AS-PANet:改进路径增强网络的重叠染色体实例分割

目的 染色体是遗传信息的重要载体，健康的人体细胞中包含46条染色体，包括22对常染色体和1对性染色体。染色体核型化分析是产前诊断和遗产疾病诊断的重要且常用方法。染色体核型化分析是指从分裂中期的细胞显微镜图像中，分割出染色体并根据染色体的条带进行分组排列的过程。染色体核型化分析通常由细胞学家手工完成，但是这个过程非常费时、繁琐且容易出错。由于染色体的非刚性特质，多条染色体之间存在重叠及交叉现象，致使染色体实例分割非常困难。染色体分割是染色体核型化分析过程中最重要且最困难的一步，因此本文旨在解决重叠、交叉染色体实例分割问题。方法 本文基于路径增强网络（PANet）模型，提出AS-PANet（amount segmentation PANet）模型用于解决重叠染色体实例分割问题。在路径增强网络的基础上引入染色体计数领域知识预测作为模型的一个预测分支，并改进了路径增强网络的模型结构和损失函数，使图像分类、目标检测、实例分割和染色体计数4个子任务共享卷积特征，进行联合训练。在临床染色体图像数据上进行标注并构建训练集和测试集，同时提出有效的数据增广方法用以扩充染色体标注训练数据集，提升模型的训练效果。结果 在临床染色体数据集中开展实证研究实验。实验结果表明，本文方法在临床染色体数据集中，平均分割精度mAP（mean average precision）为90.63%。该结果比PANet提升了1.18%，比基线模型Mask R-CNN提升了2.85%。分割准确率为85%，相比PANet提升了2%，相比Mask R-CNN（region with convolutional neural network）提升3.75%。结论 本文染色体实例分割方法能够更有效地解决临床染色体分割问题，相比现有的方法，分割效果更好。

AS-PANet: a chromosome instance segmentation method based on improved path aggregation network architecture

Objective Twenty-three pairs of chromosomes, including 22 pairs of autosomes and a pair of sex chromosomes, are found in the cells of a healthy human body. As those chromosomes are vital genetic information carriers, karyotyping chromosomes is important for medical diagnostics, drug development, and biomedical research. Chromosome karyotyping analysis refers to segmenting chromosome instances from a stained cell microphotograph and arranging them into karyotype in accordance with their band criterion. However, chromosome karyotyping analysis is usually performed by skilled cytologists manually, which requires extensive experience, domain expertise, and considerable manual efforts. In this study, we focus on the segmentation of chromosome instances because it is a crucial and challenging obstacle of chromosome karyotyping. Method In this study, we propose the AS-PANet(amount segmentation PANet) method by improving the PANet instance segmentation model for the segmentation task of chromosome instances. We add a chromosome counting task into AS-PANet for joint training, thereby enabling the classification, detection, segmentation, and counting tasks to share latent features. We propose a chromosome label augmentation algorithm for augmenting the training dataset. Result We collect clinical metaphase cell microphotograph images from Guangdong Women and Children Hospital. Then, we build a clinical dataset by labeling 802 chromosome clusters. The clinical dataset is split into training and testing datasets with a ratio of 8 :2. The proposed augmentation algorithm augments the training dataset from 802 samples into 13 482 samples. Our solution achieves a more competitive result on the clinical chromosome dataset with 90.63% mean average precision (mAP), whereas PANet yields 89.45% mAP and Mask R-CNN(regirn with convolutional neural network) obtains 87.78% mAP at the equal experimental condition. The AS-PANet method yields 85% instance segmentation accuracy, which is 2% higher than the result of PANet and 3.75% higher than the result of Mask R-CNN. Conclusion Experimental results demonstrate that the proposed method is an effective and promising solution for solving the segmentation problem of clinical chromosome instances. The contributions and highlights of this study are summarized as follows: 1) we proposed a novel chromosome instance segmentation method by improving the path aggregation network architecture. 2) We built a clinical dataset for training and verifying the proposed method. 3) We demonstrated the effectiveness of the proposed method for tackling the chromosome instance segmentation task. Experimental results showed that the proposed method is more promising than previous studies. 4) The highlights of this work are high chromosome segmentation certainty and accuracy with a small amount of manual labeling cost.