Abstract WMP29: Detection and Segmentation of Subarachnoid Hemorrhages With Deep Learning

  • Sales Barros R
  • van der Steen W
  • Ponomareva E
  • et al.
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Introduction: About 5% of all strokes are subarachnoid hemorrhages (SAHs). Accurate segmentation and detection of SAH in CT scans is important because the SAH volume is linked to delayed cerebral ischemia and poor patient outcome. SAH segmentation is a difficult task with high interobserver variability. Previous studies reported a limited average Dice of 0.66 between expert radiologists. We studied the use of deep learning for fast and accurate detection and segmentation of SAH in CT scans. Methodology: A convolutional neural network (CNN) was designed for segmentation and detection of SAH in CT scans. This CNN was trained to classify each voxel as “SAH” or “no SAH”. The input of this CNN was the local information around the voxel and its output was the probability that this voxel is “SAH” or “no SAH”. We used active learning for optimal selection of training samples. By doing so, we boosted the learning performance while keeping a balanced representation of both classes of voxels. Results: The CNN segmented 469 scans from SAH patients and achieved an average Dice of 0.62 ± 0.16 [0.19, 0.92] (avg. ± std. [min., max.]). The CNN also segmented 383 scans from patients with repeated bleeding after an initial SAH. Because most of these patients were treated with endovascular coiling, strong metal artefacts were present in these 383 scans. Regardless of these metal artefacts, an average Dice of 0.66 ± 0.19 [0.05, 0.92] was achieved. Finally, the CNN was used for detection of SAH in 35 scans from SAH patients and 35 images from ischemic stroke patients. The proposed CNN achieved an accuracy of 96% for detection of SAH in these 70 images. The CNN took less than 40 seconds to analyze a CT scan. Conclusion: The proposed CNN can produce SAH segmentations with a similar accuracy as expert radiologists. This CNN was able to reach state-of-the-art SAH segmentation results and SAH detection accuracy.




Sales Barros, R., van der Steen, W. E., Ponomareva, E., Boers, A. M., Zijlstra, Ij. J., van der Berg, R., … Marquering, H. A. (2019). Abstract WMP29: Detection and Segmentation of Subarachnoid Hemorrhages With Deep Learning. Stroke, 50(Suppl_1). https://doi.org/10.1161/str.50.suppl_1.wmp29

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