Automatic classification of symmetry of hemithoraces in canine and feline radiographs

Peyman Tahghighi; Nicole Norena; Eran Ukwatta; Ryan B. Appleby; Amin Komeili

doi:10.1117/1.JMI.10.4.044004

25 July 2023 Automatic classification of symmetry of hemithoraces in canine and feline radiographs

Peyman Tahghighi, Nicole Norena, Eran Ukwatta, Ryan B. Appleby, Amin Komeili

Author Affiliations +

Journal of Medical Imaging, Vol. 10, Issue 4, 044004 (July 2023). https://doi.org/10.1117/1.JMI.10.4.044004

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 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 include 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 model’s 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.

Citation Download Citation

Peyman Tahghighi, Nicole Norena, Eran Ukwatta, Ryan B. Appleby, and Amin Komeili "Automatic classification of symmetry of hemithoraces in canine and feline radiographs," Journal of Medical Imaging 10(4), 044004 (25 July 2023). https://doi.org/10.1117/1.JMI.10.4.044004

Received: 6 February 2023; Accepted: 11 July 2023; Published: 25 July 2023

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KEYWORDS

Radiography

Image segmentation

Education and training

Spine

Lung

Feature extraction

Contour modeling

Purpose

Approach

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