Research map / Video / Video segmentation and tracking
Automating Carotid Intima-Media Thickness Video Interpretation With Convolutional Neural Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add the carotid-bulb location constraint to ROI post-processing (ablation) | CNN-based ROI localization · CIMT test videos | ROI localization error (mm): 0.35 -> 0.19 | Helped |
| Binarize input patches using Otsu's method (ablation) | CNN frame selection · CIMT video frame selection | frame selection accuracy | Helped |
| Smooth the frame-probability signal with a Gaussian function (ablation) | CNN frame selection · CIMT video frame selection | frame selection accuracy | Helped |
| Use Gaussian smoothing with a large degree (ablation) | CNN frame selection · CIMT video frame selection | frame selection sensitivity | Hurt |
| Use deeper CNN architectures such as AlexNet (ablation) | CIMT interpretation CNNs · CIMT video interpretation | performance | No clear effect |
| Add open snakes to refine CNN-predicted interface boundaries (ablation) | CNN interface segmentation · CIMT interface segmentation | interface segmentation accuracy | Helped |
| Use 360×360 µm image patches for interface segmentation (ablation) | CNN interface segmentation · CIMT interface localization | localization error | Helped |
| Use 1.8×1.8 cm patches for ROI localization (ablation) | CNN ROI localization · CIMT ROI localization | localization error | Helped |
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