Siamese model for collateral score prediction from computed tomography angiography images in acute ischemic stroke - Fortunati, Valerio; Su, Jiahang; Wolff, Lennard; van Doormaal, Pieter-Jan; Hofmeijer, Jeanette; Martens, Jasper; Bokkers, Reinoud P. H.; van Zwam, Wim H.; van der Lugt, Aad; van Walsum, Theo (Frontiers in Neuroimaging, 2024)
Imaging biomarkers, such as the collateral score as determined from Computed Tomography Angiography (CTA) images, play a role in treatment decision making for acute stroke patients. In this manuscript, we present an end-to-end learning approach for automatic determination of a collateral score from a CTA image. Our aim was to investigate whether such end-to-end learning approaches can be used for this classification task, and whether the resulting classification can be used in existing outcome prediction models.The method consists of a preprocessing step, where the CTA image is aligned to an atlas and divided in the two hemispheres: the affected side and the healthy side. Subsequently, a VoxResNet based convolutional neural network is used to extract features at various resolutions from the input images. This is done by using a Siamese model, such that the classification is driven by the comparison between the affected and healthy using a unique set of features for both hemispheres. After masking the resulting features for both sides with the vascular region and global average pooling (per hemisphere) and concatenation of the resulting features, a fully connected layer is used to determine the categorized collateral score.