Lesion Segmentation

C2MA-Net: Cross-modal Cross-Attention Network for Acute Ischemic Stroke Lesion Segmentation based on CT Perfusion Scans

C2MA-Net: Cross-modal Cross-Attention Network for Acute Ischemic Stroke Lesion Segmentation based on CT Perfusion Scans IEEE Transactions on Biomedical Engineering (TBME)
This work adds a cross-modal and cross-attention (C2MA) mechanism into a deep learning network aiming to improve accuracy and efficacy of acute ischemic stroke (AIS) lesion segmentation from CT perfusion maps. The proposed network uses a C2MA module directly to establish a spatial-wise relationship by using the multigroup non-local attention operation between two modal features and performs dynamic group-wise recalibration through group attention block. This study demonstrates the advantages of applying C2MA-network to segment AIS lesions, which yields promising segmentation accuracy and proves the potential of applying cross-modal interactions in attention to assist in identifying new imaging biomarkers for more accurately predicting AIS prognosis in future studies. read more

C2MA-Net: Cross-modal Cross-Attention Network for Acute Ischemic Stroke Lesion Segmentation based on CT Perfusion Scans

C2MA-Net: Cross-modal Cross-Attention Network for Acute Ischemic Stroke Lesion Segmentation based on CT Perfusion Scans IEEE Transactions on Biomedical Engineering (TBME)
This work adds a cross-modal and cross-attention (C2MA) mechanism into a deep learning network aiming to improve accuracy and efficacy of acute ischemic stroke (AIS) lesion segmentation from CT perfusion maps. The proposed network uses a C2MA module directly to establish a spatial-wise relationship by using the multigroup non-local attention operation between two modal features and performs dynamic group-wise recalibration through group attention block. This study demonstrates the advantages of applying C2MA-network to segment AIS lesions, which yields promising segmentation accuracy and proves the potential of applying cross-modal interactions in attention to assist in identifying new imaging biomarkers for more accurately predicting AIS prognosis in future studies. read more