# Learning to Segment Blob-like Objects by Image-Level Counting
## 4 Experiments and Results
### Results
#### 4.3.2 Results on Natural Images  
We evaluated the training approaches on the natural data described in Section 4.2.1 using 11 datasets with and 18 datasets without objects of interest and selected 2/3 (7, respectively 12) of the datasets for training and 1/3 (4, respectively 6) for validation. In the training phase, only object counts at the image level were used. The datasets with particle occurrences consisted of a total of 11,133 images with a sum of 1909 particle trajectories, which provided a minimum of 0 and a maximum of 37 objects on an image. The particle-free datasets contained 22,572 images in total. In the end, we assessed the segmentation quality of all positive datasets using the segmented annotations. In each training step, we chose a random example from a random dataset, alternately using images with and without particles so that both cases occurred equally often. In the training, we extracted patches with a maximum side length of 512 px for each side. In the evaluation, we used the full images of the validation sets to compare the annotated segmentations with the implicitly learned ones. When using TransUNet, the patches that were cut out had to be padded with random noise for side lengths below 512 since this network only supports a constant image size given at construction time. The other networks can handle different input sizes directly. Since our focus was on the usability of the presented learning approaches, we selected only datasets that showed comparatively low artifact intensities. In this way, we avoided effects that are caused by high-intensity artifacts and not by the training methods themselves. Despite this selection, image artifacts can never be completely prevented in real-world recordings. To deal with this, we applied an augmentation approach for increasing the robustness of a network against artifacts [40] that was also used in the creation of synthetic  
Figure 7: Example input (**a**), the mask of generated objects (**b**), segmentations created using the CAM-based approach (**c**-**e**), and the approach using the counting loss, the contrast loss, and the morphological loss (**f**-**h**). In the overlayed images (**i**-**k**), the results of the CAM-based approach are represented with blue pixels, and the results of the loss-based approach are marked red. Pixels where positive segmentation values were generated for both approaches are shown in purple. images (Section 4.2.2). It was applied randomly to the training samples with a probability of 50% in each training step. Unlike the synthetic data, the natural images have different sizes. The smallest images have a size of 450 \(\times\) 170 px and the largest 1692 \(\times\) 400 px. The reason is that, during the execution of the experiments in which the images were recorded, a free selection of the focal region took place by the person performing the experiments. Therefore, we used the full image sizes, which are different for each dataset, in the final evaluation. For the analysis of the results with natural data, we additionally computed the F1-score on tracks as described in previous work [31]: a track is composed of detections from consecutive frames when the bounding boxes around these detections overlap significantly. We tolerated periods of up to three frames of missing detection before splitting a candidate track. After the construction, we discarded all tracks that appeared shorter than ten frames since we saw those occurrences as short-time disturbances based on the knowledge of typical track lengths. The track-related F1-score is a less-direct reflection of the segmentation results, but is an indicator of the usability in real-world applications, where short-term misdetections are filtered out in this way [31]. The overall values for the spatial and the tracking results were first averaged for each dataset individually and, then, combined by averaging these results so that the datasets with different numbers of images were weighted equally.