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Abstract #3104

Deep Learning for Veterinary MRI: Automated Detection of Intervertebral Disc Herniation in Pet Dogs

Guoxiong Deng1,2,3, Shoujin Huang1, Ziran Chen1, Lifeng Mei1, Jianzhong Li3, Ruixiang Jiang3, WenYue Xiao3, Dexing Wei3, Yan Kang1,2, and Mengye Lyu1,2
1College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China, 2College of Applied Sciences, Shenzhen University, Shenzhen, China, 3Shenzhen GoldenStone Medical Technology Co. , Ltd, Shenzhen, China


The automated detection of Intervertebral disc (IVD) herniation in animal MRI may facilitate veterinary diagnosis, yet it is rarely studied due to the lack of training data and the challenges from inter-breed variations. Here, we constructed a dog spinal cord MRI dataset with bounding box annotations of herniated discs, and conducted experiments using a number of well-known deep learning models. We demonstrated that automated detection of animal IVD herniation was feasible and in general two-stage detection models such as Faster R-CNN outperformed one-stage models.

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