## 1 Introduction
By implementing skip connections for every layer in the down-sampling half of the network, each up-sampling layer consisted of twice the data. The U-Net demonstrated that it was not necessary to have very large annotated datasets to achieve high accuracy in segmentation tasks using deep learning. Deep-learning approaches for cell segmentation have been developed for various cell types and imaging methodologies. DeepCell [26], developed in 2016, is a deep-learning model based on CNNs developed to segment bacterial and mammalian cells, but it has not been used for yeast cells. In 2017, a model trained to analyze yeast cells was published [27] based on the SegNet [28] architecture. The SegNet architecture is similar to the U-Net in that it follows a fully convolutional Encoder-Decoder structure, with the main difference being the method on how up-sampling is done. In SegNet the indices from max-pooling are stored and used to upsample images in the decoder part of the architecture. Notably, this model was trained to detect yeast cells from very noisy differential interference contrast (DIC) microscopy images and cannot be directly used for bright-field image analysis. In 2019, a Mask-RCNN model named YeastSpotter [29] was published for yeast-cell segmentation. YeastSpotter is a model trained on the BBBC038 [30] dataset from the Broad Bioimage Benchmark Collection for a Kaggle competition for segmenting nuclei in mammalian cells. The Mask-RCNN architecture used by YeastSpotter differs from U-Net and SegNet in that it is an instance segmentation network which means it not only classifies pixels, but also labels separate instances of classes. It contains several connected networks for locating bounding boxes, class prediction and mask prediction. For a comparison with our  
Figure 1: A visual description of simple Convolutional Neural Network (CNN). Convolution layers produce feature maps that get down-sampled by max-pooling layers. Combinations of these two layer types make up the feature extraction part of a CNN. A fully connected layer reads the output of the feature extraction and outputs a class prediction probability for every class in the data. approach, this model was taken and used directly on yeast bright-field microscopy images without fine-tuning. The U-Net has been succesfully adapted for use in similar settings as seen in DeLTA [31] where a U-Net algorithm was used to accurately and automatically segment _E. coli_ cells. A U-Net-derived network was published in 2017 for the segmentation of red blood cells [32]. They adapted the deformable U-Net [33] architecture which uses deformable convolutions for feature learning. [PERSON] et al. developed the YeaZ platform [34] which uses a standard U-Net architecture for the segmentation of yeast cells. [PERSON] et al. developed a similar platform which also uses a standard U-Net architecture [35] and focuses on multi-class segmentation of yeast cells trapped in microfluidics plates. A difficulty in segmenting images of this kind is that often cells and microfluidics traps look very similar. Therefore, by creating a multi-class labeled dataset, it was possible to accurately detect just the yeast cells. [PERSON] et al. also developed a U-Net-based platform [36] for yeast-cell segmentation using two modified U-Net networks to detect yeast-cell masks and yeast-cell centers. Both networks were modified the same way which involved adding extra convolutional layers to the encoder part of the network and removing convolutional layers from the decoder part. All these methods demonstrate high performance and improvements over previously published non-CNN approaches. The contribution of this work is two-fold. (1) We provide a dataset of 150 bright-field images of budding yeast at three levels of focus, with ground-truth segmentations. We also provide ground-truth segmentation for 80 images from two datasets found in the Yeast Image Toolkit. This type of training data for bright-field images of yeast is very limited in this field, and will enable future research in this domain.