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- AI can segment medial retropharyngeal LNs
Study: This study is an original investigation that explores the use of deep convolutional neural networks for segmenting the medial retropharyngeal lymph nodes in CT studies of dogs.
Methods: A retrospective exploratory study was conducted to test the applicability of deep convolutional neural networks for delineating certain organs with respect to their surrounding tissues. A deep convolutional neural network was trained to segment medial retropharyngeal lymph nodes in a study dataset consisting of CT scans of canine heads. The network achieved an intersection-overunion of overall fair performance with a limited dataset of 40 patients.
Results: The results indicate that these architectures can indeed be trained to segment anatomic structures in anatomically complicated and breed-related variating areas such as the head, possibly even using just small training sets. As these conditions are quite common in veterinary medical imaging, all routines were published as an open-source Python package with the hope of simplifying future research projects in the community.
Conclusion: The conclusion is that deep convolutional neural networks can be trained to segment anatomic structures in anatomically complicated and breed-related variating areas such as the head, possibly even using just small training sets. This has potential implications for future research projects in veterinary medical imaging.
Transverse contrast-enhanced CT image of a 9-year-old Shetland Sheepdog acquired in sternal recumbency (window width = 450, level = 140, 120 kVp, 280 mA) (A) at the level of the paracondylar processes showing the medial retropharyngeal lymph nodes (white arrowheads). Input image for U-Net (B) and corresponding output image (C). The pixels identified by U-Net as medial retropharyngeal lymph nodes are highlighted in image (C). Left in the images is right in the dog
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