Overlapped chromosome segmentation network based on multi-scale feature extraction

The invention provides a multi-scale U-shaped convolutional neural network MACS Net in order to solve the problems that target segmentation regions in overlapped chromosome images are different in size, not obvious in distinguishing and the like. Multi-layer cavity convolution and synchronous long pooling technology is introduced at the bottommost layer of UNet to realize detection of target segmentation regions of different sizes and extraction of features; convolution block connection is introduced between UNet codecs, so that semantic information difference is relieved. An intersection-to-parallel ratio (IoU) of chromosome overlapping regions is used as an evaluation index; the result shows that the segmentation IOU of the MACS Net at the chromosome overlapping part reaches 0.9860, which is improved by 2.78% compared with 0.99593 of UNet, and the MACS Net respectively shows more ideal noise robustness in the data set polluted by spiced salt, Gauss and Poisson noise. 本发明针对重叠染色体图像中目标分割区域大小不一且区分不明显等问题，提出一种多尺度