IEEE J Biomed Health Inform. 2021 Aug;25(8):3240-3251. doi: 10.1109/JBHI.2021.3062234. Epub 2021 Aug 5.
Karyotyping is the gold standard in the detection of chromosomal abnormalities. To facilitate the diagnostic process, in this paper, a method for chromosome classification and straightening based on an interleaved and multi-task network is proposed. This method consists of three stages. In the first stage, multi-scale features are learned via an interleaved network. In the second stage, high-resolution features from the first stage are input to a convolution neural subnetwork for chromosome joint detection, and other features are fused and fed to two multi-layer perceptron subnetworks for chromosome type and polarity classification. In the third stage, the bent chromosome is straightened with the help of detected joints by two steps: first the chromosome is separated, rotated and assembled according to the detected joints; then the areas around the bending points are recovered by replacing the gaps formed in the first step with the sampled intensities from the bent chromosome. The classification of type and polarity can expedite the process of producing karyograms, which is an important step for chromosome diagnosis in clinical practice. Straightening makes the banding information of the chromosome easier to read. Classification results of the 5-fold cross validation on our dataset with 32 810 chromosomes achieve average accuracy of 98.1% for type classification and 99.8% for polarity classification. The straightening results show consistency in intensity and length of the chromosome before and after straightening.
核型分析是检测染色体异常的金标准。为了便于诊断过程，本文提出了一种基于交错和多任务网络的染色体分类和校正方法。该方法包括三个阶段。在第一阶段，通过交错网络学习多尺度特征。在第二阶段，将来自第一阶段的高分辨率特征输入卷积神经网络子网络进行染色体联合检测，并融合其他特征并输入到两个多层感知机子网络中进行染色体类型和极性分类。在第三阶段，在检测到的关节的帮助下，通过两步弯曲的染色体变直：首先根据检测到的关节分离、旋转和组装染色体；然后通过用弯曲染色体上采样的强度替换第一步中形成的间隙来恢复弯曲点周围的区域。类型和极性的分类可以加快产生核型图的过程，这是临床实践中染色体诊断的重要步骤。变直使染色体的带型信息更容易阅读。在我们的数据集上进行的 5 倍交叉验证的分类结果，对于类型分类的平均准确率为 98.1%，对于极性分类的准确率为 99.8%。变直结果显示了变直前后染色体强度和长度的一致性。
