Related papers: Adversarial Multiscale Feature Learning for Overlapping Chromosome Segmentation

Adversarial Multiscale Feature Learning for Overlapping Chromosome Segmentation

URL: http://arxiv.org/abs/2012.11847v2
Date: Fri, 26 Mar 2021 05:44:18 GMT
Title: Adversarial Multiscale Feature Learning for Overlapping Chromosome Segmentation
Authors: Liye Mei, Yalan Yu, Yueyun Weng, Xiaopeng Guo, Yan Liu, Du Wang, Sheng Liu, Fuling Zhou, and Cheng Lei
Abstract summary: Chromosome karyotype analysis is of great clinical importance in the diagnosis and treatment of diseases. Due to the strip shape of the chromosomes, they easily get overlapped with each other when imaged. We present an adversarial multiscale feature learning framework to improve the accuracy and adaptability of overlapping chromosome segmentation.
Score: 6.180155406275231
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Chromosome karyotype analysis is of great clinical importance in the diagnosis and treatment of diseases, especially for genetic diseases. Since manual analysis is highly time and effort consuming, computer-assisted automatic chromosome karyotype analysis based on images is routinely used to improve the efficiency and accuracy of the analysis. Due to the strip shape of the chromosomes, they easily get overlapped with each other when imaged, significantly affecting the accuracy of the analysis afterward. Conventional overlapping chromosome segmentation methods are usually based on manually tagged features, hence, the performance of which is easily affected by the quality, such as resolution and brightness, of the images. To address the problem, in this paper, we present an adversarial multiscale feature learning framework to improve the accuracy and adaptability of overlapping chromosome segmentation. Specifically, we first adopt the nested U-shape network with dense skip connections as the generator to explore the optimal representation of the chromosome images by exploiting multiscale features. Then we use the conditional generative adversarial network (cGAN) to generate images similar to the original ones, the training stability of which is enhanced by applying the least-square GAN objective. Finally, we employ Lovasz-Softmax to help the model converge in a continuous optimization setting. Comparing with the established algorithms, the performance of our framework is proven superior by using public datasets in eight evaluation criteria, showing its great potential in overlapping chromosome segmentation

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