Abstract
Automatic vertebrae localization and segmentation in computed tomography (CT) are fundamental for spinal image analysis and spine surgery with computer-assisted surgery systems. But they remain challenging due to high variation in spinal anatomy among patients. In this paper, we proposed a deep-learning approach for automatic CT vertebrae localization and segmentation with a two-stage Dense-U-Net. The first stage used a 2D-Dense-U-Net to localize vertebrae by detecting the vertebrae centroids with dense labels and 2D slices. The second stage segmented the specific vertebra within a region-of-interest identified based on the centroid using 3D-Dense-U-Net. Finally, each segmented vertebra was merged into a complete spine and resampled to original resolution. We evaluated our method on the dataset from the CSI 2014 Workshop with 6 metrics: location error (1.69 ± 0.78 mm), detection rate (100%) for vertebrae localization; the dice coefficient (0.953 ± 0.014), intersection over union (0.911 ± 0.025), Hausdorff distance (4.013 ± 2.128 mm), pixel accuracy (0.998 ± 0.001) for vertebrae segmentation. The experimental results demonstrated the efficiency of the proposed method. Furthermore, evaluation on the dataset from the xVertSeg challenge with location error (4.12 ± 2.31), detection rate (100%), dice coefficient (0.877 ± 0.035) shows the generalizability of our method. In summary, our solution localized the vertebrae successfully by detecting the centroids of vertebrae and implemented instance segmentation of vertebrae in the whole spine.
Highlights
The vertebra, which is one of the main components of the spine, plays an important role in supporting the human body’s walk, twist and move
We evaluated the accuracy of vertebrae localization; the second experiments respectively evaluating the accuracy of vertebrae segmentation qualitatively and quantitatively were conducted and the results were compared with some state-of-the-art methods
To further evaluate the generalizability and the performance on pathological cases, we conducted experiments on xVertSeg dataset in terms of evaluation on location error (LE), detection rate (DR) and dice coefficient (DC)
Summary
The vertebra, which is one of the main components of the spine, plays an important role in supporting the human body’s walk, twist and move. Due to the advances of deep learning[7], recent best-performing methods for vertebrae localization are based on convolutional neural networks (CNNs). The early approaches typically are based on traditional image processing methods that could be classified into region growing methods[11], the level set method[12], clustering approaches[13], energy. Published vertebrae segmentation methods have replaced explicit modeling of the vertebral shape and appearance with convolutional neural networks. Kolařík et al[21] validated the superior performance of 3D-Dense-U-Net in medical image segmentation Both Zhou et al.[20] and Kolařík[21] failed to separately segment vertebrae from the adjacent vertebrae in their work
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