Stroke causes millions of deaths and cerebral palsy per day nowadays. \Ac{ais} occurs when a blood clot (thrombus) blocks an artery, restricting average blood circulation and initiating brain tissue damage. The lesion caused by the stroke can be separated into (1) the penumbra, which represents the brain tissue that is potentially recoverable but temporarily injured, and (2) the core, the irreversibly damaged tissue. The lesion's size and location are crucial pieces of information 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) which is critical for saving lives and  %where the decisions are made.   Its correct detection and treatment choice are time-consuming and each second is crucial for saving lives and 
improving life expectancy of patients.

This paper proposes an experimentally validated and computationally affordable method capable of segmenting the thrombus of an \ac{ais} patient with few false positives using lesion segmentation as support. This technique is based on a light spatial recurrent neural network architecture.

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\subsection{Related works on classical image processing techniques}
As the lesion is  brighter (in the case of DWI) or darker (in ADC) depending on the MRI modalities, some approaches exploit these properties using non-learning algorithms. For instance, 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 the 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,   \cite{thrombict} defined an automatic segmentation method 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} Random Forests are used to classify features extracted by thresholding and connected component clustering. 

\subsection{Deep learning works related to brain imagery}
ISLES challenges \cite{isles2022} provide several public datasets that allow the 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}. Attention methods such as AABTS-Net \cite{aabts} have been used, incorporating axial attention \cite{axial}, skip-connections as PerfUnet \cite{perfunet} or soft-attention mechanisms as DRANet \cite{DRANet}. UNETR proposed by \cite{UneTR} is an architecture adapted from  Visual Transformers \cite{vit} to 3D inputs for medical applications. But, all these methods rely on considerable big datasets and neither of them is only focused on the hyperacute phase, where the delineation of the affected area is more difficult to do \cite{DWIPitfalls}. On the other hand, for the thrombi segmentation,  \cite{thrombict3}  learns to segment potential candidates using Unet3D and a Convolutional architecture classifies the candidates.  Polar-UNet \cite{polarunet}, uses a CNN to segment thrombi restricting the volume-of-interest to the brainstem, or in \cite{hemisphere} patches from two hemispheres are used to segment the thrombi, as a healthy brain tissue should be symmetric. However, all these methods use CT images. For MRis in \cite{jon} Multidirectional Unet was used and a recurrent method also denoted as Logic LLSTM \cite{thesis} but no distal thrombi were present.


