  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.
