RISEC: Rotational Invariant Segmentation of Elongated Cells in SEM Images with Inhomogeneous Illumination
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.
KeywordsInstance segmentation Orientation-invariance Illumination Convolutional capsules Generative adversarial networks
This work was supported in part by NIH/NIAID 1UO1 AI-24290-01 and by the Hugh Roy and Lillie Cranz Cullen Endowment Fund. At the time of data collection. Dr. Endres was a postdoctoral fellow at the University of Houston. All statements of facts, opinion or conclusions contained herein are those of the authors and should not be construed as representing official views or policies of the sponsors.
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