Employing deep convolutional neural networks for segmenting the medial retropharyngeal lymph nodes in CT studies of dogs.
David SchmidVolkher B ScholzPatrick R KircherInes E LautenschlaegerPublished in: Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association (2022)
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.
Keyphrases
- convolutional neural network
- deep learning
- lymph node
- high resolution
- computed tomography
- healthcare
- artificial intelligence
- dual energy
- end stage renal disease
- contrast enhanced
- image quality
- ejection fraction
- machine learning
- chronic kidney disease
- newly diagnosed
- resistance training
- sentinel lymph node
- gene expression
- magnetic resonance imaging
- neoadjuvant chemotherapy
- mental health
- prognostic factors
- quality improvement
- magnetic resonance
- current status
- body composition
- patient reported
- weight gain
- network analysis
- optic nerve