# ENSeg: A Novel Dataset and Method for the Segmentation of Enteric Neuron Cells on Microscopy Images
## 7 Conclusions  
In this work, we introduced a new dataset called ENSeg. This dataset presented a novel task to the literature, containing different microscopy images obtained from the Enteric Nervous System (ENS). Studying cells from the ENS presents significant challenges due to the limited availability of specialized techniques in existing literature. Researchers must undertake labor-intensive, time-consuming analyses to assess the effectiveness of new methods designed to explore the effects of chronic degenerative diseases on the human body. To solve such a problem, we developed a new approach that combines the efficiency of automatic instance segmentation models with the versatility of SAM-like architectures. Our goal was to allow human interaction with the system while providing a pipeline that fits the problem we are solving. Therefore, we employed YOLO architectures for generating segmentation candidates that were later combined with SAM architectures, in a schema that may include the fusion of their output instance masks or the input prompts. Our final mDICE score was 0.7877, achieved using YOLOv8-m for candidate selection, SAM-b for segmentation, and a fusion protocol designed to enhance input point prompts by converting them into bounding boxes. This represents a mean increase of 26.74% and 19.14% over our baselines, namely standalone SAM-b point prompt and YOLOv8-m, respectively. However, this score is 10.14% below the upper limit of our solution, defined by SAM-b results obtained from bounding boxes perfectly fitted to the cells (oracle evaluation). These findings indicate that there is still room for improvement in our approach. Future work will aim to fully fine-tune SAM models for this task; design task-specific adapter modules for partial SAM fine-tuning; evaluate the usage of new late fusion techniques; train new candidate generation methods/models; develop an ensemble learning training pipeline for a hybrid approach; validate the developed technique on other ENS cells (e.g., Enteric Glial Cells); and validate our method on the resources (e.g., images and hardware) of new laboratories and by different ENS researchers. Figure 11: Comparison of SAM architectures, used for generating the final segmentation masks. **Author Contributions:** Conceptualization, G.Z.F. ; methodology, G.Z.F., L.N. and Y.Me.G.d.C. ; software, G.Z.F. ; resources, J.N.Z. ; data labeling, I.G.G. and J.N.Z. ; writing--original draft preparation, G.Z.F. ; writing--review and editing, L.N., J.N.Z. and Y.M.e.G.d.C. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was partially funded by Programa de Doutorado Sanduiche no Exterior (PDSE) grant number 88881.846308/2023-01. **Institutional Review Board Statement:** All procedures regarding the images' obtaining were submitted to and approved by the Standing Committee on Ethics in Animal Experimentation at the State University of Maringa (Protocol number 062/2012, protocol date 3 July 2012). **Informed Consent Statement:** Not applicable. **Data Availability Statement:** All source code and detailed information regarding the ENSeg dataset, including both images and object annotations, are readily available on our GitHub page: [[https://github.com/gustavozf/seg-lib](https://github.com/gustavozf/seg-lib)]([https://github.com/gustavozf/seg-lib](https://github.com/gustavozf/seg-lib)) (accessed on 9 December 2024). It is worth mentioning that the code sources are licensed under Apache 2.0, and the ENSeg dataset is licensed under Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). **Acknowledgments:** The authors would like to thank the Coordination for the Improvement of Higher Education Personnel (CAPES)-Finance Code 001, and the National Council for Scientific and Technological Development (CNPq) for partially financing this research. AI or AI-assisted tools were used in parts of the manuscript exclusively for language translation, language editing, and grammar checking.