## 1 Introduction  
Stroke remains the second-leading cause of death globally, with 101 million prevalent cases in 2019 [1], and showed significant increases in its prevalence and incidence rates in people younger than 70 years between 1990 and 2019, with an even faster increase from 2010 to 2019. Among all stroke cases, 10-15% belong to intracerebral hemorrhage (ICH), which demonstrates greater mortality and morbidity rates [2]. In ICH, patient outcome is highly dependent on the final hematoma volume, which can grow since the time of admission and the first diagnostic imaging scan. Hematoma expansion (HE) is defined as a relative increase in hematoma volume \(>33\%\) or an absolute increase \(>6\) mL [3]. It occurs in 30-38% of patients with ICH, and it can drastically worsen functional outcome and lead to disability and death [4]. For instance, for each 1 mL of absolute increase in hematoma volume, patients are 7% more likely to worsen from independence to poor outcome [5]. Therefore, it is crucial to rapidly identify patients at risk of HE at the time they present with intracerebral hemorrhage. Computed tomography (CT) is the prevalent modality for hemorrhagic stroke diagnosis [6], as it is widely available and allows for rapid and inexpensive image acquisition. It can provide prognostic information that might help identifying patients at risk of HE. Nowadays, investigation of different imaging markers that could predict the expansion of hematoma is a trending research topic. Various radiological markers have been proposed; for instance, the spot sign, identified from CT angiography (CTA) images, is the most currently studied radiological predictor of HE [7, 8]. However, the sensitivity of CTA spot sign to HE remains low, and CTA is not systematically performed for stroke diagnosis and may not even be available in some centers [8; 9]. Additionally, several CT HE markers have been developed to predict the risk of HE. For instance, the blend sign is a relatively hypoattenuated area next to a hyperattenuated area of the hematoma, with a well-defined margin between the two regions [10]; the swirl sign is the presence of one or more rounded or irregular regions of hypoattenuation or isoattenuation within the hemorrhage [10]. Another HE imaging biomarker is the black hole sign, which is defined as a low-density area wrapped in the high-density area with an identifiable border [3]. Some other examples of HE prediction biomarkers are the island sign, hypodensity, and the satellite sign [9]. Even though these CT imaging markers seem to be promising for HE prediction, their clinical relevance and contribution to the field are still under investigation and require further research [3; 9; 11]. Current clinical practice suggests the acquisition of initial CT upon arrival of the patient to the hospital and then repetition of the scan within 24 h to evaluate the development of HE, particularly in patients that show neurological deterioration [11]. From a clinical point of view, it is relevant to predict HE growth from the initial image and, a more challenging yet valuable task, to predict the final HE of the lesion, which can provide more comprehensive information on the extent and pattern of its growth and facilitate better clinical decision making. There are several research works predicting the final extent of the lesioned area in ischemic stroke [12; 13]. However, to the best of our knowledge, there is only one work, developed by [14], which tackled the problem of hemorrhagic stroke growth prediction. Their approach is based on the assumption that the follow-up hematomas are based on the initial hematoma's spatial characteristics and displacements. Their algorithm is a deep learning approach based on an encoder-decoder framework which learns a displacement vector field (DVF), which is used to warp the baseline CTs with the corresponding hematoma mask into the predicted follow-up image and its lesion mask.