Can incomplete silver standard labels improve performance of DTI-based volumetric segmentation of the corpus callosum?

Abstract

Corpus Callosum (CC) segmentation is required when the analysis from this structure is desirable. Many of these studies require the CC segmentation on diffusion tensor images (DTI). However, few methods perform segmentation directly in the DTI. Segmenting on DTI makes it possible to disregard the registration step after segmenting on T1 images. This work studies the possibility of improving automated segmentation of the CC using silver standard annotations. With incomplete silver standard annotations, limited to 5 or 7 central slices, experiments performed throughout this work were done to compare methods of pre-training and fine tuning in an attempt to translate silver standard knowledge to improved performance in 3D CC segmentation. Experiments include 3D and 2D U-Net as deep learning architectures. Results point to central limited silver standard annotations not being useful for improving the performance in gold standard 3D annotations. Our best method involved training a 3D U-Net with gold standards and post processing, achieving a 3D Dice of 83.33 Dice, surpassing 2D U-Net.