# Unsupervised Cyclic Siamese Networks Automating Cell Imagery Analysis
## 3 Methodology  
### Natural Data  
Image data applied in this study were obtained by MSCC of mammalian suspension cells, as introduced before in the literature [40]. The CHO-K1 cells were cultivated in polydimethylsiloxane (PDMS) glass chips. Perfusion of the device constantly provided the cultures with nutrients. An automated inverted microscope performed the live cell imaging, taking images of the relevant positions on-chip every 20 min. The data used in this work are split into two major parts according to the two microscopy technologies, namely bright-field microscopy and phase-contrast microscopy, abbreviated as BF and PC, which were used for the analysis of the architecture. Figure 2 shows example data from both microscopy technologies after the application of the preprocessing described below.  
Around 10,000 images were taken over the course of the experiments per microscopy technology; then, images of empty and fully filled cell chambers were removed, since, for these, the experiment had not started yet or the outcome of the experiment was already determined, respectively. In total, 2983 BF images and 3944 PC images remained relevant for the machine learning task. Around 20% of the data were labeled by hand exclusively for testing and will from here on be called Nat-L-Te (natural, labeled test data); the other 80 percent remain unlabeled and are used for training and called Nat-U-Tr (natural, unlabeled training data). The test data were split in half to obtain a verification data set and to prevent accidental specialized training on the test data over the course of the hyperparameter optimization. During the test data selection process, we ensured that full experimental runs as well as randomly picked images from the various experiment series were part of the test and verification data. Table 1 gives an overview over the different types of data sets used in our work.  
Figure 2: Samples from the natural data sets after application of various data enrichment techniques, described below. Phase-contrast technology on the left, bright-field technology on the right. The image resolution equals the working resolution.  
We crop and rotate all images to center the cultivation chamber. Further data augmentation beyond this preprocessing is described in Section 3.2. We place our focus on the larger data set called Nat-PC from here on. It contains more experimental samples and the biological processes covered are more diverse. In addition, phase-contrast microscopy is more popular, and we will nonetheless show that our method also works reliably on the smaller Nat-BF data set, although the variations in cell positions, numbers, and sizes are lower in this data set and therefore the quality of these images is lower in terms of machine learning, similar to what might be the case for entirely different types of cells, such as plant cells.  
To make the best use of the poor amount of experimental data, the following enrichment techniques are applied to the data. Flips along both image axes are followed by a random crop up to the edges of the cell chamber, not cropping away cells, except for the entrance tunnel, where precise cell detection is not required. The crop does complicate cell detection, as cells may be in positions where only the chamber rim and the outside of the chamber would be without the crop, but it proved necessary to allow cells to appear anywhere on the images to ensure uniform detection success that is barely affected by the position of the cells within an image. Then, a random rotation of 90\({}^{\circ}\) is performed and a randomly generated noise map is multiplied by a small weighting factor and applied to the image to simulate more fluctuation in the cells' visuals, since occasionally there are dead cells in the experimental data that do not change in appearance for multiple images. All augmentations are reapplied to the original data for every epoch of training with seed consistency to ensure reproducibility.