DETCIC: Detection of Elongated Touching Cells with Inhomogeneous Illumination using a Stack of Conditional Random Fields

Abstract

Clostridioides difficile infection (C. diff) is the most common cause of death due to secondary infection in hospital patients in the United States. Detection of C. diff cells in scanning electron microscopy (SEM) images is an important task to quantify the efficacy of the under-development treatments. However, detecting C. diff cells in SEM images is a challenging problem due to the presence of inhomogeneous illumination and occlusion. An Illumination normalization pre-processing step destroys the texture and adds noise to the image. Furthermore, cells are often clustered together resulting in touching cells and occlusion. In this paper, DETCID, a deep cell detection method using adversarial training, specifically robust to inhomogeneous illumination and occlusion, is proposed. An adversarial network is developed to provide region proposals and pass the proposals to a feature extraction network. Furthermore, a modified IoU metric is developed to allow the detection of touching cells in various orientations. The results indicate that DETCID outperforms the state-of-the-art in detection of touching cells in SEM images by at least 20 percent improvement of mean average precision.

Edge, Ridge, and Blob Detection with Symmetric Molecules

“…This principle is often termed divisive normalization and closely related to the normalization procedure which ensures contrast invariance in the proposed measures (cf. (22), (44), (55), and (64)). The implementations of the developed edge, ridge, and blob measures for digital images could also be modeled as artificial neural networks, where the applied even-and odd-symmetric digital filters define a convolutional layer, the absolute value is used as a non-linearity and max-pooling as well as divisive normalization are applied to obtain the final normalized values.…”

“…This principle is often termed divisive normalization and closely related to the normalization procedure which ensures contrast invariance in the proposed measures (cf. ( 22), ( 44), (55), and ( 64)).…”

“…Previous versions of E(f, y) and R(f, y) that are based on complex-valued shearlets [60,44,62] were already successfully applied in different feature extraction tasks such as the detection and characterization of flame fronts [62,44], the detection of borders of tidal flats in the Wadden Sea [44], the extraction of fracture-traces in rock masses [13], the detection of channel boundaries in seismic data [41,42], and the automated detection of the boundaries of touching cells in scanning electron (SEM) images [55].…”