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首页> 外文会议>IEEE International Conference on Machine Learning and Applications >Two-stage and dual-decoder convolutional U-Net ensembles for reliable vessel and plaque segmentation in carotid ultrasound images
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Two-stage and dual-decoder convolutional U-Net ensembles for reliable vessel and plaque segmentation in carotid ultrasound images

机译：颈动脉超声图像中可靠血管和斑块分段的两阶段和双解码器卷积U-Net集团

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Carotid ultrasound is a screening modality used by physicians to direct treatment in the prevention of ischemic stroke in high-risk patients. It is a time intensive process that requires highly trained technicians and physicians. Evaluation of a carotid ultrasound requires segmentation of the vessel wall, lumen, and plaque of the carotid artery. Convolutional neural networks are state of the art in image segmentation yet there are no previous methods to solve this problem on carotid ultrasounds. We introduce two novel convolutional U-net models for both vessel and plaque from ultrasound images of the entire carotid system. We obtained de-identified images under IRB approval from 226 patients. We isolated a total of 500 ultrasound images spanning the internal, external, and common carotid arteries. We manually segmented the vessel lumen and plaque in each image that we then use as ground truth. In 10-fold cross-validation all models attain over 90% accuracy for vessel segmentation. With a basic convolutional U-Net we obtained an accuracy of 66.8% for plaque segmentation. With our dual-decoder model we see an improvement to 68.8% whereas our two-stage model falls behind at 65.1% accuracy. However, if we gave our two-stage model the true correct vessel as input its plaque accuracy rises to 81.7% suggesting that the method has potential and needs more work. We ensemble our U-Net and dual decoder U-Net models to obtain confidence scores for segmentations. By considering high confidence outputs above the 60% and 80% thresholds the accuracy of our dual decoder U-Net rises to 75.2% and 87.3% respectively. Our work here shows the potential of dual and two-stage methods for vessel and plaque segmentation in carotid artery ultrasound images and is an important first step in creating a system that can independently evaluate carotid ultrasounds.
机译：颈动脉超声是医生使用的筛选模态，以直接治疗在高危患者中预防缺血性脑卒中。这是一个需要高度培训的技术人员和医生的时间密集的过程。颈动脉超声的评价需要颈壁，内腔和颈动脉斑块的分割。卷积神经网络是图像分割中的最先进的，但是没有以前的方法可以在颈动脉超声中解决这个问题。我们为整个颈动脉系统的超声图像引入了两种新型卷积U-NET模型和来自整个颈动脉系统的超声图像。我们在226名患者中获得了IRB批准下的De-Ideratification的图像。我们隔绝了跨越内部，外部和常见颈动脉的500个超声图像。我们手动将船只腔腔和牌匾分割为我们然后用作地面真理。在10倍的交叉验证中，所有型号的所有型号都可以获得超过90％的船舶分割精度。使用基本的卷积U-Net，我们获得了斑块细分的66.8％的准确性。通过我们的双解码器模型，我们看到了68.8％的提高，而我们的两级模型落后于65.1％的准确性。但是，如果我们向我们的两级模型提供了真正的正确船只，则输入其斑块精度上升至81.7％，表明该方法具有潜力，需要更多的工作。我们合奏我们的U-Net和双解码器U-Net模型，以获得分割的置信度分数。通过考虑高于60％和80％阈值的高置信能量，我们的双重解码器U-Net的准确性分别上升至75.2％和87.3％。我们的工作在这里显示了颈动脉超声图像中血管和斑块分段的双级和两级方法的潜力，并且是创造一个可以独立评估颈动脉超声波的系统的重要第一步。

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