## 4 Discussion  
In this work, we present a learnable U-Net model for the segmentation of yeast cells in bright-field images. Recent advances in this field have led to the development of very accurate tools for this task but they each have several trade-offs of varying severity. Among these trade-offs are: manual and time-consuming user input, unfeasible number of z-stack image for time-lapse analysis, and expensive equipment. We demonstrate that a deep-learning approach to this problem can yield an accurate yeast-cell segmentation tool that requires minimal user input, minimal data per time point, and can be used with common and widely available imaging platforms. The high performance attained by YeastNet3 at all three levels of focus indicates that it is resistant to the segmentation errors usually caused by changes in focus. Resistance to changes in focus is a very important feature because focus drift is a significant problem that can cause many problems with image analysis [47], especially with large experiments where hundreds or thousands of images are being taken every hour. There are several challenges in applying computer vision to this domain. A common problem with time-lapse microscopy is drift in the focus of images. The focus of bright-field images is very important because nearly all segmentation algorithms rely on the yeast cells being at a certain focal length for accurate segmentation. By using the 3 different z-stacks of each time point as separate training examples, our YeastNet model learned to \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{2}{c}{**Cell IoU**} & \multicolumn{2}{c}{**Tracking Accuracy**} \\ \cline{2-5}  & **CellStar** & **YeastNet3** & **CellStar** & **YeastNet3** \\ \hline Focus 1 & 0.469 (0.144) & **0.888** (0.023) & 0.606 & **0.939** \\ Focus 2 & 0.771 (0.035) & **0.903** (0.029) & 0.858 & **0.922** \\ Focus 3 & 0.806 (0.100) & **0.917** (0.029) & 0.891 & **0.937** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Cell tracking performance at different levels of focus. Focus 1: in focus, Focus 2: slightly out of focus (0.6 μm above focal plane), Focus3: more out of focus (1.2 μm above focal plane). The Cell IoU column lists mean values with standard deviation values shown in brackets, while the tracking accuracy column lists an F-measure statistic. The highest Cell IoU and tracking accuracy for each level of focus is in bold. Figure 6: **(A)** Bright-Field Microscopy image from our dataset. The cells whose fluorescence profiles are plotted in **(B)** match the color-coding in **(A)**. **(B)** Single-cell fluorescence tracking, generated by YeastNet, to study dynamic gene expression. Each curve is a time-lapse mean pixel fluorescence measure describing the protein abundance of a dynamically expressed gene in an individually tracked cell. Each plot was smoothed using a window of length 5. The image was obtained using a 60\(\times\) oil immersion objective. A 5 μm scale bar is presents to show the scale of yeast cells. detect yeast cells at different focal lengths; in essence making the trained model invariant to minor changes in the focus. A qualitative comparison of the performance on different focal lengths between our model and CellStar is shown in Figure 5A and Table 2. The higher segmentation performance leads to an increased tracking accuracy. Furthermore, the wider difference in the IoU between CellStar and YeastNet is significant for downstream applications of the cell traces. Due to variation in fluorescent protein within the cell, accurate fluorescence quantification requires entire cells to be segmented. We also present a new, labeled dataset for training computer vision models to segment bright-field microscopy images. Since annotated data of this kind was not publicly available, only data we labeled manually could be used to train this model. Manually segmenting images to generate the datasets is laborious and due to the limited size of the dataset, we discovered that minor biases in what constitutes a cell can have many downstream effects.