Deep learning cascade networks for segmentation of fluorine-18 sodium fluoride positron emission tomography scans of equine metacarpo- and metatarsophalangeal joints outperform atlas-based method
Year: 2026
Type: article
Abstract: Objective: To create a labeled dataset and evaluate a convolutional neural network (CNN) for segmentation of fluorine-18 sodium fluoride PET scans of the equine metacarpo- and metatarsophalangeal joint (fetlock), targeting the third metacarpal bone, proximal phalanx, proximal sesamoid bone(s), and soft tissue. Methods: PET and corresponding CT scans were retrospectively selected from June 2024 through November 2025 using convenience sampling and coregistered to a common reference frame. The coregistered PET-CT scans were manually labeled; the labels were then transferred to PET-only images and used to train a cascade of CNNs with and without data augmentation. Segmentation accuracy was quantified using the Dice coefficient and compared to atlas-based segmentation of the PET-CT scans. Results: A total of 84 scans were collected from 2 facilities. For the test set (n = 8), the minimum, mean, and maximum Dice coefficients computed for all anatomical areas together were 0.85, 0.88, and 0.92, respectively, exceeding the values for atlas-based segmentation of 0.69, 0.80, and 0.87. Analysis of the Dice coefficient by area showed the best CNN segmentation for the third metacarpal bone (mean Dice coefficient (Dmean) = 0.92) and the worst for the medial proximal sesamoid bone (Dmean = 0.82); corresponding atlas-based values were 0.86 and 0.77. Conclusions: CNN-based segmentation of fluorine-18 sodium fluoride PET images of the fetlock outperforms an atlas-based method in both speed and accuracy measured by Dice coefficient. This advancement enables new, previously inaccessible strategies for PET image quantification. Clinical Relevance: Accurate and robust segmentation of PET fetlocks enables more accurate analysis and novel insights into lesion characterization.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7129735776