Fully Automatic Intervertebral Disc Segmentation Using Multimodal 3D U-Net

TL;DR

This paper introduces a 3D U-Net based deep learning framework for automatic segmentation of intervertebral discs from multimodal MRI images, achieving high accuracy and robustness in spine analysis.

Contribution

The study presents a novel 3D U-Net approach with localization and cropping strategies for improved IVD segmentation from multimodal MRI data.

Findings

Achieved a mean Dice coefficient of 89.0% in segmentation.

Compared 2D and 3D U-Net models with and without data augmentation.

Demonstrated effectiveness across various multimodal MRI combinations.

Abstract

Intervertebral discs (IVDs), as small joints lying between adjacent vertebrae, have played an important role in pressure buffering and tissue protection. The fully-automatic localization and segmentation of IVDs have been discussed in the literature for many years since they are crucial to spine disease diagnosis and provide quantitative parameters in the treatment. Traditionally hand-crafted features are derived based on image intensities and shape priors to localize and segment IVDs. With the advance of deep learning, various neural network models have gained great success in image analysis including the recognition of intervertebral discs. Particularly, U-Net stands out among other approaches due to its outstanding performance on biomedical images with a relatively small set of training data. This paper proposes a novel convolutional framework based on 3D U-Net to segment IVDs from multi-modality MRI images. We first localize the centers of intervertebral discs in each spine sample and then train the network based on the cropped small volumes centered at the localized intervertebral discs. A detailed comprehensive analysis of the results using various combinations of multi-modalities is presented. Furthermore, experiments conducted on 2D and 3D U-Nets with augmented and non-augmented datasets are demonstrated and compared in terms of Dice coefficient and Hausdorff distance. Our method has proved to be effective with a mean segmentation Dice coefficient of 89.0% and a standard deviation of 1.4%.