# ENSeg: A Novel Dataset and Method for the Segmentation of Enteric Neuron Cells on Microscopy Images
## 1 Introduction  
The Enteric Nervous System (ENS) is a specialized division of the autonomic nervous system, intricately woven throughout the gastrointestinal tract. Often referred to as the \"second brain\", the ENS operates independently of the central nervous system, showcasing its remarkable autonomy. Tasked with regulating digestive processes, it coordinates a variety of movement patterns within the digestive tract. These include rapid propulsion of contents (peristalsis), mixing movements (segmentation), slow propulsion, and retrepulsion, the latter aiding in the expulsion of harmful substances, such as during vomiting [1]. The ENS consists of two primary networks, commonly referred to as neural plexuses. The first, the myenteric (or Auerbach's) plexus, is situated between the longitudinal and circular muscle layers. The second, the submucosal (or Meissner's) plexus, resides within the submuc. The myenteric plexus governs gastrointestinal motility, while the submucosal plexus primarily regulates secretion and local blood flow within the gastrointestinal tract [1]. Within the ENS, two main cell types predominate: Enteric Neuron Cells (ENCs) and Enteric Glial Cells (EGCs). These cells are essential for maintaining the homeostasis of gastrointestinal tract (GIT) functions [2]. In preclinical research, the analysis of ENS cells is primarily conducted to evaluate new methodologies and techniques in animal models before progressing to human trials. This approach helps mitigate the risk of mortality or permanent disabilities in human subjects. Neurogastroenterology researchers often rely on images of ENCs and EGCs to examine the visual effects of various diseases and assess the efficacy of emerging medical treatments. Images of enteric cells are particularly valuable in such studies due to the significant impact of chronic degenerative diseases (e.g., diabetes mellitus [3; 4; 5], rheumatoid arthritis [6; 7], cancer [8; 9; 10; 11], etc.) on these cells. This versatility enables researchers to use the same source material across multiple research disciplines, broadening the scope and applicability of their findings. Chronic degenerative diseases significantly affect both the shape and quantity of ENCs and EGCs. For that reason, researchers conduct morphometric and quantitative analyses to evaluate the health status of target animals. However, these processes are predominantly manual, labor-intensive, and highly repetitive, making them both exhausting and time-consuming. This highlights the critical need for developing computational models that can perform these tasks automatically and efficiently [12]. The described problem here can be reduced to an instance segmentation task. By segmenting each cell individually, we could ease the process of analyzing them on both their shape and quantity. Although previous works have been conducted aiming to classify images from the ENS [12; 13], the lack of available data with expert-annotated segmentation masks increases the challenge of automatically performing such a task. Keeping that in mind, in this work, we present a novel dataset from the ENS, called Enteric Nervous System-Segmentation (ENSeg). This one represents an upgraded subset of the ENS dataset published in [13], containing images of ENC labeled for instance segmentation. In total, 187 images were annotated by the same laboratory in which they were originally obtained, presenting 9709 individually tagged ENCs. Datasets annotated for segmentation tasks in the ENS domain are notably scarce in the literature. To the best of our knowledge, this is the first dataset to be openly accessible to the research community. In addition to the dataset, we present a novel approach that aims to deal with the proposed task and present baseline results to ENSeg. Our method consists of using instance segmentation models, e.g., YOLOv8 [14], for candidate generation and using user prompts to select them.