# ENSeg: A Novel Dataset and Method for the Segmentation of Enteric Neuron Cells on Microscopy Images
## 6 Experimental Results
### Baseline Results  
In this section, we evaluate the stand-alone segmentation models to establish baseline results and better assess the results from the proposed approach. Figure 5 shows the metrics obtained by YOLOv8, YOLOv9, and YOLOv11, calculated by averaging the DICE values of each fold, following our validation schema (leave-one-patient-out cross-validation). In this one, it is possible to observe that all values were inferior to 0.7. Since we do not have all cells labeled in our dataset, we can assume that suboptimal models were found during training. Such an assumption is made based on the fact that the architectures could identify ENC outside the annotated set. Therefore, during training, such objects would be identified as wrong defections, causing noise in the calculation of training error. Since YOLOv8-m performed as well as the other architectures while having a smaller standard deviation and acceptable latency (when compared to larger versions), this one was selected to run the candidate selection on experiments presented in Section 6.2. Figure 6 presents the obtained mDICE for SAM, SAM2, and SAM-Med2D, averaged from the oracle evaluation of each validation set. When analyzing it, it is possible to notice that SAM-Med2D underperformed compared to the regular SAM and SAM2 models. Although it was fine-tuned on medical datasets, we presume that its reduced input size and lack of similar microscopy image data influenced this result. This affirmation is based on the fact that ENSeg images differ from regular microscopy images (e.g., the ones with H&E coloration) and therefore should require additional tuning to enhance such representation. Figure 5: YOLOv8 (red), YOLOv9 (yellow), and YOLOv11 (blue) baseline metrics on annotated cells. Figure 6: SAM baseline metrics (oracle evaluation method). When checking the prompt types, it is possible to observe that by using bounding boxes as prompt inputs, we can reach higher mDICE values. Such behavior matches what was reported by the original authors. Also, the combination of bounding boxes and click prompts (by simultaneous input) did not improve the metrics. Additionally, when comparing the versions of SAM, we may notice that although they have different sizes, their performance is similar. The obtained metrics also show that SAM slightly outperformed SAM2. Therefore, throughout the rest of this paper, we choose to use SAM-b, since it presents a better mDICE for both points and bound box prompts and has a smaller latency when compared to the other SAM variations (i.e., SAM-I and SAM-h). Table 2 presents our final baseline results. Click (point) prompts are obtained with the random evaluation schema since it is closer to human behavior. As reported in previous sections, these are preferable on the ENSeg dataset. The bounding boxes on the other hand have their values presented from the oracle evaluation since they represent an upper limit to our results. In this table, it is possible to observe that the mean difference from the bound box to the click prompt type is equivalent to 0.2448 (mIoU) and 0.2551 (mDICE). This difference in performance justifies our protocols with prompt refinement, in which we try to leverage the performance of point prompts on the here-approached task. It is also possible to notice that the WT subjects have an average mDICE difference of 0.3563, while for the C subjects, it is equivalent to 0.1540 (79.26% smaller). The WT subjects often present a higher level of unwanted noises (as described in Section 3) and lower sharpness when compared to C, increasing the challenge of segmenting ENCs on them. Table 3 shows the total number of objects detected by the trained YOLOv8-m model. It can be observed that, in most evaluation sets (animal tags), YOLOv8 detected more objects than were originally labeled, with an average of 11.26% additional ENCs. In our task, this over-detection is interpreted as generating a variety of segmentation candidates, which will later be filtered during the overall process and excluded from the final mask.