A recurrent network for segmenting the thrombus on brain MRI in patients with hyper-acute ischemic stroke

Sofia Vargas Ibarra, Vincent Martin VIGNERON, Sonia Garcia Salicetti, Hichem Maaref, Jonathan Kobold, Nicolas Chausson, Yann Lhermitte, Didier Smadja

Keywords: hyperacute stroke, brain imagery, MRI modalities, lesion, thrombus, spatial recurrence, deep learning

Abstract: In the stroke workflow, timely decision-making is crucial. Identifying, localizing, and measuring occlusive arterial thrombi during initial imaging is a critical step that triggers the choice of therapeutic treatment for optimizing vascular re-canalization. We present a recurrent model that segments the thrombus in patients suffering from a hyper-acute stroke. A cross-attention module is defined to merge the diffusion and susceptibility-weighted modalities available in Magnetic Resonance Imaging (MRI), which are fed to a modified version of the Convolutional Long-Short-Term Memory (CLSTM) model. It detects almost all the thrombi with a Dice higher than 0.6. The lesion segmentation prediction reduces the false positives to almost zero and the performance is comparable between distal and proximal occlusions.

Submission Number: 145