Deeply supervised full convolution network for HEp-2 specimen image segmentation

摘要

Human Epithelial-2 (HEp-2) cell images play an important role for the detection of antinuclear autoantibodies (ANA) in autoimmune diseases. Segmentation is the primary step for classification, further treatment and diagnosis. However, the staining patterns and scales of HEp-2 specimen images have different variances, which still make segmentation quite a challenging task. To solve it, we propose a novel deeply supervised full convolutional network (DSFCN) for robust segmentation of different HEp-2 cell images dataset. DSFCN is based on a very deep network, which integrates the dense deconvolution layer (DDL) and hierarchical supervision structure (HS). Specifically, The DDL uses the up-sampling to restore the high resolution of the original input images to replace the traditional deconvolution layer, and the hierarchical supervision is added to capture feature information of the shallow layers. The high-resolution predictive output is obtained by capturing local and global information between layers. Without relying on the prior knowledge and complex post-processing, DSFCN can be effectively trained in an end-to-end manner. The proposed method is trained and tested on the I3A-2014 public dataset, and the segmentation result demonstrates that the performance of our model outperforms other state-of-the-art methods. (C) 2019 Published by Elsevier B.V.