Evaluation of a Deep Active Learning Model for the Segmentation of Canine Thoracic Radiographs

Abstract

With increasing interest in artificial intelligence (AI) for veterinary medical imaging, there will be an increasing need for the segmentation of medical images. Image segmentation-the process of delineating anatomical structures in medical images-is a critical step for enabling analysis and decision support in veterinary radiology. Manual segmentation of medical images is a time-consuming and tedious task associated with user variation. Many segmentation tasks require a radiologist's expertise. To date, there have been limited evaluations of segmentation methods in veterinary medicine. It is unknown whether novice evaluators can segment radiographs with similar accuracy to experts. The present study aimed to evaluate the performance of an AI segmentation tool in enhancing the accuracy and reducing the time of canine radiograph segmentation of novice, intermediate, and expert users when using an internally developed software that allows both AI-assisted semiautomated and manual segmentation. The AI model was trained using 50 thoracic radiographs from patients referred to the Ontario Veterinary College between January 2020 and July 2021. The intersection over union scores (IoU) for the abdomen, heart, and spinous process labels were higher when all cohorts used the semiautomated method (0.98, 0.98, and >0.74, respectively) versus the manual method (>0.93, >0.94, and >0.42, respectively). The Hausdorff distance for the structure labels was significantly lower when the participants used the semiautomated method than the manual method (p < .0001). The intraobserver intraclass correlation coefficients (ICC) for the semiautomatic and manual methods were 0.81 and 0.36, respectively. In conclusion, the semiautomated tool effectively assisted users with segmenting canine thoracic radiographs.

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