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Purpose: Thoracic radiographs are commonly used to evaluate patients withconfirmed or suspected thoracic pathology. Proper patient positioning is morechallenging in canine and feline radiography than in humans due to less patientcooperation and body shape variation. Improper patient positioning duringradiograph acquisition has the potential to lead to a misdiagnosis.Asymmetrical hemithoraces are one of the indications of obliquity for which wepropose an automatic classification method.  Approach: We propose a hemithoraces segmentation method based onConvolutional Neural Networks (CNNs) and active contours. We utilized the U-Netmodel to segment the ribs and spine and then utilized active contours to findleft and right hemithoraces. We then extracted features from the left and righthemithoraces to train an ensemble classifier which includes Support VectorMachine, Gradient Boosting and Multi-Layer Perceptron. Five-foldcross-validation was used, thorax segmentation was evaluated by Intersectionover 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 scoreof 82.8% . To test the robustness of the proposed thorax segmentation method tounderexposure and overexposure, we synthetically corrupted properly exposedradiographs and evaluated results using IoU. The results showed that the modelsIoU for underexposure and overexposure dropped by 2.1% and 1.2%, respectively.  Conclusions: Our results indicate that the proposed thorax segmentationmethod is robust to poor exposure radiographs. The proposed thorax segmentationmethod can be applied to human radiography with minimal changes.

Automatic Classification of Symmetry of Hemithoraces in Canine and Feline Radiographs