# ENSeg: A Novel Dataset and Method for the Segmentation of Enteric Neuron Cells on Microscopy Images
## 6 Experimental Results
### Final Remarks and Limitations  
To evaluate the final performance of the proposed approach, Figures 7-11 illustrate each step of the process using the same input image. These results were obtained by applying the pipeline that achieved the best overall metrics: YOLOv8-m for candidate selection, SAM-b for segmenting the ENC, and the prompt refinement fusion protocol. Figure 7a shows the annotated input image, containing a total of 70 labeled ENCs. Figure 7b represents the simulation of user-prompted inputs, where one point prompt is provided per cell. It is worth noting that the points are not centered due to the random validation schema employed, in which points are randomly sampled from within the labeled cell polygons. Figure 8a depicts the segmentation results from the candidate generation step, where 68 cells were pre-segmented. While the majority of cells in the image were successfully identified, the model encountered difficulties in accurately defining the boundaries of individual cells. The resulting polygons, averaging 17 points per polygon compared to 12 in the annotations, often displayed a squared shape that lacked the natural roundness typically observed in the cells. Furthermore, some segmented objects exhibited slight margins of error relative to their original counterparts. These findings are consistent with the low standalone segmentation metrics discussed in previous sections. A comparison of outputs across different YOLO architectures, as shown in Figure 9, reveals that the previously mentioned behavior persists regardless of the version or size used. This observation suggests that, for the application presented here, these architectures struggled to accurately predict the complex shapes of ENCs. One possible explanation for this limitation is the constrained availability of annotated images, which may have hindered effective fine-tuning of the model. Notably, among the architectures evaluated, YOLOv8-m achieved the highest match rate for the visible cluster of cells, with only a single ENC candidate missing. The input point prompts intersected with 62 of the required cells, achieving a match rate of 88.57%. The matched cells are highlighted with green boundaries in Figure 8b. Following prompt enhancement, the bounding boxes of the matched cells were recalculated, and their corresponding point prompts were updated. Figure 10a illustrates the input prompts provided to SAM-b, which include 62 bounding boxes and eight points. The final segmentation results are shown in Figure 10b. Figure 8: Example of output results from candidate generation step. (**a**) Generated candidates (YOLOv8). (**b**) Candidate matches (outlined in green) and non-matches (outlined in red). Figure 7: Example of input images and prompt points. (**a**) Ground truth. (**b**) Point prompts (random schema). The final output segmentation mask reveals that the segmented ENCs exhibit shapes closely aligned with the expected contours. Additionally, the boundaries of the segmented objects are more accurately delineated, showing better alignment with the actual objects compared to the results produced by YOLOv8. However, in some cases, the SAM model merged clusters of cells into a single segmentation object, particularly when large bounding boxes were used. This issue could be mitigated by refining the candidate selection method to better isolate individual cells. Finally, Figure 11 compares the outputs obtained from the tested SAM architectures. From this comparison, it is clear that SAM-Med2D performed poorly in our task, as no individual cells were successfully segmented. Instead, this architecture produced suboptimal masks containing sub-clusters of cells. Additionally, for the input sample presented here, only SAM-b, SAM-I, and SAM2-t yielded satisfactory results for ENC segmentation. The other models, namely SAM-h, SAM2-s, SAM2-b+, and SAM2-l, also segmented additional structures, such as the background and the entire plexus, which led to misleading final results.