Semantic Segmentation of Spontaneous Intracerebral Hemorrhage, Intraventricular Hemorrhage, and Associated Edema on CT Images Using Deep Learning

Kok, Yong En; Pszczolkowski, Stefan; Law, Zhe Kang; Ali, Azlinawati; Krishnan, Kailash; Bath, Philip; Sprigg, Nikola; Dineen, Robert A.; French, Andrew P.

Abstract

This study evaluated deep learning algorithms for semantic segmentation and quantification of intracerebral hemorrhage (ICH), perihematomal edema (PHE), and intraventricular hemorrhage (IVH) on noncontrast CT scans of patients with spontaneous ICH. Models were assessed on 1732 annotated baseline noncontrast CT scans obtained from the Tranexamic Acid for Hyperacute Primary Intracerebral Haemorrhage (ie, TICH-2) international multicenter trial (ISRCTN93732214), and different loss functions using a three-dimensional no-new-U-Net (nnU-Net) were examined to address class imbalance (30% of participants with IVH in dataset). On the test cohort (n = 174, 10% of dataset), the top-performing models achieved median Dice similarity coefficients of 0.92 (IQR, 0.89–0.94), 0.66 (0.58–0.71), and 1.00 (0.87–1.00), respectively, for ICH, PHE, and IVH segmentation. U-Net–based networks showed comparable, satisfactory performances on ICH and PHE segmentations (P . .05), but all nnU-Net variants achieved higher accuracy than the Brain Lesion Analysis and Segmentation Tool for CT (BLAST-CT) and DeepLabv31 for all labels (P, .05). The Focal model showed improved performance in IVH segmentation compared with the Tversky, two-dimensional nnU-Net, U-Net, BLAST-CT, and DeepLabv31 models (P, .05). Focal achieved concordance values of 0.98, 0.88, and 0.99 for ICH, PHE, and ICH volumes, respectively. The mean volumetric differences between the ground truth and prediction were 0.32 mL (95% CI: −8.35, 9.00), 1.14 mL (−9.53, 11.8), and 0.06 mL (−1.71, 1.84), respectively. In conclusion, U-Net–based networks provide accurate segmentation on CT images of spontaneous ICH, and Focal loss can address class imbalance. International Clinical Trials Registry Platform (ICTRP) no. ISRCTN93732214.

Citation

Kok, Y. E., Pszczolkowski, S., Law, Z. K., Ali, A., Krishnan, K., Bath, P., …French, A. P. (2022). Semantic Segmentation of Spontaneous Intracerebral Hemorrhage, Intraventricular Hemorrhage, and Associated Edema on CT Images Using Deep Learning. Radiology: Artificial Intelligence, 4(6), https://doi.org/10.1148/ryai.220096

• Journal Article Type: Article
• Acceptance Date: Sep 12, 2022
• Online Publication Date: Sep 28, 2022
• Publication Date: 2022-11
• Deposit Date: Sep 14, 2022
• Publicly Available Date: Mar 29, 2023
• Journal: Radiology: Artificial Intelligence
• Print ISSN: 2638-6100
• Electronic ISSN: 2638-6100
• Publisher: Radiological Society of North America
• Peer Reviewed: Peer Reviewed
• Volume: 4
• Issue: 6
• DOI: https://doi.org/10.1148/ryai.220096
• Keywords: Artificial Intelligence; Radiology, Nuclear Medicine and imaging; Radiological and Ultrasound Technology
• Public URL: https://nottingham-repository.worktribe.com/output/11195449
• Publisher URL: https://pubs.rsna.org/doi/10.1148/ryai.220096