  Stroke segmentation plays a crucial role in the diagnosis and treatment of
stroke patients by providing spatial information about affected brain regions
and the extent of damage. Segmenting stroke lesions accurately is a challenging
task, given that conventional manual techniques are time consuming and prone to
errors. Recently, advanced deep models have been introduced for general medical
image segmentation, demonstrating promising results that surpass many state of
the art networks when evaluated on specific datasets. With the advent of the
vision Transformers, several models have been introduced based on them, while
others have aimed to design better modules based on traditional convolutional
layers to extract long-range dependencies like Transformers. The question of
whether such high-level designs are necessary for all segmentation cases to
achieve the best results remains unanswered. In this study, we selected four
types of deep models that were recently proposed and evaluated their
performance for stroke segmentation: a pure Transformer-based architecture
(DAE-Former), two advanced CNN-based models (LKA and DLKA) with attention
mechanisms in their design, an advanced hybrid model that incorporates CNNs
with Transformers (FCT), and the well-known self-adaptive nnUNet framework with
its configuration based on given data. We examined their performance on two
publicly available datasets, and found that the nnUNet achieved the best
results with the simplest design among all. Revealing the robustness issue of
Transformers to such variabilities serves as a potential reason for their
weaker performance. Furthermore, nnUNet's success underscores the significant
impact of preprocessing and postprocessing techniques in enhancing segmentation
results, surpassing the focus solely on architectural designs
