# An Empirical Evaluation of Nuclei Segmentation from H&E Images in a Real Application Scenario
## 4 Experimental Evaluation
### Datasets  
In our experiments, we evaluate the performance of nuclei segmentation methods on two public histopathology image datasets that are two of the most used for cell nuclei detection and segmentation. MIC17 (available at [[http://miccai.cloudapp.net/competitions/](http://miccai.cloudapp.net/competitions/)]([http://miccai.cloudapp.net/competitions/](http://miccai.cloudapp.net/competitions/))) is the dataset provided for the MICCAI 2017 Digital Pathology Challenge. It includes 32 annotated squared image patches of sizes \(500\times 500\) or \(600\times 600\) that were manually cropped from H&E-stained histopathology Whole Slide Images (WSIs) of four various types of cancer, namely glioblastoma (GBM), lower grade glioma (LGG), head and neck squamous cell carcinoma (HNSC) and lung squamous cell carcinoma (LUSC). The first row of Figure 2 shows an image example for each type of cancer present in the dataset. During our experiments, we used the same partitions already used in [23] that consist of a training set composed by 24 randomly selected images (six for each cancer type) and a testing set composed by the remaining images (two for each cancer type). BNS (available at [[https://peterjacknaylor.github.io/PeterJackNaylor.github.io/2017/01/15/lsbi/](https://peterjacknaylor.github.io/PeterJackNaylor.github.io/2017/01/15/lsbi/)]([https://peterjacknaylor.github.io/PeterJackNaylor.github.io/2017/01/15/lsbi/](https://peterjacknaylor.github.io/PeterJackNaylor.github.io/2017/01/15/lsbi/))), presented by [PERSON] et al. [28], contains 33 manually annotated images that were obtained by randomly cropping the patches with a fixed size of 512 \(\times\) 512 from the original H&E-stained histopathology WSIs. The third row of Figure 2 shows some images present in the dataset. Even in this case, we used the same partitions proposed in [23] that consist of a training set composed of 23 randomly selected samples and a testing set composed by the remaining images.  
Both datasets present H&E images in PNG format, and each image is associated with a file containing the ground truth of the manual segmentation performed by expert pathologists. As can beobserved in the second and fourth lines of Figure 2, the ground truth images contain different labels for each cell, meaning that they can be used to evaluate both the segmentation and counting performances.  
In addition, we also used images that were expressly collected for the HistoDSSP project, and therefore cannot yet be shared publicly. The original WSIs were acquired with an Aperio AT2 scanner with a magnification of \(\times 20\), which was lower than the magnification used to acquire the public image datasets, which was 40\(\times\). Then, from the H&E-stained histopathology WSIs, a series of semiautomated crops with a fixed size of 512 \(\times\) 512 were selected. Some images present in the dataset are shown in Figure 1. Unfortunately, for matters of time and resources, we could not get the manual annotations. Still, this set of images was constructive to visually evaluate the performances of the segmentation methods on images with a different scale from the training images.