Robust 3D reconstruction of rib cage bones in computed tomography images, by combining knowledge from radiological, machine learning, and innovative graph guidance (Englisch)

In: Research on Biomedical Engineering   ;  35 ,  1  ;  45-56  ;  2019

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Abstract

Purpose

More than 70 million computed tomography scans are made per year. A great number of them aim at the thoraxic region, due to the number of organs and structures within it. The 3D visualization of these structures, including the bone, can lead to a more precise medical diagnosis. There are a number of works regarding 3D bone reconstruction, but most fail to present a quantitative evaluation of their assessment or have not achieved an assessment close to 100%. We present an automatic method of bone segmentation followed by 3D reconstruction that approaches these current limitations.

Methods

The proposed methodology has three blocks: (1) Preprocessing, whereby a median filter was applied to images that presented a high level of noise; (2) feature extraction procedure, in which (i) the images intensity levels were converted to attenuation coefficients and (ii) a (MLP) neural network was used to populate the Space of Attributes with the corresponding feature vectors; and (3) 3D structural construction, whereby a red-and-black tree with graph guidance combined the regarding clustered feature vectors with their spatial neighbors. To evaluate the results, the accuracy between the 2D-segmented images and their corresponding gold standards was calculated.

Results

The material is composed of a set of 90 CT chest volumes. We obtained high accuracy parameters, such as Overlap Dice (%) = 98.77 ± 0.58 and False Negative (%) = 0.13 ± 0.026.

Conclusion

The proposed methodology presented significant accuracy values and processing time, having a large database to test the methodology, besides being a fully automatic method of 3D bone reconstruction.

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