1 Concordia University, Gina Cody School of Engineering and Computer Science, 1455 De Maisonneuve Blvd. W., Montreal, H3g 1M8, Quebec, Canada. Electronic address: m_ktar@encs.concordia.ca.
2 Concordia University, Gina Cody School of Engineering and Computer Science, 1455 De Maisonneuve Blvd. W., Montreal, H3g 1M8, Quebec, Canada.
3 Queens University, Department of Diagnostic Radiology, Kingston Health Sciences Centre, Kingston General Hospital 76 Stuart Street Kingston, K7L 2V7, Ontario, Canada.

This study conducts collateral evaluation from ischemic damage using a deep learning-based Siamese network, addressing the challenges associated with a small and imbalanced dataset. The collateral network provides an alternative oxygen and nutrient supply pathway in ischemic stroke cases, influencing treatment decisions. Research in this area focuses on automated collateral assessment using deep learning (DL) methods to expedite decision-making processes and enhance accuracy. Our study employed a 3D ResNet-based Siamese network, referred to as SCANED, to classify collaterals as good/intermediate or poor. Utilizing non-contrast computed tomography (NCCT) images, the network automates collateral identification and assessment by analyzing tissue degeneration around the ischemic site. Relevant features from the left/right hemispheres were extracted, and Euclidean Distance (ED) was employed for similarity measurement. Finally, dichotomized classification of good/intermediate or poor collateral is performed by SCANED using an optimal threshold derived from ROC analysis. SCANED provides a sensitivity of 0.88, a specificity of 0.63, and a weighted F1 score of 0.86 in the dichotomized classification.