The invention discloses a contour
perception multi-organ segmentation
network construction method based on class-by-class
convolution operation. The contour
perception multi-organ segmentation networkconstruction method comprises the following steps: 1, performing region coarse segmentation and
edge detection on multiple organs of the
abdomen; 2, introducing a semantic-guided class-by-class multi-scale attention mechanism; step 3, performing class-by-class fusion of multi-
branch information; step 4, performing introduction of multi-task loss. According to the invention, the advantages of a
convolutional neural network and a gated recurrent neural unit are utilized, and for the characteristics and difficulties of a multi-organ segmentation task, via the contour information assisted multi-scale
feature extraction, a class-by-class multi-scale semantic attention mechanism, a class-by-class cavity
convolution fusion mechanism and a plurality of loss functions can be introduced to relievethe inter-
class imbalance problem of organs; multi-organ segmentation is performed on a three-dimensional CT image more efficiently and accurately, and the advantages of the invention are verified ona
data set containing 14 types of organ labels; the invention can be widely applied to computer-
aided diagnosis and treatment application, such as
endoscopic surgery,
interventional therapy and radiotherapy plan making.