Segment and Slice: A Two-Step Deep Learning Pipeline for Opportunistic Vertebral Fracture Detection in Computed Tomography

Abstract

An end-to-end method is introduced to build a combined segmentation-classification pipeline using deep learning for opportunistic fracture detection in CT spine images of varying field-of-views. This retrospective study builds on 452 CTs of the lumbar/thoracolumbar spine. Patients were included based on the evidence of ≥1 vertebral body fracture and excluded in case of history of spinal surgery or pathologic fractures. The collective was split into training/validation (405) and test (47) sets. An open-source pre-segmented spine dataset was used to train a preliminary segmentation model, which was applied on the training set. The resulting segmentation was post-processed to remove posterior vertebral structures and if needed manually refined by a radiologist. Using the refined version as new training data, a final segmentation nnU-net was trained. Sagittal slices from each vertebra were labelled individually with regard to fracture evidence. Slices without signs of fracture were used as negative class. 27,019 slices (20,396 negative, 6,623 positive) trained a classification algorithm using resnet18. Two senior readers independently assessed fractures in the test set to obtain a consensual ground truth. The segmentation-classification pipeline was applied to the test set and compared to the ground truth. The segmentation model correctly segmented 330/339 (97%) vertebrae. Considering every segmented vertebra, the classifier detected fractures with 88% sensitivity, 95% specificity and 93% accuracy. Our two-step method can help to detect spine fractures on images of varying field-ofviews, with an accuracy comparable to that of a radiologist in-training. The final models as well as our code material are available at https://github.com/usbradiology/VertebraeFx.