## 3 Experiments  
### Dataset  
Data Information  
For cell counting, two cell lines were used, namely a human osteosarcoma cell line (U2 OS) and a human leukemia cell line (HL-60). Osteosarcoma is a primary malignant form of bone cancer. Generally, it affects children and adolescents, where it represents the eighth-most common form of childhood cancer. Considering its poor prognosis, it is the second most important cause of deaths related to cancer in both children and adolescents [64; 65]. Cells from the U2 OS cell line originate from bone tissue, from a differentiated sarcoma of the tibia. Moreover, U2 OS cells are epithelial adherent cells and exhibit a fast growth rate. HL-60 is a cell line that was originally derived from a woman suffering from acute promyelocytic leukemia. This type of blood cancer is a subtype of acute myeloid leukemia. Untreated, the median survival is less than one month [66]. The U2 OS and HL60 cell lines were cultured in Roswell Park Memorial Institute 1640 medium (RPMI; GibcoBRL, Grand Island, NY, USA) and Dulbecco's Modified Eagle Medium (DMEM; GibcoBRL, Grand Island, NY, USA), respectively, supplemented with 10% Fetal Bovine Serum (FBS) and 1% antibiotics (penicillin and streptomycin). To determine the number of cells growing in medium, hemocytometer counting was performed according to the ABCAM protocol 'Counting Cells Using a Hemocytometer' with minor modifications. Visualization and counting of the cells was done using microscope Axiovert S100 from Zeiss, with a 10X objective. The mean number of cells was determined by repeatedly counting cells in four non-overlapping sets of 16 corner squares, indicated on the hemocytometer, selected at random. A camera and computer program from Zeiss, AxioCam Cm1 and ZEN 2 respectively, were used to capture and save snapshots of the hemocytometer containing the cells. These microscope images could then be used for experiments. Data Preparation  
The images were originally in the Carl Zeiss Images (.Czi) format, a microscope image file format commonly containing image stacks, time-lapse series, and tile images captured from a Carl Zeiss microscope. In contrast with regular image formats, Czi can combine imaging data with additional metadata about the image itself, the microscope, and the camera used. The files in this dataset, however, only contained metadata on the images, i.e., its size (1388 by 1038 pixels with 1 channel), and its pixel type ('gray16'). In the filename, the number of cells present in the inner 16 squared grid was given. Using the aicspylibczli library ([[https://pypi.org/project/aicspylibczi/](https://pypi.org/project/aicspylibczi/)]([https://pypi.org/project/aicspylibczi/](https://pypi.org/project/aicspylibczi/)) (accessed on 26 May 2021)) the images were loaded as Numpy arrays and saved in the TIFF format using the Pillow Imagemodule ([[https://pillow.readthedocs.io/](https://pillow.readthedocs.io/)]([https://pillow.readthedocs.io/](https://pillow.readthedocs.io/)) (accessed on 26 May 2021)). Next, the grid of 16 inner squares was manually cropped because only cells in this area were counted and represented in its label. This resulted in images of 700 by 700 pixels. The OpenCV library was used to handle images in the tiff format. When looking at the original images in Figure 4, it can be observed that the following challenges are present:  
* The cells tend to have varying circularity ratios, ranging from elongated ellipses to round circles. Therefore, algorithms that rely on the round shape assumption do not hold. * Some cells have differing levels of staining intensity. Therefore, algorithms that require homogeneous intensity distributions of the object will not perform well. * Since cells were manually counted using counting chambers, the grid lines are still present in the image. These will cause interference with algorithms that separate foreground from background. Altogether, we have obtained 165 images that we randomly split into the training set (133 images) and the test set (32).