An optimized segmentation convolutional neural network with dynamic energy loss function for 3D reconstruction of...

3D reconstruction for lumbar spine based on segmentation of Magnetic Resonance (MR) images is meaningful for diagnosis of degenerative lumbar spine diseases. However, spine MR images with unbalanced pixel distribution often cause the segmentation performance of Convolutional Neural Network (CNN) reduced. Designing a composite loss function for CNN is an effective way to enhance the segmentation capacity, yet composition loss values with fixed weight may still cause underfitting in CNN training. In this study, we designed a composite loss function with a dynamic weight, called Dynamic Energy Loss, for spine MR images segmentation. In our loss function, the weight percentage of different loss values could be dynamically adjusted during training, thus CNN could fast converge in earlier training stage and focus on detail learning in the later stage. Two datasets were used in control experiments, and the U-net CNN model with our proposed loss function achieved superior performance with Dice similarity coefficient values of 0.9484 and 0.8284 respectively, which were also verified by the Pearson correlation, Bland-Altman, and intra-class correlation coefficient analysis. Furthermore, to improve the 3D reconstruction based on the segmentation results, we proposed a filling algorithm to generate contextually related slices by computing the pixel-level difference between adjacent slices of segmented images, which could enhance the structural information of tissues between slices, and improve the performance of 3D lumbar spine model rendering. Our methods could help radiologists to build a 3D lumbar spine graphical model accurately for diagnosis while reducing burden of manual image reading.