An automatic progressive chromosome segmentation approach using deep learning with traditional image processing.

The fully automatic chromosome analysis system plays an important role in the detection of genetic diseases, which in turn can reduce the diagnosis burden for cytogenetic experts. Chromosome segmentation is a critical step for such a system. However, due to the non-rigid structure of chromosomes, chromosomes may curve in any direction, and two or more chromosomes may touch or overlap to form unpredictable chromosome clusters in metaphase chromosome images, leading to automatic chromosome segmentation as a challenge. In this paper, we propose an automatic progressive segmentation approach to perform the entire metaphase chromosome image segmentation using deep learning with traditional image processing. It follows three stages. In the first stage, thresholding-based and geometric-based methods are employed to divide all chromosomes as single ones and chromosome clusters. To tackle the segmentation for unpredictable chromosome clusters, we first present a new chromosome cluster identification network named CCI-Net to classify all chromosome clusters into different types in the second stage, and then in the third stage, we combine traditional image processing with deep CNNs to accomplish chromosome instance segmentation from different types of clusters. Evaluation results on a clinical dataset of 1148 metaphase chromosome images show that the proposed automatic progressive segmentation method achieves 94.60% chromosome cluster identification accuracy and 99.15% instance segmentation accuracy. The experimental results exhibit that our proposed approach can effectively identify chromosome clusters and successfully perform fully automatic chromosome segmentation.