  Histology method is vital in the diagnosis and prognosis of cancers and many
other diseases. For the analysis of histopathological images, we need to detect
and segment all gland structures. These images are very challenging, and the
task of segmentation is even challenging for specialists. Segmentation of
glands determines the grade of cancer such as colon, breast, and prostate.
Given that deep neural networks have achieved high performance in medical
images, we propose a method based on the LinkNet network for gland
segmentation. We found the effects of using different loss functions. By using
Warwick-Qu dataset, which contains two test sets and one train set, we show
that our approach is comparable to state-of-the-art methods. Finally, it is
shown that enhancing the gland edges and the use of hematoxylin components can
improve the performance of the proposed model.
