The invention discloses a spine Cobb angle automatic measurement method and device based on fine shape characterization and
dynamic data amplification, and aims to construct an end-to-end
deep learning framework for automatic measurement and
structured analysis requirements of a Cobb angle in a spine X-
ray image so as to realize integrated spine contour reconstruction and
lateral bending angle prediction. According to the method, firstly,
bone structure extraction and contour key point detection are performed on a spine X-
ray image in an original
training set, so that the contour shape of each segment of the spine is finely represented by using key points; constructing a
training set spine segment contour matrix, and estimating robust contour shape subspace representation through robust subspace
recovery; then, constructing a spine contour detection and regression network fusing multi-scale
feature extraction and a sparse-dense sampling strategy, and realizing segment region positioning and basis vector regression; and reconstructing a spine contour through a coefficient so as to calculate a direction included angle between adjacent segments and an overall Cobb angle. Through robust subspace modeling, multi-scale
feature fusion and circular
data optimization strategies, the problems of discontinuous contour, large angle
estimation error and data scarcity in traditional Cobb angle measurement are effectively solved, and the accuracy, stability and clinical availability of spine shape analysis are remarkably improved.