ArticleVeterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association2022
Employing deep convolutional neural networks for segmenting the medial retropharyngeal lymph nodes in CT studies of dogs.
Article in Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 13 citations in OpenAlex.
- Automated segmentation of canine pulmonary masses in CT imaging using AI.The veterinary quarterly · 2025Article
- Machine-learning prediction of postoperative complications after high tibial osteotomy for canine cranial cruciate ligament disease.Veterinary surgery : VS · 2025Article
- A Novel Machine Learning Model for the Automated Diagnosis of Nasal Pathology in Canine Patients.Animals : an open access journal from MDPI · 2025Article
- Automated AI-based segmentation of canine hepatic focal lesions from CT studies.Frontiers in veterinary science · 2025Article
- Deep-learning-based automatic liver segmentation using computed tomography images in dogs.Frontiers in veterinary science · 2025Article
- Automatic gross tumor segmentation of canine head and neck cancer using deep learning and cross-species transfer learning.Frontiers in veterinary science · 2023Article
- Employing deep convolutional neural networks for segmenting the medial retropharyngeal lymph nodes in CT studies of dogs.Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association · 2022Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
While still in its infancy, the application of deep convolutional neural networks in veterinary diagnostic imaging is a rapidly growing field. The preferred deep learning architecture to be employed is convolutional neural networks, as these provide the structure preferably used for the analysis of medical images. With this retrospective exploratory study, the applicability of such networks for the task of delineating certain organs with respect to their surrounding tissues was tested. More precisely, a deep convolutional neural network was trained to segment medial retropharyngeal lymph nodes in a study dataset consisting of CT scans of canine heads. With a limited dataset of 40 patients, the network in conjunction with image augmentation techniques achieved an intersection-overunion of overall fair performance (median 39%, 25 percentiles at 22%, 75 percentiles at 51%). 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.
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