In recent years the use of supervised methods has shown promising results in segmentation tasks. Supervised machine learning algorithms build mathemat-ical models from a set of labeled data. Example of supervised techniques are convolution neural networks (CNN), support vector machine (SVM) and naive Bayes model. Due to the ability of CNN to capture pattern of **data** in challenging datasets, they have been used in many works. For instance, [15] increases the receptive field by applying 7 × 7 filter size on fully convolutional network (FCN), this results in an outstanding segmentation performance of 99.7% accuracy on feline calicivirus images. In [16], [17], in order to tackle the challenge of imbalance between the foreground and background, a dice coefficient is ap- as SegNet [20] and U-net [21] which are capable of working in small number of datasets, gives us a suitable option for our dataset. Moreover, SegNet shows more superiority for having few parameters and hence faster to train, because it passes pooling indeces to the upsampling layers and does not use the heavy deconvolution layers. U-net has been applied in many works for segmentation of EM. Nevertheless, to the best of our knowledge no any work has been done on segmentation of EMs using SegNet, except for one work which uses SegNet directly without any network changes from the original one on sementation of yeast cells [22]. Thus, in this paper we attempt to leverage SegNet for segmentation of weakly visible microorganisms.