## 1 Introduction
Unsupervised algorithms which use computer vision processes such as the aforementioned watershed algorithm [18], and active contour fitting [20; 21] are popular solutions. An updated version the 2013 Doncic algorithm was reported in 2019 [19] with improved watershed marker selection, a step that used to be interactive. It also takes advantage of the fact that all cells are present in the final image in a time-series, by starting from the final image and working backwards they simplify the problem of detecting and tracking new cells. Recently, hybrid algorithms using pipelines of multiple processes with manual user supervision have become more common. One of these methods called CellStar uses thresholding and active contour fitting, in addition to user-enabled automated parameter fitting. CellStar shows the highest segmentation accuracy for a variety of data sets [21]. Most recent computer vision tools have leveraged the power of deep learning. This became possible with the development of the convolutional neural net (CNN) which uses convolutional layers over fully connected layers, the building blocks of the original neural networks. The multi-perception, the original deep-learning network architecture was not suited for use on multi-dimensional data such as images so the CNN was developed to handle the increased computational power required to use deep learning with images. The critical change introduced with the CNN is the convolutional layer which reduces the connections between neurons to only a neighborhood of pixels rather than the whole image, as well as weight sharing. The convolutional layer, paired with the pooling layer, which reduces the resolution of convolutional feature maps, combine to allow for deep convolutional networks to learn features at multiple scales. These feature maps are then fed into a fully connected layer to make a class prediction. An example of a simple 2-layer convolutional network is shown in Figure 1. A more in-depth description of CNNs can be found in [22; 23]. CNNs have been adopted across nearly every discipline for their many advantages over previous algorithms. Model inference is fully automated and CNNs can be trained to be exceptionally accurate. In addition, since images often share similar features, common pre-trained models can be reused in different domains in a process called transfer learning. CNNs suffer from several disadvantages which involve the requirement of a large number of labeled images for training as well as lengthy training times. CNNs are also computationally intensive to train and run, though the ability to train CNNs on Graphics Processing Units (GPUs) can speed this up greatly. In recent years, CNNs have been extended to the task of semantic segmentation and led to new possibilities in the field of biomedical imaging. Semantic segmentation is the process of segmenting an image into multiple classes by classifying each pixel in an image. The fully convolutional network for semantic segmentation [24] is the seminal architecture that introduced the ability to train a network to output pixel-wise class predictions for an input image. The fully connected layers usually found at the end of CNNs and used to classify images were replaced with learned convolutional up-sampling layers. By using up-sampling layers, the down-sampling of the encoder layers is reversed, and the output of the network maintains the resolution of the input image. In order to improve the up-sampling layer, feature maps obtained at earlier stages of the network are appended to the final layer via skip connections. It has been discovered that the increase in the number of connections leads to higher accuracy of the network [24]. In 2015, the U-Net architecture [25] improved classification accuracy achieved with the fully convolutional semantic segmentation networks introduced by [PERSON] et al. [24]. The U-Net was developed for the task of pixel-wise cell segmentation of mammalian cells.