Automatic vertebrae recognition from arbitrary spine MRI images by a category-Consistent self-calibration detection framework

Accurate vertebrae recognition is crucial in spinal disease localization and successive treatment planning. Although vertebrae detection has been studied for years, reliably recognizing vertebrae from arbitrary spine MRI images remains a challenge. The similar appearance of different vertebrae and the pathological deformations of the same vertebrae makes it difficult for classification in images with different fields of view (FOV). In this paper, we propose a Category-consistent Self-calibration Recognition System (Can-See) to accurately classify the labels and precisely predict the bounding boxes of all vertebrae with improved discriminative capabilities for vertebrae categories and self-awareness of false positive detections. Can-See is designed as a two-step detection framework: (1) A hierarchical proposal network (HPN) to perceive the existence of the vertebrae. HPN leverages the correspondence between hierarchical features and multi-scale anchors to detect objects. This correspondence tackles the image scale/resolution challenge. (2) A Category-consistent Self-calibration Recognition (CSRN) Network to classify each vertebra and refine their bounding boxes. CSRN leverages the dictionary learning principle to preserve the most representative features; it imposes a novel category-consistent constraint to force vertebrae with the same label to have similar features. CSRN then innovatively formulates message passing into the deep learning framework, which leverages the label compatibility principle to self-calibrate the wrong pre-recognitions. Can-See is trained and evaluated on a capacious and challenging dataset of 450 MRI scans. The results show that Can-See achieves high performance (testing accuracy reaches 0.955) and outperforms other state-of-the-art methods.