Detection and Segmentation of Lesion Areas in Chest CT Scans For The Prediction

Detection and Segmentation of Lesion Areas in Chest CT Scans For The Prediction of COVID-19

In this paper we compare the models for the detection and segmentation of Ground Glass Opacity and Consolidation in chest CT scans. These lesion areas are often associated both with common pneumonia and COVID-19.We train a Mask R-CNN model to segment these areas with high accuracy using three approaches: merging masks for these lesions into one, deleting the mask for Consolidation, and using both masks separately. The best model achieves the mean average precision of 44.68% using MS COCO criterion for instance segmentation across all accuracy thresholds. The classification model, COVID-CT-Mask-Net, which learns to predict the presence of COVID-19 vs common pneumonia vs control, achieves the 93.88% COVID-19 sensitivity, 95.64% overall accuracy, 95.06% common pneumonia sensitivity and 96.91% true negative rate on the COVIDx-CT test split (21192 CT scans) using a small fraction of the training data. We also analyze the effect of Non-Maximum Suppression of overlapping object predictions, both on the segmentation and classification accuracy. The full source code, models and pretrained weights are available on https://github.com/AlexTS1980/COVID-CT-Mask-Net.

胸部CT扫描中病变区域的检测和分割，以预测COVID-19

在本文中，我们比较了胸部CT扫描中用于检测和分割毛玻璃不透明度和固结的模型。这些病变区域通常与常见的肺炎和COVID-19有关。.. 我们使用三种方法训练一个Mask R-CNN模型，以高精度对这些区域进行分割：将这些病变的掩模合并为一个，删除用于合并的掩模，以及分别使用两个掩模。最好的模型使用MS COCO标准对所有准确度阈值进行实例分割，可实现平均平均准确度为44.68％。通过分类模型COVID-CT-Mask-Net，可以预测COVID-19与普通肺炎与对照之间的关系，可达到93.88％的COVID-19敏感性，95.64％的总体准确度，95.06％的普通肺炎敏感性和96.91％使用一小部分训练数据在COVIDx-CT测试分割（21192次CT扫描）上的真实阴性率。我们还分析了重叠目标预测的非最大抑制对分割和分类准确性的影响。 （阅读更多）