Stroke causes millions of deaths per year and many disabilities nowadays. According to the World Health Organization, stroke is one of the most common causes of death worldwide. \ac{ais} occurs when a blood clot (thrombus) blocks an artery, restricting average blood circulation and initiating brain tissue damage. The region affected by the stroke, referred to as the lesion, can be separated into (1) the penumbra, which represents the brain tissue that is potentially recoverable but has been temporarily injured, and (2) the core, representing irreversibly damaged tissue that cannot be rescued. The lesion's size and location are crucial informations that help the doctors locate the thrombi and decide the treatment. In an emergency case, the pathology is in the hyper-acute phase ($<$6h from the onset of the symptoms)  where the %decisions are made.   Its correct detection and 
treatment is decided choice are time-consuming and each second is crucial for saving lives and improving the life expectancy of the patients and their quality.
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\subsection{Related works on classical image processing techniques}
 As the lesion is present brighter (in the case of DWI) or darker (in ADC), some approaches exploit these properties using non-learning algorithms. Applying unsupervised clustering such as Fuzzy C-means produces considerably good automatic segmentation results \cite{touseadc}.  It is applied on intensity values lower than the histogram peak (healthy brain tissue) using a fixed threshold, and it eliminates the artifacts thanks to several modalities.  Also, anomaly detection can be done on DWI as in \cite{anomaly}, where the Crawford-Howell t-test and comparison of stroke images to healthy controls are used to 
develop a classifier to discriminate the images into stroke or non-stroke categories following the lesion segmentation. For the thrombi,  in \cite{thrombict}, automatic segmentation is defined in three steps: segmentation of the contralateral vasculature; creation of a mask in the occluded arterial segment by mapping the contralateral segmentation using mirror symmetry; and segmentation of the thrombus using intensity-based region growing. In  \cite{thrombict2}, using features from candidates extracted by thresholding and connected component clustering, the classification is done using Random Forest. 

\subsection{Related works on deep learning}
ISLES challenges \cite{isles2022} provide several public datasets that allow to training of lesion segmentation models. To manage 3D inputs, 2D CNN models have been used \cite{2dar}, \cite{2dar2}, the mixture of 2D and 3D architectures have been proposed as  D-Unet \cite{dunet}, DFENet \cite{dfenet},  and also using variations of Unet3D \cite{unetct}, \cite{Factorizer}. In addition, some attention methods have been used as AABTS-Net \cite{aabts}, which incorporates Axial attention \cite{axial}. In PerfUnet\cite{perfunet}, an attention module is employed on the skip-connections to match the dimensions of the encoder and
decoder and in DRANet \cite{DRANet} using a trunk branch and dilated soft mask branch, it adopts the soft attention mechanism. The adaptation of Visual Transformers  (VIT) \cite{vit} to 3D inputs,  UNETR \cite{UneTR}, proposes an architecture for medical applications. For the thrombi, in  \cite{thrombict3}, a Unet3D learns to segment potential candidates and a Convolutional architecture classifies the candidates in no clot, left clot, or right clot. Also, Polar-UNet \cite{polarunet}, uses a CNN to segment thrombi restricting the volume-of-interest (VOI) to the brainstem, or in \cite{hemisphere} patches from two hemispheres are used to segment the thrombi, as a healthy brain should be symmetric. However, all these methods use CT images, where the thrombi and its texture are more difficult to see. Finally, in \cite{jon} Multidirectional Unet was used on a private dataset using MRI, but no distal thrombi were present.


