## 4 Discussion  
We have implemented and evaluated a CNN-based thrombus detection and segmentation approach on baseline NCCT image data. The accuracy was similar to expert neuroradiologists. Of the two neuroradiologists who evaluated the images, one had highest sensitivity for thrombus identification and thrombus location assessment, while the other had the highest specificity for thrombus detection. In all cases, the network scored second best. The network performed better on sensitivity than specificity, which means that the network is better in detecting true positives than true negatives. The thrombus volumetric agreement, represented by the ICC, of the segmentations of the network was similar to the agreement amongst the neuroradiologists. However, the agreement of the thrombus density as determined with the network was poor and less accurate than the density agreements of the neuroradiologists. This suggests that, even though the thrombus volume was similar, the thrombus was not segmented at the exact same location as the ground truth segmentation. Moreover, the Bland-Altman plots for the CNN show that the segmentation generated by the CNN generally caused an overestimation of the thrombus volume and underestimation of the thrombus density. The segmentations of both neuroradiologists generally underestimated the thrombus volume and overestimated the thrombus density. Overall, the results suggest that our network is less suitable for exact thrombus segmentation and should mainly be used for thrombus detection. The evaluation of correct thrombus segmentation is challenging because of class imbalance. A common segmentation evaluation is the DICE coefficient, which quantifies the overlap between ground truth and segmentation [25]. However, because the number of background voxels is substantially larger than the number of thrombus voxels the DICE score will be strongly penalized for false positives. Because thrombus volume and density have been associated with treatment outcome [26; 27], we have decided that these parameters were more appropriate evaluation parameters. Before the presented CNN was established, we evaluated multiple CNN architectures. Hereby, we varied patch size, learning rate, batch size, dropout rate and inclusion of batch normalization. In the end, the presented CNN architecture performed best in discriminating thrombus from non-thrombus patches. There are a few studies that describe the detection of LVO. [PERSON] et al. published an extensive systematic review in which they summarized the available tools for LVO detection [28]. However, most of these methods do not point out where the thrombus is located. Machine learning methods, such as random forest learning and support vector machines, are used to detect LVO based on the Alberta stroke program early CT score or the detection of diffusion-perfusion mismatch from CT perfusion images. The use of these so-called black-box LVO detection is suboptimal since radiologists have to search for the thrombus location themselves after the detection of the LVO. Moreover, previous approaches detect thrombi in CTA images only, whereas the availability of contrast-enhanced imaging is suboptimal in off-hours situations [29; 30]. [PERSON] et al. [31] presented a thrombus segmentation method with a similar approach. Their network performs thrombus detection based on asymmetry in NCCT images. Their network resulted in a high area under the receiver operating characteristic curve, but low precision-sensitivity area under the curve (AUC). Concretely, the network showed high performance for balanced data, but low performance in unbalanced data. This makes their network less suitable for the segmentation of entire scans. In addition to the approach presented by [PERSON] et al. we combined asymmetry detection with HAS detection. Moreover, the presented architecture of our CNNs was based on AlexNet [21], which is a well-known and validated architecture for image recognition tasks.