We have proposed a model relating the lesion and the thrombi segmentation on MRIs that outperforms the other methods tested. A cross-attention between the modalities as a recurrent model where the longitudinal direction matches time and a way of merging the lesion and the thrombus is defined.  Att\ac{llstm}  detects almost all thrombi present in stroke patients in less than 3 minutes, producing promising results in the hyper-acute phase, for distal and proximal clots. This detection could save time, help to choose a treatment or estimate the survival of the patients.  
% but the comparisons of the Att\ac{llstm} with the other architectures are not completely fair, as, for that model, the DWI additional modality is included in the inputs. As we wanted to imitate the doctors' procedure and our proposal of \ac{llstm} is smaller, it is the architecture that allows us to include that module. % 

 But, due to the lack of public datasets, the multicentric robustness cannot be tested, even though augmentations and normalizations have been defined to overcome that problem. MRIs from different vendors will not only produce different images due to calibration but the susceptibility modalities can also be different: SWI instead of SWAN. The two postprocessing proposed techniques could be done differently: for example,  merging the lesion and thrombus with a learning method or improving the Dice with another technique.

%To conclude, the model proposed mixes the thrombi and lesion segmentation. It produces a good performance for patients in the hyper-acute phase, where the treatment is chosen and the time is crucial.