Ischemic Stroke Segmentation from a Cross-Domain Representation in Multimodal Diffusion Studies

Localization and delineation of ischemic stroke are crucial for diagnosis and prognosis. Diffusion-weighted MRI studies allow to associate hypoperfused brain tissue with stroke findings, observed from ADC and DWI parameters. However, this process is expensive, time-consuming, and prone to expert observational bias. To address these challenges, currently, deep representations are based on deep autoencoder representations but are limited to learning from only ADC observations, biased also for one expert delineation. This work introduces a multimodal and multi-segmentation deep autoencoder that recovers ADC and DWI stroke segmentations. The proposed approach learns independent ADC and DWI convolutional branches, which are further fused into an embedding representation. Then, decoder branches are enriched with cross-attention mechanisms and adjusted from ADC and DWI findings. In this study, we validated the proposed approach from 82 ADC and DWI sequences, annotated by two interventional neuroradiologists. The proposed approach achieved higher mean dice scores of 55.7% and 57.7% for the ADC and DWI annotations by the training reference radiologist, outperforming models that only learn from one modality. Notably, it also demonstrated a proper generalization capability, obtaining mean dice scores of 60.5% and 61.0% for the ADC and DWI annotations of a second radiologist. This study highlights the effectiveness of modality-specific pattern learning in producing cross-domain embeddings that enhance ischemic stroke lesion estimations and generalize well over annotations by other radiologists.