Automating Whole Brain Histology to MRI Registration: Implementation of a Computational Pipeline
This work addresses the challenge of aligning histological and MRI data for neuroanatomical studies, which is crucial for researchers in neuroscience and medical imaging, though it is incremental as it builds on existing methods with specific improvements.
The authors tackled the problem of registering distorted whole-brain histological images to MRI by developing a semi-automatic computational pipeline, achieving successful registration with minimal user interaction in two whole-brain datasets from the Brain Bank of the Brazilian Aging Brain Study Group.
Although the latest advances in MRI technology have allowed the acquisition of higher resolution images, reliable delineation of cytoarchitectural or subcortical nuclei boundaries is not possible. As a result, histological images are still required to identify the exact limits of neuroanatomical structures. However, histological processing is associated with tissue distortion and fixation artifacts, which prevent a direct comparison between the two modalities. Our group has previously proposed a histological procedure based on celloidin embedding that reduces the amount of artifacts and yields high quality whole brain histological slices. Celloidin embedded tissue, nevertheless, still bears distortions that must be corrected. We propose a computational pipeline designed to semi-automatically process the celloidin embedded histology and register them to their MRI counterparts. In this paper we report the accuracy of our pipeline in two whole brain volumes from the Brain Bank of the Brazilian Aging Brain Study Group (BBBABSG). Results were assessed by comparison of manual segmentations from two experts in both MRIs and the registered histological volumes. The two whole brain histology/MRI datasets were successfully registered using minimal user interaction. We also point to possible improvements based on recent implementations that could be added to this pipeline, potentially allowing for higher precision and further performance gains.