Abstract
Accurate and robust segmentation of anatomical structures from magnetic resonance images is valuable in many computer-aided clinical tasks. Traditional codec networks are not satisfactory because of their low accuracy of edge segmentation, the low recognition rate of the target, and loss of detailed information. To address these problems, this study proposes a series of improved models for semantic segmentation and progressively optimizes them from the three aspects of convolution module, codec unit, and feature fusion. Instead of the standard convolution structure, we apply a new type of convolution module for the feature extraction. The networks integrate a multi-path method to obtain richer-detail edge information. Finally, a dense network is utilized to strengthen the ability of the feature fusion and integrate more different-level information. The evaluation of the Accuracy, Dice coefficient, and Jaccard index led to values of 0.9855, 0.9185, and 0.8507, respectively. These metrics of the best network increased by 1.0%, 4.0%, and 6.1%, respectively. Boundary F1-Score reached 0.9124 indicating that the proposed networks can segment smaller targets to obtain smoother edges. Our methods obtain more key information than traditional methods and achieve superiority in segmentation performance.
Highlights
With the continuous development of society, people have become increasingly busy, and various pressures of daily life and diseases have been discovered [1]
This study proposes a series of improved models for semantic segmentation and progressively optimizes them from the three aspects of convolution module, codec unit, and feature fusion
Compared with the traditional codec structure network, the improved models were optimized in three aspects: convolution module, codec unit, and feature fusion
Summary
With the continuous development of society, people have become increasingly busy, and various pressures of daily life and diseases (e.g., low back pain) have been discovered [1]. With the development of computer and digital information technologies, people have increasingly focused on the acquisition and analysis of medical images. To improve the feasibility of diagnosis and treatment before clinical diagnosis or spinal surgery, a doctor can prioritize clinical analysis based on the patient’s medical image and efficiently obtain more accurate clinical information from the segmented spinal image [3]. Magnetic resonance imaging (MRI) is known as the most sensitive non-invasive medical image technique with an outstanding effect on the spinal structure [4]. With the application of computer-aided diagnosis in the field of clinical diagnosis, doctors and scholars have increased
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