RISEC: Rotational Invariant Segmentation of Elongated Cells in SEM Images with Inhomogeneous Illumination

Abstract

Detection of Clostridioides difficile cells in scanning electron microscopy images is a challenging task due to the challenges of cell rotation and inhomogeneous illumination. Currently, orientation-invariance in deep ConvNets is achieved by data augmentation. However, training with all possible orientations increases computational complexity. Furthermore, conventional illumination-invariance models include pre-processing illumination normalization steps. However, illumination normalization algorithms remove important texture information which is critical for the analysis of SEM images. In this paper, RISEC (Rotational Invariant Segmentation of Elongated Cells in SEM images with Inhomogeneous Illumination) is proposed to address the challenges of cell rotation and inhomogeneous illumination. First, a generative adversarial network segments the candidate cell regions proposals, addressing the inhomogeneous illumination. Then, the region proposals are passed to two capsule layers where a rotation-invariant shape representation is learned for every cell type via dynamic routing. Our experiments indicate that RISEC outperforms the state of the art models (e.g., CapsNet, and U-net) by at least 11% improving the dice score.