Breast Cancer Related LVI Status Prediction Based on Active Contour Model and Radiomics

doi:10.12068/j.issn.1005-3026.2020.02.008

Abstract

Abstract: To predict preoperative lymphovascular invasion(LVI)status of breast cancer patients， a computer-aided analysis method which combines an active contour model and radiomics is proposed. First， the image from dynamic contrast enhanced magnetic resonance imaging(DCE-MRI)of breast cancer is segmented by an active contour model(ACM)based on post probability and fuzzy velocity function. By constructing the region term of the active contour model based on post probability in the wavelet domain and the edge stop term of the active contour model by using the fuzzy velocity function， the accuracy of breast cancer lesion segmentation can be improved. Second， the image features such as morphology， grayscale and texture are extracted. Finally， a model for predicting LVI status is developed by the random forest classifier and its predictive ability is verified by experimental results.

Key words: DCE-MRI, breast cancer, active contour model, radiomics, image segmentation, image classification

CLC Number:

TP391.41

FENG Bao， LI Chang-lin， LI Zhi， LIU Zhuang-sheng. Breast Cancer Related LVI Status Prediction Based on Active Contour Model and Radiomics[J]. Journal of Northeastern University Natural Science, 2020, 41(2): 193-199.

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