SC-Net:a contextual information skip connection network for overlapping chromosome segmentation
Objective Chromosome karyotype analysis separates and categorizes chromosomes in midcell division images,and it is widely used for the diagnosis of genetic diseases,in which overlapping chromosome segmentation is one of the key steps.Based on image analysis of overlapping chromosomes,the morphologically diverse chromosome clusters depend on detailed features,such as accurate boundaries during segmentation,in addition to obtaining the basic contour,texture,and semantic information.For this reason,in this paper,a two-stage overlapping chromosome segmentation model SC-Net was constructed through fusion of the contextual information of the target to improve the segmentation performance of the network.Method First,the model SC-UNet++added the hybrid pooling module(HPM)to the baseline model U-Net++for semantic segmentation to capture the local context information of overlapping chromosomes and complemented the detailed features of chromosomes,such as color,thickness,and stripes,based on the superposition operation of empty space pyra-mid pooling and stripe pooling.The context fusion module(CFM)was connected in parallel to a decoder network,i.e.,the channel correlation of input features was extracted using the efficient channel attention module,and the features obtained via the multiplication of the output with the input were subsequently fed to the HPM and the spatial attention mod-ule(SAM),which explored the correlation of the region around the pixel to obtain the local context and extract the global context through global pooling operation,respectively.In addition,context prior auxiliary branch(CPAB)was introduced after CFM to improve the global context information on channel and space.Second,the category a priori information of labeled training samples,which serves as an additional source of supervisory information during training and effectively dis-tinguishes confusing spatial features in overlapping chromosome images,was used to generate the true affinity matrix.Finally,the elements of overlapping and non-overlapping regions were iteratively paired by the chromosome instance recon-struction algorithm to splice and form a single chromosome.In this paper,experimental analysis were based on ChromSeg dataset,and the hardware resources used included a desktop server with 32 GB RAM,a 3.3 GHz Intel Xeon CPU,and an NVIDIA RTX 3070 GPU.The model was used based on the semantic segmentation toolkit MMSegmentation version 0.30.0 and implemented under the Ubuntu 18.04 operating system,with PyTorch 1.10.0 serving as a deep learning framework.The following sections describe relevant hyperparameter settings and initialization methods for network training and the loss function selection strategy.Result SC-Net fully extracted and utilized the contextual and category prior informa-tion of overlapping chromosome images and showed good performance in segmentation scenarios with various numbers of overlapping chromosomes.The effect of each improvement on the algorithm performance was investigated through ablation experiments,where various combinations of CFM,HPM,CPAB,and segmentation loss were designed on the baseline model U-Net++.The results proved the better performance of SC-UNet++compared with models in all evaluation metrics.This condition confirms the effectiveness of the method proposed in this paper,i.e.,SC-UNet++attained better perfor-mance in the segmentation of overlapping chromosomes.Through comparative experiments,the SC-Net proposed in this paper caused improvements on the ChromSeg dataset,which outperformed several models in terms of all metrics,and the model achieved an overlapping chromosome region intersection and merger ratio of 83.5％.The overall accuracy obtained after the reconstruction of chromosome instances was 92.3％,which is higher than the best and the same two-stage Chrom-Seg segmentation methods by 2.7％and 1.8％.The SC-Net outperformed these models mainly due to its capability to extract contextual information and category relevance of the target,and it enables the model to further gain insights into overlapping regions.Conclusion The overlapping chromosome segmentation model constructed in this paper can effec-tively solve the segmentation problem of morphologically diverse overlapping chromosome clusters by fusing contextual information and obtaining finer and more accurate results compared with the existing methods.
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