  There is growing concern that male reproduction is affected by environmental
chemicals. One way to determine the adverse effect of environmental pollutants
is to use wild animals as monitors and evaluate testicular toxicity using
histopathology. Automated methods are necessary tools in the quantitative
assessment of histopathology to overcome the subjectivity of manual evaluation
and accelerate the process. We propose an automated method to process histology
images of testicular tissue. Segmenting the epithelial layer of the
seminiferous tubule is a prerequisite for developing automated methods to
detect abnormalities in tissue. We suggest an encoder-decoder fully connected
convolutional neural network (F-CNN) model to segment the epithelial layer of
the seminiferous tubules in histological images. Using ResNet-34 modules in the
encoder adds a shortcut mechanism to avoid the gradient vanishing and
accelerate the network convergence. The squeeze & excitation (SE) attention
block is integrated into the encoding module improving the segmentation and
localization of epithelium. We applied the proposed method for the 2-class
problem where the epithelial layer of the tubule is the target class. The
f-score and IoU of the proposed method are 0.85 and 0.92. Although the proposed
method is trained on a limited training set, it performs well on an independent
dataset and outperforms other state-of-the-art methods. The pretrained
ResNet-34 in the encoder and attention block suggested in the decoder result in
better segmentation and generalization. The proposed method can be applied to
testicular tissue images from any mammalian species and can be used as the
first part of a fully automated testicular tissue processing pipeline. The
dataset and codes are publicly available on GitHub.
