# Learning to Segment Blob-like Objects by Image-Level Counting
## 5 Discussion  
We presented a method for implicit segmentation training based on image-level annotations and compared it with a strongly supervised segmentation method. The evaluation was performed on three types of segmentation networks, two CNNs and one transformer-based network, on natural microscopy images, as well as synthetic data. The strongly supervised segmentation training showed the most-stable results for the synthetic data: the F1-scores differed only slightly for the different network architectures, and there was no significant change in the F1-scores for the different sizes within one network architecture. When evaluated with natural data, the configurations achieved more-varying results in the purely spatial F1-scores, as well as the F1-scores based on the temporally linked tracks. We evaluated a straightforward candidate for a solution using CAM to obtain segmentations from a counting network. In using a self-developed CAM-based approach, we showed that this methodology was able to achieve high F1-scores in certain cases, but was unsuitable when applied to natural data since it does not allow for controlling the extracted  
Figure 10: Undesired shapes in the segmentation results of two natural example images. The networks were trained only using the counting loss. The segmentations were generated using the Score-CAM approach. Figure 9: Unexpected shapes in the segmentation results of three synthetic example images. The segmentations were extracted with Score-CAM after training only using the counting loss. segmentation shapes. As a result, it may split a segmentation into fragments around the actual object when objects show unsharp edges. In the evaluated natural data, this effect was caused by local wave-like artifacts around objects. Even in the synthetic case, repeating the training can lead to undesirable shapes in the segmentation results, which cannot be controlled without modifying the underlying method. Therefore, we discourage its use beyond the scope of analyzing the reasons for a network output. Training our weakly supervised approach based on additional losses, i.e., the contrast loss and the morphological loss, avoided undesired shapes by preferring high-contrast segmentations with blob-like structures. Despite an increased controllability of the segmentation results compared to the CAM-based approach, this type of training also led to more-divergent results than the strongly supervised approach when training multiple times with the same configuration. The size of a segmented object may vary with each training, as the morphological loss is only limited to blob-like shape characteristics, but not their exact sizes. If the determination of the exact sizes is of minor importance, this method can significantly reduce the time needed to generate annotations. The presented morphological loss included more knowledge of the desired results than the counting loss and the contrast loss as it contained a vague expectation of the blob-like shape of objects and was, therefore, not suitable for all other use cases. Nevertheless, transferability to other application contexts where similar shapes are expected, e.g., other laboratory analyses such as fluorescence microscopy images, is expected. To assess the transferability of the results, we also have to be aware of the data used. The synthetic examples offer a fixed data characteristic and an arbitrary number of objects and, by that, advantages that are usually not achievable in real applications. That means the results on these data showed a possibility rather than values that can be expected on natural data. For the feasibility check on natural data, a limited amount of datasets with rather low intensities of image artifacts was examined. Therefore, the findings cannot be considered generalizable without restrictions, but we expect that similar result characteristics can be found with other data and that improvements can be achieved with an increased amount and variety in the characteristics of the training data and optimized architectures.