Automated vertebrae detection (identification and localization) aims to identify vertebrae and locate their centroids in medical images, which is a critical step of spinal computer-aided systems. However, due to unpredictable field-of-view and various pathology cases, the image content is diverse and the vertebral morphology can be abnormal in a variety of ways, which challenges the designed systems. In this paper, we propose an effective sequence-based framework robust to various tough cases for accurate vertebrae identification and localization. Our method consists of three sub-modules: (1) Local Feature Extraction (LFE) module designs a shape-compatible category-balanced sampler to collect patches to train a convolution neural network, which extracts representative local features and generates score maps. (2) Discriminative Sequential Image Description (DSID) module proposes a node screening strategy for reliable vertebral feature sequence construction based on feature maps and score maps. This effectively prevents false positives and false negatives in light-weighted dense prediction schemes and fuses local features into a hierarchical discriminative description of given images. (3) Spinal Pattern Exploitation (SPE) module designs an end-balanced relative position learning scheme to fuse hierarchical local-global information for comprehensively exploiting spinal patterns to overcome the FOV and pathological variation challenges in vertebrae detection. Extensive experiments on a challenging dataset consisting of 450 spinal MRIs show that the identification rate of FSDF reaches 0.974 ±0.025 and the localization error is only 4.742 ±2.928 pixels, which demonstrates the effectiveness of our method with pathological and field-of-view variations and its superiority over other state-of-the-art methods.
Keywords: Automated vertebral identification and localization; Sequence-based framework.
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