Deep Vision Network Based CT Image Detection for Aiding Lumbar Herniated Disc Diagnosis

Abstract

Recently, artificial intelligence (AI) technologies have applied in the field of clinical medicine widely. And some of researches try to use AI to assist the diagnosis of spinal disease. In this study, HerniationDet: an automatic lumbar disc herniation detection method based on two stage detection framework (e.g. R-CNN, Fast R-CNN, Faster R-CNN, etc.) is presented. Firstly, after comparing the performance of various backbone networks such as VGG, ResNet, EfficientNet, etc., a feature extractor based on VGG16 is constructed to automatically and efficiently extract the necessary feature information from medical images. Secondly, we use region proposal network (RPN) to generate region proposals and provide them to the part of Fast R-CNN for classification and regression. After precisely studying the image of disc herniation, we adjust the scale and radio of the anchor, to make them more in line with the characteristics of the lumbar disc image dataset. Finally, the object detection algorithm is first used on CT images which achieved 89.50% mAP, and then applied to MR images of the lumbar disc to achieve the goal of automatically identifying lumbar disc herniation with or without calcification. Hence, artificial intelligence assisted diagnosis of calcified lumbar disc herniation on MR images can be achieved with 81.24% mAP, by further using a multi-modal learning strategy.