# ChroSegNet: An Attention-Based Model for Chromosome Segmentation with Enhanced Processing
## 2 Related Works  
Chromosome segmentation is a branch in the field of medical image segmentation and one of the most critical stages in the process of karyotype analysis. The purpose of chromosome segmentation is to separate the chromosome instances from the complex microscopic chromosome images. Different from other medical images, chromosome microscopic images are susceptible to sensor noises, staining noises, and uneven illumination noises. These noises are due to the irresistible factors in the process of image preparation and acquisition. In addition, chromosomes have the variability of morphological structure and the diversity of contact overlap, which is difficult to identify by traditional methods. The need for hospitals to protect patient privacy has led to difficulties in obtaining chromosome microscopy images; thus, there is a severe lack of data volume. The above problems impose significant challenge in chromosome segmentation. In the early years, many researchers proposed traditional segmentation methods based on specific rules to segment chromosomes. [PERSON] et al. [15] proposed a rule based on geometric contour analysis to extract chromosomes. [PERSON] [16] and [PERSON] [17] proposed segmentation methods based on the watershed algorithm, which are too sensitive to noise thus often leading to oversegmentation. [PERSON] et al. [18] proposed a method based on adaptive fuzzy c-means clustering, which better overcomes the problem of uneven illumination caused by microscope imaging systems and can segment overlapping adjacent chromosomes in different illumination areas. A segmentation method based on spatial variable thresholding was proposed by [PERSON] et al. [19], which selects the best region for segmentation based on geometric features and pixel distribution. The above traditional segmentation methods, however, have limited performance and are time-consuming and labor-intensive. In recent years, the excellent performance of deep learning has made it widely utilized in the field of medical images'. One category of such representative methods is the chromosome segmentation based on convolutional neural network. For example, [PERSON] et al. [20] proposed an overlapping chromosome segmentation method for MFISH (Multicolor Fluorescence In Situ Hybridization) images, which employs fully convolutional networks [21] (FCN), using spatial and spectral information in an end-to-end manner. [PERSON] et al. [13] proposed a chromosome segmentation model combining Mask-RCNN [22] and geometric correction algorithm. This research achieved the instance segmentation of chromosome microscopic images for the first time. Although this model achieved a high accuracy, its structure is too complicated. When the scale of the real chromosome images is small, the segmentation accuracy of this model drops drastically. [PERSON] et al. [23] proposed U-Net in 2015, which is a network designed based on FCN [21]. Because of the simple structure of the network and the effective use of high and low dimensional feature information, it is suitable for the medical image segmentation with the lack of data and complex image features. [PERSON] et al. [24] proposed a method for semantic segmentation of overlapping chromosomes based on U-Net [23]. This study not only made structural improvements based on the original network such as adding an appropriate number of layers but also used Test Time Augmentation (TTA) to overcome the overfitting problem that occurred during the training process. Compared with previous similar work, the segmentation accuracy is improved, but it is still low due to the lack of improvement for chromosome characteristics. [PERSON] et al. [25] proposed a primitive G-band chromosome image segmentation method based on U-Net. [PERSON] et al. [26] proposed a G-band chromosome segmentation method combining U-Net and YOLOv3. The method consists of two stages. In the first stage, YOLOv3 detects chromosome instances and obtains multiple detection boxes containing one or more chromosome instances.