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Automatic Classification of Symmetry of Hemithoraces in Canine and Feline Radiographs

2023-02-24 22:46:16
Peyman Tahghighi, Nicole Norena, Eran Ukwatta, Ryan B Appleby, Amin Komeili

Abstract

Purpose: Thoracic radiographs are commonly used to evaluate patients with confirmed or suspected thoracic pathology. Proper patient positioning is more challenging in canine and feline radiography than in humans due to less patient cooperation and body shape variation. Improper patient positioning during radiograph acquisition has the potential to lead to a misdiagnosis. Asymmetrical hemithoraces are one of the indications of obliquity for which we propose an automatic classification method. Approach: We propose a hemithoraces segmentation method based on Convolutional Neural Networks (CNNs) and active contours. We utilized the U-Net model to segment the ribs and spine and then utilized active contours to find left and right hemithoraces. We then extracted features from the left and right hemithoraces to train an ensemble classifier which includes Support Vector Machine, Gradient Boosting and Multi-Layer Perceptron. Five-fold cross-validation was used, thorax segmentation was evaluated by Intersection over Union (IoU), and symmetry classification was evaluated using Precision, Recall, Area under Curve and F1 score. Results: Classification of symmetry for 900 radiographs reported an F1 score of 82.8% . To test the robustness of the proposed thorax segmentation method to underexposure and overexposure, we synthetically corrupted properly exposed radiographs and evaluated results using IoU. The results showed that the models IoU for underexposure and overexposure dropped by 2.1% and 1.2%, respectively. Conclusions: Our results indicate that the proposed thorax segmentation method is robust to poor exposure radiographs. The proposed thorax segmentation method can be applied to human radiography with minimal changes.

Abstract (translated)

目的: 常用的方法是评估确认或怀疑Thoracic Pathology 的患者。在犬和小狐狸的X射线片中,正确的患者位置比人类更困难,因为患者合作程度不足和身体形状变化。在X射线采集期间不正确的患者位置有可能导致错误的诊断。斜面分割是斜面突出物分类的一个适应症,我们提出了一种自动分类方法来对其进行分类。方法:我们提出了基于卷积神经网络(CNNs)和主动轮廓的斜面分割方法。我们使用U-Net模型分割肋骨和脊柱,然后使用主动轮廓找到左和右斜面。我们然后从左和右斜面提取特征来训练一个集成分类器,其中包括支持向量机、梯度提升和多层感知器。使用五次交叉验证,对 Thoracic Segmentation 进行评估,使用Intersection over Union(IoU) 和对称分类进行评估,使用精度、召回率和曲线下面积和F1 分数进行评估。结果:对900张照片的对称分类报告F1 得分为82.8%。为了测试所提出的 Thoracic Segmentation 方法对不足曝光和过度曝光的鲁棒性,我们合成了正确的曝光照片并使用IoU 进行评估。结果表明,不足曝光和过度曝光模型的IoU分别下降了2.1%和1.2%。结论:我们的结果表明,所提出的 Thoracic Segmentation 方法对较差曝光的X射线片非常鲁棒。所提出的 Thoracic Segmentation 方法可以应用于人类X射线片,几乎没有变化。

URL

https://arxiv.org/abs/2302.12923

PDF

https://arxiv.org/pdf/2302.12923.pdf


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