A Localisation-Segmentation Approach for Multi-label Annotation of Lumbar Vertebrae using Deep Nets

TL;DR

This paper presents a two-stage deep learning approach for accurate multi-label segmentation and localization of lumbar vertebrae in CT scans, effectively handling healthy and abnormal cases with high accuracy.

Contribution

It introduces a novel combination of global localization and local segmentation stages using deep networks for lumbar vertebrae annotation.

Findings

Achieved over 90% Dice coefficient on challenging datasets.

Successfully segmented vertebrae with severe deformities.

Demonstrated high generalizability to abnormal spine cases.

Abstract

Multi-class segmentation of vertebrae is a non-trivial task mainly due to the high correlation in the appearance of adjacent vertebrae. Hence, such a task calls for the consideration of both global and local context. Based on this motivation, we propose a two-staged approach that, given a computed tomography dataset of the spine, segments the five lumbar vertebrae and simultaneously labels them. The first stage employs a multi-layered perceptron performing non-linear regression for locating the lumbar region using the global context. The second stage, comprised of a fully-convolutional deep network, exploits the local context in the localised lumbar region to segment and label the lumbar vertebrae in one go. Aided with practical data augmentation for training, our approach is highly generalisable, capable of successfully segmenting both healthy and abnormal vertebrae (fractured and scoliotic spines). We consistently achieve an average Dice coefficient of over 90 percent on a publicly available dataset of the xVertSeg segmentation challenge of MICCAI 2016. This is particularly noteworthy because the xVertSeg dataset is beset with severe deformities in the form of vertebral fractures and scoliosis.