Automatic segmentation of hemorrhagic transformation on follow-up non-contrast CT after acute ischemic stroke - Sun, Jiacheng; Werdiger, Freda; Blair, Christopher; Chen, Chushuang; Yang, Qing; Bivard, Andrew; Lin, Longting; Parsons, Mark (Frontiers in Neuroinformatics, 2024)
Background: Haemorrhagic transformation (HT) following reperfusion therapies is a serious complication for patients with acute ischaemic stroke. Segmentation and quantification of haemorrhage provides critical insights into patients' condition and aids in prognosis. This study aims to automatically segment haemorrhagic regions on follow-up non-contrast head CT (NCCT) for stroke patients treated with endovascular thrombectomy (EVT).Methods: Patient data were collected from ten stroke centres across two countries. We propose a semi-automated approach with adaptive thresholding methods, eliminating the need for extensive training data and reducing computational demands. We used Dice Similarity Coefficient (DSC) and Lin's Concordance Correlation Coefficient (Lin's CCC) to evaluate the performance of the algorithm.Results: A total of 51 patients were included, with 28 Type 2 haemorrhagic infarction (HI2) cases and 23 parenchymal hematoma (PH) cases. The algorithm achieved a mean DSC of 0.66 ±0.17. Notably, performance was superior for PH cases (mean DSC of 0.73 ± 0.14) compared to HI2 cases (mean DSC of 0.61 ± 0.18). Lin's CCC was 0.88 (95% CI 0.79-0.93), indicating a strong agreement between the algorithm's results and the ground truth. In addition, the algorithm demonstrated excellent processing time, with an average of 2.7 seconds for each patient case.To our knowledge, this is the first study to perform automated segmentation of posttreatment haemorrhage for acute stroke patients and evaluate the performance based on the radiological severity of HT. This rapid and effective tool has the potential to assist with predicting prognosis in stroke patients with HT after EVT.