1data. The LUNA data is in MHD format, and the data is read by the SimpleITK tool. The Data Science Bowl 2017 data is in the DICOM format, and the data is read by the dicom tool.

[0062] S2: Perform a preprocessing operation on the lung CT image data;

[0063] In this step, read lung CT image data;

[0064]Calculate the geometric distance from any point in each slice to the center point, and remove the geometric distance from the point to the center point whose geometr...

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Abstract

The invention discloses a time series based treatment effect evaluation method. The time series based treatment effect evaluation method comprises the steps of: acquiring lung CT image data, wherein the lung CT image data is composed of a plurality of lung sections; performing pre-processing on lung CT image data; designing a lung nodule detection model according to the pre-processed lung CT imagedata; designing a lung nodule classification model; designing a time axis based lung cancer prediction model according to the lung nodule detection model; and training the lung nodule detection modelaccording to the number of samples per batch. Based on the deep learning, a time series is added, the method dynamically analyzes the changes of the lung nodules, so that a doctor can more accuratelydiagnose a treatment effect of the lung cancer and predict the early recurrence. The diagnosis time of the algorithm is far less than the doctor's manual diagnosis time, and the important steps of the doctor's diagnosis can be assisted in a diagnosis process, which can save a lot of time and energy of the doctor.

Description

technical field [0001] The invention relates to imaging technology, in particular to a treatment effect evaluation method and electronic equipment based on time series. Background technique [0002] Lung cancer is one of the malignant tumors with the highest incidence rate among malignant tumors, and its five-year survival rate is only about 15%. During the treatment and reexamination of patients, doctors need to review CT images of multiple regular physical examinations to understand the change process of pulmonary nodules, the treatment effect of lung cancer, and the probability of lung cancer recurrence. When doctors study and judge each nodule of a patient, they have to track and evaluate the change of each nodule in time, which consumes a lot of time and energy, and it is easy to miss diagnosis and misdiagnose. [0003] Someone proposed the use of computer-aided diagnosis in the 1960s, using the large amount of computing power of the computer to help doctors diagnose. ...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06N3/04
CPCG06T7/0012G06T2207/30096G06T2207/30064G06T2207/10081G06N3/045
Inventor 周志光
Owner 中山仰视科技有限公司
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