## 1 Introduction  
The ongoing digitisation in pathology allows for computer-aided diagnosis in a wide spectrum of applications. In fact, the development of image recognition techniques enables the quantitative analysis of digital images with a high throughput processing rate. Automation continues to be widely applied in clinical practice since it both reduces the need for manual assessments, which are labor intensive and time consuming, while also reducing intra- and interobserver variability among pathologists [1, 2]. Furthermore, automation can easily provide reproducible measurements and standardisation for follow-up evaluations and comparative studies used, for example, in personalised medicine. Quantitative information such as cell size, shape and spatial distribution are generally used by pathologists for cancer diagnosis [3]. For example, detecting nuclei enables the computer-aided assessment of immune cell infiltrates, which has a clear advantage due to its prognostic potential and use in immunotherapy trials [4, 5]. Once accurately segmented, nuclear morphometric and appearance features can be beneficial for assessing cancer grades and also predicting treatment response [6]. Thus, a robust and generalised technique that accurately segments nuclei in a diverse range of images could be integrated into the computer-aided assessment technology used by pathologists. The detection and segmentation of cell nuclei is one of the core operations in computer-aided diagnosis; it serves as a basis for cell counting and the study of subcellular morphology, such as investigations into the shape, size and textural properties [7]. This task is particularly challenging in histopathology image analysis, since the target cells, which are in many cases very small, are surrounded by background clutter made up of histological structures (capillaries, collagen, etc.) with irregular visual aspects, as well as artefacts which appear during image acquisition (see Figure 1). Second, significant variations in the appearance of cells are observed in different phases of mitosis development, which complicates the cell nuclei detection task. In particular, the amount of cells may range from tens to thousands in a typical high-resolution microscopy image, so the overlap of cells is a common phenomenon. Furthermore, the problem becomes more challenging because of the diversity of nuclear morphology, different staining conditions and different imaging technology applied. These challenges related to cell nuclei detection remain to be solved [2; 5; 8]. During the last few decades, many attempts have been made to improve the efficiency and accuracy of automatic nuclear segmentation techniques in digital pathology, with a special focus on histopathological image analysis. Recent comprehensive reviews of the nuclei segmentation literature [2; 9] showed that image analysis techniques applied in this domain have evolved from classical approaches with hand-crafted features to deep learning-based techniques that typically yield superior accuracy. The most compelling advantage of deep learning is the ability to generalise and automatically learn problem-specific features directly from the original data. As such, deep convolutional neural networks (CNNs) demonstrated advancements in image recognition tasks and achieved state-of-the-art results in many medical imaging applications [8; 10]. U-Net and DeepCell are examples of models that were designed specifically for cell or nuclei segmentation. Recently published results show that these architectures outperformed classical image processing algorithms in various segmentation tasks [11; 12; 13; 14]. For example, the CNN architecture, called the comparison detector, is proposed for the detection of cervical cancer in small datasets [15]. In [16], a CNN-based algorithm is developed to perform cell segmentation from single channel images using different stain markers and magnifications.