A novel evolutionary method for spine detection in ultrasound samples of spina bifida cases. (January 2021) - Cengizler, Caglar; Kerem Ün, M.; Buyukkurt, Selim
Highlights: A linear combination of three geometric factors is utilized in the fitness function which is effective for selecting the best individuals. Applied innovative energy minimization stage increases the search space exploration ability of the algorithm. Presented approach distinguishes the spinal bones from others even if severe morphological defects exist on the sonograms. Abstract: Background and objectives: Spina bifida is a fetal spine defect observed during pregnancy. The defect is caused by unfinished closure of the embryonic neural column. Common diagnosis of the defect is still based on manual examination which aims to detect any deformation on spinal axis. This study proposes a novel evolutionary method for locating spinal axis on sonograms of spina bifida pathology. Methods: The method involves a meta-heuristic evolutionary approach, where the sonogram is automatically divided into columns and bone regions belonging to the spine are classified. Accordingly, a specific genetic algorithm is utilized which constructs a set of candidate spine axes. Fitness of the candidate axes is measured by a proposed problem-specific fitness function. A combination of conventional genetic operators and a novel energy minimization approach is applied to each population in order to explore the problem search space. Results: Results show that presented algorithm is generally able to distinguish the spinal bones from others even in the presence of severe morphological defects. Conclusion: It is observed that the presented approach is promising and in most samples the spines identified by the proposed algorithm closely match those drawn by the experts. A computer assisted ultrasound diagnosis system specialized for spina bifida cases does not exist yet, but an algorithm to identify the spine, such as the one presented in this work, is the first natural step towards a diagnosis system. In the future, we intend to improve the algorithm by improving the segmentation stage and further optimizing the various stages of the genetic algorithm.