Automatic vertebrae recognition is crucial in spine diseases diagnosis, treatment planning, and response assessment. Although vertebrae detection has been studied for years, reliably recognizing vertebrae from arbitrary spine MRI images remains a challenge due to varying image characteristics, field of view (FOV) as well as vertebrae appearance. In this paper, we propose a Hierarchical Self-calibration Detection Framework (Hi-scene) to precisely recognize the labels and bounding boxes of all vertebrae in an arbitrary spine MRI image. Hi-scene is designed to first coarsely localize regions where vertebrae exist without the need of a priori knowledge about the scale, image characteristics and FOV; then accurately recognize vertebrae and automatically correct wrong recognitions by an elaborated self-calibration recognition network that embeds message passing into deep learning network. The method is trained and evaluated on a capacious and challenging dataset of 450 MRI scans, and the evaluation results show that our Hi-scene achieves high performance (testing accuracy reaches 0.933) from arbitrary input spine MRI and outperforms other state-of-the-art methods.
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