Vertebral Compression Fracture detection using Multiple Instance Learning and Majority Voting

Abstract

Vertebral compression fractures (VCF) often miss detection in radiology scans, risking more severe secondary fractures in the future leading to permanent disability and death. Automated solutions are therefore desirable, however a frequent bottleneck in medical image analysis is the availability of radiologist’s time for annotations. To alleviate this problem, this work presents the first attempt at VCF detection using Multiple Instance Learning (MIL), a weakly supervised learning approach that can cope with a small annotated data set. The method involves localisation of the thoracic and lumbar spine regions by generating 6 bounding boxes from which 2D patches are extracted. These patches are then used as instances in a bag within an MIL setting to train a deep learning architecture using an algorithm employing an embedded space paradigm with a shared convolutional neural network (CNN) layer. Majority voting is then performed on the results of the 6 bounding boxes to achieve accuracy / F1 score of 81.05% / 80.74% for thoracic and 85.45 % / 85.61% for lumbar spine respectively.