## 3 Results  
We compared the segmentation performance of YeastNet on our dataset to a non-trainable (manually parameterized) classic algorithm adapted from a yeast-cell-cycle research article [42]. We also compared YeastNet to CellStar [21], the current state-of-the-art tool for bright-field yeast-cell segmentation. We used the CellStar package and followed the instructions, to complete cell segmentation and tracking of our dataset. We also compared with YeastSpotter [29], a mask-RCNN deep-learning model trained for the segmentation of mammalian cell nuclei. YeastNet was trained on just training portions of our dataset and was used to demonstrate the ability of this model to generalize to unseen test samples of our datasets and all samples in YIT datasets 1 and 3. Furthermore, as a separate experiment, our model was also trained on a combination of training splits of all three datasets (we name this trained model YeastNet2) and was tested on test sets of the three datasets. The mean results obtained from 10-fold cross-validation are reported in Table 1. Clearly, our method achieves the highest cell IoU on our dataset. Our method also generalizes similarly to other methods, despite using a single dataset for training. Our model trained on training splits of all three datasets shows improved performance on all datasets. Figure 5 shows a visual comparison of the segmentations by the non-trainable method, CellStar, and YeastNet. (As indicated in Table 1, YeastSpotter performed worse than CellStar, thus its segmentation is not provided here.) Our dataset contains three different bright-field images at each time point, each corresponding to a different level of focus. We compared the segmentation and tracking performance of YeastNet and CellStar separately for each level of focus. We intended to train a model to be invariant to focus level by training on all types of images (images of the same colony at different levels of focus are treated as separate training examples). To testthe tracking performance, it is necessary to use the entire set of images from a time-lapse, and invariably some of these images could come from the training set. To avoid this problem, a YeastNet3 model was trained using only transformed (rotated and mirrored) images. The test set consisted of only the original images with three levels of focus. Table 2 shows both segmentation and tracking performance tested on the original time-course images at different levels of focus. As expected, CellStar's segmentation performance is higher on out-of-focus images (Focuses 2 and 3) in comparison with its performance on in-focus images (Focus 1). YeastNet however has a higher cell IoU at both levels of focus, compared to CellStar. On the in-focus images, CellStar performs poorly, attaining less than a 0.5 cell IoU. Meanwhile, YeastNet obtains a cell IoU that is almost double with 0.888. YeastNet also maintains nearly the same segmentation accuracy with in-focus images as with out-of-focus images, while CellStar is over 15% lower. Thanks to its segmentation performance, YeastNet3 attains a higher tracking accuracy at every level of focus in comparison with CellStar, with the largest improvement (by 55%) at focus \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Method** & **Our Dataset** & **YIT Dataset 1** & **YIT Dataset 3** \\ \hline Non-trainable & 0.562 (0.252) & 0.585 (0.069) & 0.553 (0.060) \\ CellStar & 0.680 (0.184) & 0.701 (0.070) & 0.751 (0.041) \\ YeastSpotter & 0.561 (0.109) & 0.609 (0.072) & 0.677 (0.043) \\ YeastNet & 0.873 (0.020) & 0.681 (0.029) & 0.732 (0.012) \\ \hline YeastNet2 & **0.883** (0.014) & **0.820** (0.020) & **0.855** (0.012) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: 10-fold cross-validation of segmentation performance of our methods and benchmarks in terms of cell IoU. The table lists mean values with standard deviation values shown in brackets. The highest IoU for each dataset is in bold. Figure 5: Cell segmentation mask of the same colony at different levels of focus.