Multi-Center CNN-Based Spine Segmentation from T2W MRI Using Small Amounts of Data

Segmentation of the spinal tissues on MRI is the basis for quantitative analyses, but time-consuming if done manually. In this work, we construct a pipeline for automatic vertebrae segmentation from T2w MRI scans, assessing performance and generalizability by external validation. Our study used 15 scans from one site (Haukeland University Hospital, HUH) and 10 scans from another (Sahlgrenska University Hospital, SUH). MRI experts manually delineated the vertebral bodies Th12-L5 on all the HUH data and a subset of six scans from SUH. We trained multiple convolutional neural networks, assessing the performance in an experimental design tailored to small-data contexts and also on external data. Our best model achieved a mean Dice score of 0.899. This is comparable to results in the literature, but our system required much less training data.
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