## 1 Introduction  
Automatically analyzing microscope images is an important and challenging computer vision problem that can be done for a wide range of tasks. These include but are not limited to: classifying cell types [1, 2], extracting cell shapes [3, 4], identifying the position of cells [5, 6, 7], and counting the number of cells [8, 9, 10, 11]. Counting cells in particular serves an important function for several different biomedical tasks. It has been used to aid in estimating microbial content [12, 13], measuring cytotoxicity [14, 15] and discovering the role of particular genes in cell biology, microbiology, and immunology [16, 17, 18]. For example, when conducting cancer research, researchers can investigate the effects of radiation using the clonogenic colony formation assay [19, 20, 21] or the neurosphere formation assay for measuring the proportion of brain tumor-initiating cells [22, 23, 24]. Manually counting cells is still conducted in some laboratories--frequently using hemocytometers as a visual aid [25]. However, this method is prone to interobserver variations, and it is arduous,time-consuming, and not feasible when using high throughput assays. Therefore, there is a need to count cells automatically. In the last couple of decades, many computer-vision-based approaches have been suggested for automatic cell counting. The structure of these frameworks can be roughly categorized into two sections. First, hand-crafted feature representations are extracted that are further fed into a classifier. For instance, features are extracted using Histogram of Oriented Gradients (HOG) [26], Laplacian of Gaussian operation (LoG) [27], or Local Binary Patterns (LBP) [28] to represent the input images. Then the image representation is fed to a classification model such as a Support Vector Machine (SVM) or a Random Forest (RF) to detect the cells. However, approaches with hand-crafted features as input to classifiers could suffer from the following limitations: **(i)** Selecting a suitable feature extractor is a non-trivial task. Frequently, this requires specific knowledge of the cells in an image and the context wherein those cells are present. **(ii)** Feature extraction methods can be highly coupled with a specific type of cell, and, therefore, they do not generalize well to different images containing other cell types. **(iii)** Many feature extraction methods contain a large number of tunable parameters that highly affect the final performance. This makes the optimization process time-consuming and a matter of trial-and-error. **(iv)** Even with large datasets, the performance of such methods reaches a level upon which it cannot overcome anymore. Deep Neural Networks (DNN) have been applied for a wide range of computer vision tasks and has set new state-of-the-art on several benchmarks related to classification, object detection, and segmentation [29; 30; 31; 32; 33]. The biggest advantage of using deep neural networks is that there is no need to rely on hand-crafted features anymore. Instead, a deep neural network is optimized using a (mini-batch) gradient descent over large amounts of data and automatically learns problem-specific features directly from training data. With regards to the problem of counting cells in images, this task can be approached from two distinct directions: (i) developing a cell detector, and (ii) developing a cell counter. Cell detection approaches consider the problem as a matter of identifying cell instances in an image first and then counting the number of instances found. Frequently, these approaches rely on each image containing annotation information, consisting of either dot annotation localizing the centroids of cells [34; 35; 36], or bounding box annotations indicating the borders of cells [37]. The problem with these approaches is that using them in real-world applications is challenging because cell densities can be extremely high, ranging in the thousands per image, and cells can have wide varieties regarding shapes, sizes, and colors.