Automatic generation of diffusion tensor imaging for the lumbar nerve using convolutional neural networks

【Purpose】

Diffusion Tensor Imaging (DTI) with tractography is useful for the functional diagnosis of degenerative lumbar disorders. However, it is not widely used in clinical settings due to time and health care provider costs, as it is performed manually on hospital workstations. The purpose of this study is to construct a system that extracts the lumbar nerve and generates tractography automatically using deep learning semantic segmentation.

【Methods】

We acquired 839 axial diffusion weighted images (DWI) from the DTI data of 90 patients with degenerative lumbar disorders, and segmented the lumbar nerve roots using U-Net, a semantic segmentation model. Using five architectural models, the accuracy of the lumbar nerve root segmentation was evaluated using a Dice coefficient. We also created automatic scripts from three commercially available software tools, including MRICronGL for medical image viewing, Diffusion Toolkit for reconstruction of the DWI data, and Trackvis for the creation of the tractography, and compared the time required to create the tractography, and evaluated the quality of the automated tractography was evaluated.

【Results】

Among the five models, the architectural model Resnet34 performed the best with a Dice = 0.780. The creation time for the automatic lumbar nerve tractography was 191 s, which was significantly shorter by 235 s than the manual time of 426 s (p < 0.05). Furthermore, the agreement between manual and automated tractography was 3.67 ± 1.53 (satisfactory).

【Conclusions】

Using deep learning semantic segmentation, we were able to construct a system that automatically extracted the lumbar nerve and generated lumbar nerve tractography. This technology makes it possible to analyze lumbar nerve DTI and create tractography automatically, and is expected to advance the clinical applications of DTI for the assessment of the lumbar nerve.