Training Samples-Optimized Dictionary Learning for Magnetic Resonance Super-Resolution Imaging
Keywords:
MR super-resolution, dictionary learning, sparse reconstruction, training samples optimizationAbstract
Sensorineural hearing loss is a condition characterized by damage to the inner ear or nerve pathways connecting it to the brain, resulting in hearing impairment. Cochlear implants have been developed as a solution for children with bilateral or unilateral sensorineural hearing loss. However, the success of cochlear implant surgery relies on the presence and functionality of the cochlear nerve. Therefore, accurate segmentation and measurement of the cochlear nerve are crucial for surgeons to predict the outcome of the cochlear implant procedure. In this study, we propose a modified region growing segmentation algorithm that accurately segments the cochlear nerve region. The segmentation accuracy is assessed using various parameters, including Jaccard, Dice, False Positive Dice, and False Negative Dice. Additionally, the segmented region is measured using long diameter, short diameter, and cross-sectional area. Statistical analyses, such as intra/inter-observer correlation and limits of agreement, are conducted on the cross-sectional area of the cochlear nerve to assess the reproducibility of the automated measurement. This automated segmentation and measurement approach holds promise for predicting the outcome of cochlear implantation in individuals with sensorineural hearing loss.
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