# ENSeg: A Novel Dataset and Method for the Segmentation of Enteric Neuron Cells on Microscopy Images
## 3 ENSeg Dataset
Future works may present new training schema as the number of annotated subjects (rats) increases. It is only indicated that images from the same source (animal) are kept in the same data split, to avoid biased results. Even though those samples are randomly selected from the animals' original tissue, we can achieve a higher level of credibility this way. Some image samples taken from the dataset may be seen in Figure 2. In this one, the labeled neuron cells may be highlighted in the overall image. We reinforce that the total quantity of annotated neuron cells differs from the total quantity present in the image. Such samples represent a microscopic view of the randomly selected patches obtained from the animal's small intestine. Therefore, the total number of cells per image may vary. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Animal Tag** & **\# of Images** & **\# of Neurons** & **Average \({}^{*}\)** & **\# of Vertices (Avg.) \({}^{**}\)** \\ \hline
2C & 32 (17.11\%) & 1590 (16.37\%) & 49.68 & 13 \\
4C & 31 (16.57\%) & 1513 (15.58\%) & 48.80 & 13 \\
5C & 31 (16.57\%) & 2211 (22.77\%) & 71.32 & 13 \\
22 WT & 31 (16.57\%) & 1386 (14.27\%) & 44.70 & 10 \\
23 WT & 31 (16.57\%) & 1520 (15.65\%) & 49.03 & 11 \\
28 WT & 31 (16.57\%) & 1489 (15.33\%) & 48.03 & 11 \\ \hline Total & 187 (100\%) & 9709 (100\%) & - & - \\ \hline \hline \end{tabular}  
* Number of neurons divided by the number of images. ** Sum of the number of vertices per neuron, divided by the total number of neurons.  
\end{table} 
Table 1: Total number of image samples and masks per animal tag. Figure 3 illustrates the specific types of noise most commonly observed in the image samples from the ENSeg dataset. The noise level in a given subject (rat) can vary depending on the image acquisition process. Similar variations may also affect the brightness and contrast of the images, as noted in [13]. Examples of such noises include the following:  
* Blur noises caused during the immunostaining process (Figure 3a). This is the most common noise type, found in most image samples. Blur decreases the visibility of the ENC boundaries, directly affecting their separability, and therefore the segmentation performance. * Tear of the tissue (Figure 3b). This noise may compromise the integrity of the contained cells, affecting their shape and possibly the overall quantity. * Presence of blood vessels (Figure 3c). Blood vessels often overlap the ENC on the images, blocking its visibility. Additionally, due to their brightness level, it may mislead the segmentation models in some scenarios. * Cells overlapping (Figure 3d). This occurs when ENCs are captured in a deeper layer of the tissue. This characteristic gives them a low-brightness aspect and decreases their visibility. In the case of one cell overlapping another, the uppermost cell is always preferred for analysis, since its full shape is visible. Finally, to the best of our knowledge, it is important to highlight that this is the first dataset publicly available to the research community containing expert-annotated ENCs for object segmentation. Research on the ENS remains a niche field with significant potential for further automation. The process of capturing these images, which spans extended periods, poses challenges in generating large datasets, making high-volume data collection particularly difficult. This limitation underscores the size of our dataset and highlights the need for innovative, highly adaptive methods, including zero-shot techniques, to address these constraints effectively. The ENSeg dataset will be made publicly available, alongside the developed source code, through a project on the authors' main GitHub repository. Figure 2: ENSeg image samples, with the polygon annotations delimited as red contours: **(a)** 5C; **(b)** 22 WT.