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Semi‐automated infarct segmentation from follow‐up noncontrast CT scans in patients with acute ischemic stroke
Author(s) -
Kuang Hulin,
Me Bijoy K.,
Qiu Wu
Publication year - 2019
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.13703
Subject(s) - segmentation , artificial intelligence , voxel , random forest , convolutional neural network , medicine , pattern recognition (psychology) , image segmentation , computer science , radiology
Purpose Cerebral infarct volume observed in follow‐up noncontrast computed tomography (NCCT) scans of acute ischemic stroke (AIS) patients is as an important radiologic outcome measure of the effectiveness of endovascular therapy (EVT). In this paper, our aim is to propose a semiautomated segmentation approach that can accurately measure ischemic infarct volume from NCCT images of AIS patients. Methods A novel cascaded random forest (RF) learning is first employed to classify each voxel into normal or ischemic voxel, leading to an infarct probability map. Four types of features: intensity, statistical information in local region, symmetric difference compared to the contralateral side, and spatial probability of infarct occurrence generated by the STAPLE method, are extracted. These features are input into RF to train a first‐stage classifier. The coarse segmentation results generated by the first‐stage classifier are then used to train a fine second‐stage classifier with fivefold cross validation. The RF estimated infarct probability map obtained in the second‐stage testing as well as user input high‐level knowledge are then incorporated into a convex optimization function to obtain final segmentation. One hundred AIS patients were collected in this study, of which 70 patient images were used for evaluation while the remaining 30 patient images were used for RF training. Results Quantitative results show that the proposed approach is capable of yielding a dice coefficient (DC) of 79.42%, significantly outperforming some state‐of‐the‐art automated segmentation methods, such as the RF‐based methods and convolutional neural network (CNN)‐based segmentation method, U‐net. The infarct volume obtained by the proposed method is strongly correlated with the manually segmented volume. In addition, interobserver variability analysis initialized by two observers suggests low user dependency. Conclusions Our proposed semiautomated segmentation method can accurately segment infarct from NCCT of AIS patients.