论文标题
使用U-NET模型通过非门控CT扫描中的半监督学习，使用U-NET模型进行自动冠状动脉钙评分
Automated Coronary Calcium Scoring using U-Net Models through Semi-supervised Learning on Non-Gated CT Scans
论文作者
论文摘要
每年，成千上万的无辜者因心脏病发作而死。由于许多当前的医疗计划并不需要在这些扫描中搜索钙化的费用，因此通常无法诊断出心脏病发作会令人们感到惊讶。只有怀疑某人有心脏问题，进行封闭式的CT扫描，否则，患者无法意识到可能的心脏病发作/疾病。虽然更周期性地进行了不合格的CT扫描，但很难检测到钙化，通常是为了在动脉中定位钙化以外的目的。实际上，仅在门控的CT扫描中计算冠状动脉钙化评分，而不是不添加CT扫描。在训练了冠状动脉钙和CT的门控扫描上的UNET模型之后，它在未触及的测试集上获得了0.95的骰子系数。该模型用于预测非核CT扫描，其平均绝对误差（MAE）为674.19，桶分类精度为41％（5类）。通过对图像和图像中存储的信息的分析，得出了数学方程式，并用于自动裁剪心脏位置周围的图像。通过进行半监督的学习，新的裁切的非缝制扫描能够非常类似于封闭式的CT扫描，在MAE中提高了91％的性能（62.38），准确性为23％。
Every year, thousands of innocent people die due to heart attacks. Often undiagnosed heart attacks can hit people by surprise since many current medical plans don't cover the costs to require the searching of calcification on these scans. Only if someone is suspected to have a heart problem, a gated CT scan is taken, otherwise, there's no way for the patient to be aware of a possible heart attack/disease. While nongated CT scans are more periodically taken, it is harder to detect calcification and is usually taken for a purpose other than locating calcification in arteries. In fact, in real time coronary artery calcification scores are only calculated on gated CT scans, not nongated CT scans. After training a unet model on the Coronary Calcium and chest CT's gated scans, it received a DICE coefficient of 0.95 on its untouched test set. This model was used to predict on nongated CT scans, performing with a mean absolute error (MAE) of 674.19 and bucket classification accuracy of 41% (5 classes). Through the analysis of the images and the information stored in the images, mathematical equations were derived and used to automatically crop the images around the location of the heart. By performing semi-supervised learning the new cropped nongated scans were able to closely resemble gated CT scans, improving the performance by 91% in MAE (62.38) and 23% in accuracy.
