# ENSeg: A Novel Dataset and Method for the Segmentation of Enteric Neuron Cells on Microscopy Images
## 3 ENSeg Dataset  
The dataset used here represents an enhanced subset of the ENS dataset [13]. According to the authors, the ENS dataset comprises image samples extracted from the ENS of male adult Wistar rats (Rattus norvegicus, alibus variety), specifically from the jejunum, the second segment of the small intestine. The original dataset consists of two classes: sick animals with cancer, induced with Walker-256 Tumor (WT), and control animals (C). Image acquisition involved 13 different animals, with 7 belonging to the C class and 6 to the WT class. Each animal contributed 32 image samples obtained from the myenteric plexus. All images were captured using the same setups and configuration, and saved in PNG format, with a spatial resolution of 1384 \(\times\) 1036 pixels. The overall process of obtaining the images and performing the morphometric and quantitative analyses takes approximately 5 months, performing the following steps:  
1. Treatment, euthanasia, and preparation of animal samples (1 month);
2. Dissection of the small intestine (2 weeks);
3. Immunostaining of tissues (2 weeks);
4. Image capture (1 month);
5. Quantitative and morphometric analyses (2 months). It is essential to note that the dataset was developed under ethical principles outlined in Brazilian federal law (Law 11,794 (October 2008) and Decree 66,689 (July 2009)), as established by the Brazilian Society of Science on Laboratory Animals (SBCAL). All procedures were submitted to and approved by the Standing Committee on Ethics in Animal Experimentation at the State University of Maringa (Protocol number 062/2012). Following the experimental period, the animals were euthanized and the jejunum was collected for the immunostaining process. Finally, the animals were frozen and subsequently incinerated. More details about the dataset, including the obtaining process and protocols, may be found in the original papers [12; 13]. Our dataset version considers expert-annotated labels for 6 animals, tagged as 2C, 4C, 5C, 22 WT, 23 WT and 28 WT. The image labels were created by members from the same laboratory in which the images originated, i.e., researchers from the Enteric Neural Plasticity Laboratory of the State University of Maringa (UEM). Labels were designed using labelme [63] and consisted of polygons annotated around each body cell. To keep the labeling paired with the laboratory's analysis quality standards, only the neuron cells with well-defined borders were considered in the final label masks. The labeling process lasted 9 months (executed from November 2023 to July 2024) and was iteratively reviewed by the lab lead researcher. The generated files were later adapted for training and evaluating segmentation models. In the case of instance segmentation models based on the generation of point coordinates (e.g., YOLOv8), a list of \(x,y\) points per object in the image, normalized by its original height and width, is required for training. Individual segmentation masks are created for each object to extract metrics from the segmented objects. Such individual masks are created following a similar pipeline, as described in SA-Med2D-20M [58]. But, different from this one, we do not remove small masks, considering that we aim to preserve the expert's annotations' integrity. After executing this process, the full dataset contains a total of 187 images and 9709 cell annotations. Table 1 presents the total number of image samples and ENCs per animal tag. Due to the restricted quantity of animal samples and the natural split of images, it is recommended that during training, a leave-one-out cross-validation method is used to have more reliable results. Keeping that in mind, 6 training sessions are executed per experiment. In each one, images from the same subject are isolated and used for testing the resulting model, while the remaining images are used for training. It is worth mentioning that the usage of cross-validation is recommended, but not mandatory.