# ChroSegNet: An Attention-Based Model for Chromosome Segmentation with Enhanced Processing
###### Abstract
With rapid technological advancement, deep learning technology has achieved remarkable results in various fields including computer vision and image processing [9]. Convolutional Neural Network (CNN) is a class of deep neural networks with convolutional structure, which has excellent feature extraction ability, self-learning ability, and low computational cost. In the past decade, CNNs have excelled in various fields of computer vision. Several CNNs including AlexNet [10], Resnet [11], and VggNet [12] have been applied to image classification, image segmentation, and achieved advanced superior performance. In the literature of medical image segmentation, a large number of CNN-based medical image segmentation models have been proposed. Most such models are designed for semantic segmentation [13], e.g., segmenting the cell contours. The task of chromosome image segmentation in this work, however, is to segment (and classify) each chromosome instance. In fact, there is a pressing need to develop the instance-level segmentation models in the field of chromosome segmentation because of its significant applications as discussed earlier. Figure 1: A chromosome microscopic image (**a**) and the corresponding karyotyping image (**b**). Specifically, the challenge of chromosome instance segmentation stems from four main points [14]: (1) chromosome data belongs to medical privacy data, which is difficult to obtain, limited in quantity and quality; (2) the chromosomes are prone to distortion, and the same type of chromosomes still have deformation differences; (3) each chromosome image contains a large number of chromosomes, and (4) contacts and overlaps between chromosomes are easy to occur. Because of the above challenges, the existing segmentation networks cannot efficiently complete the task of instance-level chromosome segmentation. In view of the lack of data sources, we worked with the National Engineering Laboratory of Key Technologies for Birth Defect Prevention and Control to obtain chromosome data. However, the quantity and quality of the original chromosome data are far from enough for the experimental standards. Therefore, we designed an enhanced processing pipeline to chromosome data, which assists in our construction of a large-scale chromosome segmentation dataset. To overcome the limitation of the existing segmentation networks, we propose in this paper a novel convolutional neural network for chromosome segmentation, called ChroSegNet. In this network, we designed a new hybrid attention module combining channel attention and spatial attention to extract key feature information and location information of chromosome instances. The channel attention mechanism helps the network to extract key feature information of the target chromosome, while the spatial attention mechanism helps the network to focus on more important spatial information, such as the position relationship between each chromosome. In addition, we carry on the appropriate deepening of the network structure, so that our network can obtain more multidimensional feature information. These improvements would lead to improved feature extraction capability and segmentation accuracy of ChroSegNet, demonstrated in our experimental results reported later. The main contributions of this paper are summarized as follows. * We process the chromosome data with particular techniques to realize the quality and quantity enhancement of the chromosome data. Based on the processed data, we constructed our chromosome segmentation dataset containing 13,096 pairs of chromosome data ready for the training of not only our ChroSegNet but also any other CNN models for chromosome processing. * We propose our end-to-end chromosome segmentation network, i.e., ChroSegNet. ChroSegNet can focus on key feature information and location information of each chromosome through the attention module proposed by us. In addition, the deep-level feature fusion further improves the ability of the network to extract chromosome feature information. The rest of this paper is organized as follows.