## 3 Materials and Methods
### Experimental Setup
#### 3.2.1 Datasets  
This study evaluated the experiments using the Ischemic Stroke Lesion Segmentation (ISLES) dataset of the challenges in 2015 and 2022, respectively denoted as ISLES 2015 [13] and ISLES 2022 [36]. These challenges were carried out to promote the scientific progress of medical image processing, making the datasets available under an Open Database License. The data are provided on request in the Uncompressed Neuroimaging Informatics Technology Initiative (NIfTI) format, which allows the visualization of the brain in the axial, coronal, and sagittal planes. The ISLES 2015 challenge contains a subtask for segmenting subacute ischemic stroke lesions (SISS). The dataset for this subtask includes MRI scans of 28 patients in the FLAIR, DWI, T1, and T2 modalities and the segmentation masks of the lesions. A medical center supplied the cases with complete data anonymization, and experienced raters prepared the segmentation masks. The ISLES 2022 challenge also includes a subtask of segmenting stroke lesions in multimodal MRI images. This subtask presents the images in NIfTI format and segmentation masks for the data of 250 subjects. The main feature of this dataset is that the images were obtained in multiple imaging centers using three different MRI scan devices, which, in contrast to ISLES 2015, provides high variability in the images' and lesions' size as well as lesions' locations. Healthcare professionals obtained the images of both datasets in clinical imaging routines for stroke patients, and expert raters provided the annotations over the images to conform the segmentation masks. Furthermore, an inter-rater analysis was performed in both datasets before their release to evaluate the annotations and maximize the overall segmentation accuracy. Consequently, these datasets help to evaluate the proposed model's performance under actual clinical conditions. The datasets were processed in the axial, coronal, and sagittal planes as follows to carry out the experiments. For each subject, each slice with at least one value different from zero in the NIfTI file of the masks was taken and transformed into an image in the Portable Network Graphics (PNG) format. Posteriorly, the slices for each MRI modality corresponding to these mask images were normalized, converted to uint16 data type and saved in PNG format. Since this procedure was applied to each plane, three subsets are given for each dataset. Both datasets present high variance in lesion sizes, which involves class imbalance, making them suitable for model evaluation. Figure 2 presents violin plots of the sizes of segmentation masks in each plane to illustrate their distribution. Moreover, the first quartile (Q1), median (Q2), and third quartile (Q3) for each plane and dataset are annotated in the figure. In order to feed the model in the training phase with a balanced number of samples of each mask' size, each subset of the datasets was divided into four categories according to the quartiles given in Figure 2: Small (\(s_{p}<\) Q1), Medium Down (Q1 \(\leq s_{p}<\) Q2), Medium Up (Q2 \(\leq s_{p}<\) Q3), and Large (Q3 \(\leq s_{p}\)), with \(s_{p}\) being the size in pixels of the mask. These categories were considered to comprise the training, validation, and testing sets. Each subset was split into training, validation, and testing sets, followed by a proportion of 30% for testing and 70% for training and validation. Then, this last set was divided into 80% for training and 20% for validation. Consequently, the general proportions for training, validation, and testing sets constitute 56%, 14%, and 30%, respectively. These sets were built by randomly selecting and merging samples from each mask size category using the mentioned proportion. Furthermore, the experiments were evaluated using these ratios with repeated k-fold validation of ten iterations to compute the average and standard deviation of the results. Tables 1 and 2 present the number of samples employed to compose these sets.