Evaluating nnU-Net for early ischemic change segmentation on non-contrast computed tomography in patients with Acute Ischemic Stroke
| Autoři | |
|---|---|
| Rok publikování | 2022 |
| Druh | Článek v odborném periodiku |
| Časopis / Zdroj | Computers in Biology and Medicine |
| Fakulta / Pracoviště MU | |
| Citace | |
| www | https://www.sciencedirect.com/science/article/pii/S0010482521008271?via%3Dihub |
| Doi | http://dx.doi.org/10.1016/j.compbiomed.2021.105033 |
| Klíčová slova | Machine learning; Deep learning; Computer vision; Segmentation; Neurovascular imaging; Computed tomography; Acute ischemic stroke; Brain lesion |
| Popis | Identifying the presence and extent of early ischemic changes (EIC) on Non-Contrast Computed Tomography (NCCT) is key to diagnosing and making time-sensitive treatment decisions in patients that present with Acute Ischemic Stroke (AIS). Segmenting EIC on NCCT is however a challenging task. In this study, we investigated a 3D CNN based on nnU-Net, a self-adapting CNN technique that has become the state-of-the-art in medical image segmentation, for segmenting EIC in NCCT of AIS patients. We trained and tested this model on a sizeable and heterogenous dataset of 534 patients, split into 438 for training and validation and 96 for testing. On this test set, we additionally assessed the inter-rater performance by comparing the proposed approach against two reference segmentation annotations by expert neuroradiologist readers, using this as the benchmark against which to compare our model. In terms of spatial agreement, we report median Dice Similarity Coefficients (DSCs) of 39.8% for the model vs. Reader-1, 39.4% for the model vs. Reader-2, and 55.6% for Reader-2 vs. Reader-1. In terms of lesion volume agreement, we report Intraclass Correlation Coefficients (ICCs) of 83.4% for model vs. Reader-1, 80.4% for model vs. Reader-2, and 94.8% for Reader-2 vs. Reader-1. Based on these results, we conclude that our model performs well relative to expert human performance and therefore may be useful as a decision-aid for clinicians. |