DDParcel: Deep Learning Anatomical Brain Parcellation From Diffusion MRI

Fine-scale striatal parcellation using diffusion MRI tractography and graph neural networks

The striatum, a crucial part of the basal ganglia, plays a key role in various brain functions through its interactions with the cortex. The complex structural and functional diversity across subdivisions within the striatum highlights the necessity for precise striatal segmentation. In this study, we introduce a novel deep clustering pipeline for automated, fine-scale parcellation of the striatum using diffusion MRI (dMRI) tractography. Initially, we employ a voxel-based probabilistic fiber tractography algorithm combined with a fiber-tract embedding technique to capture intricate dMRI connectivity patterns. To maintain critical inter-voxel relationships, our approach employs Graph Neural Networks (GNNs) to create accurate graph representations of the striatum. This involves encoding probabilistic fiber bundle characteristics as node attributes and refining edge weights using activation functions to enhance the graph's interpretability and accuracy. The methodology incorporates a Transformer-based GraphConv autoencoder in the pre-training phase to extract critical spatial features while minimizing reconstruction loss. In the fine-tuning phase, a novel joint loss mechanism markedly improves segmentation precision and anatomical fidelity. Integration of traditional clustering techniques with multi-head self-attention mechanisms further elevates the accuracy and robustness of our segmentation approach. This methodology provides new insights into the striatum's role in cognition and behavior and offers potential clinical applications for neurological disorders.

Towards an informed choice of diffusion MRI image contrasts for cerebellar segmentation

The fine-grained segmentation of cerebellar structures is an essential step towards supplying increasingly accurate anatomically informed analyses, including, for example, white matter diffusion magnetic resonance imaging (MRI) tractography. Cerebellar tissue segmentation is typically performed on structural magnetic resonance imaging data, such as T1-weighted data, while connectivity between segmented regions is mapped using diffusion MRI tractography data. Small deviations in structural to diffusion MRI data co-registration may negatively impact connectivity analyses. Reliable segmentation of brain tissue performed directly on diffusion MRI data helps to circumvent such inaccuracies. Diffusion MRI enables the computation of many image contrasts, including a variety of tissue microstructure maps. While multiple methods have been proposed for the segmentation of cerebellar structures using diffusion MRI, little attention has been paid to the systematic evaluation of the performance of different available input image contrasts for the segmentation task. In this work, we evaluate and compare the segmentation performance of diffusion MRI-derived contrasts on the cerebellar segmentation task. Specifically, we include spherical mean (diffusion-weighted image average) and b0 (non-diffusion-weighted image average) contrasts, local signal parameterization contrasts (diffusion tensor and kurtosis fit maps), and the structural T1-weighted MRI contrast that is most commonly employed for the task. We train a popular deep-learning architecture using a publicly available dataset (HCP-YA), leveraging cerebellar region labels from the atlas-based SUIT cerebellar segmentation pipeline. By training and testing using many diffusion-MRI-derived image inputs, we find that the spherical mean image computed from b=1000 s/mm2 shell data provides stable performance across different metrics and significantly outperforms the tissue microstructure contrasts that are traditionally used in machine learning segmentation methods for diffusion MRI.

DDEvENet: Evidence-based ensemble learning for uncertainty-aware brain parcellation using diffusion MRI

In this study, we developed an Evidential Ensemble Neural Network based on Deep learning and Diffusion MRI, namely DDEvENet, for anatomical brain parcellation. The key innovation of DDEvENet is the design of an evidential deep learning framework to quantify predictive uncertainty at each voxel during a single inference. To do so, we design an evidence-based ensemble learning framework for uncertainty-aware parcellation to leverage the multiple dMRI parameters derived from diffusion MRI. Using DDEvENet, we obtained accurate parcellation and uncertainty estimates across different datasets from healthy and clinical populations and with different imaging acquisitions. The overall network includes five parallel subnetworks, where each is dedicated to learning the FreeSurfer parcellation for a certain diffusion MRI parameter. An evidence-based ensemble methodology is then proposed to fuse the individual outputs. We perform experimental evaluations on large-scale datasets from multiple imaging sources, including high-quality diffusion MRI data from healthy adults and clinically diffusion MRI data from participants with various brain diseases (schizophrenia, bipolar disorder, attention-deficit/hyperactivity disorder, Parkinson's disease, cerebral small vessel disease, and neurosurgical patients with brain tumors). Compared to several state-of-the-art methods, our experimental results demonstrate highly improved parcellation accuracy across the multiple testing datasets despite the differences in dMRI acquisition protocols and health conditions. Furthermore, thanks to the uncertainty estimation, our DDEvENet approach demonstrates a good ability to detect abnormal brain regions in patients with lesions that are consistent with expert-drawn results, enhancing the interpretability and reliability of the segmentation results.

Diffusion MRI with Machine Learning

Diffusion-weighted magnetic resonance imaging (dMRI) of the brain offers unique capabilities including noninvasive probing of tissue microstructure and structural connectivity. It is widely used for clinical assessment of disease and injury, and for neuroscience research. Analyzing the dMRI data to extract useful information for medical and scientific purposes can be challenging. The dMRI measurements may suffer from strong noise and artifacts, and may exhibit high inter-session and inter-scanner variability in the data, as well as inter-subject heterogeneity in brain structure. Moreover, the relationship between measurements and the phenomena of interest can be highly complex. Recent years have witnessed increasing use of machine learning methods for dMRI analysis. This manuscript aims to assess these efforts, with a focus on methods that have addressed data preprocessing and harmonization, microstructure mapping, tractography, and white matter tract analysis. We study the main findings, strengths, and weaknesses of the existing methods and suggest topics for future research. We find that machine learning may be exceptionally suited to tackle some of the difficult tasks in dMRI analysis. However, for this to happen, several shortcomings of existing methods and critical unresolved issues need to be addressed. There is a pressing need to improve evaluation practices, to increase the availability of rich training datasets and validation benchmarks, as well as model generalizability, reliability, and explainability concerns.

Assessment of the Depiction of Superficial White Matter Using Ultra-High-Resolution Diffusion MRI

The superficial white matter (SWM) consists of numerous short-range association fibers connecting adjacent and nearby gyri and plays an important role in brain function, development, aging, and various neurological disorders. Diffusion MRI (dMRI) tractography is an advanced imaging technique that enables in vivo mapping of the SWM. However, detailed imaging of the small, highly-curved fibers of the SWM is a challenge for current clinical and research dMRI acquisitions. This work investigates the efficacy of mapping the SWM using in vivo ultra-high-resolution dMRI data. We compare the SWM mapping performance from two dMRI acquisitions: a high-resolution 0.76-mm isotropic acquisition using the generalized slice-dithered enhanced resolution (gSlider) protocol and a lower resolution 1.25-mm isotropic acquisition obtained from the Human Connectome Project Young Adult (HCP-YA) database. Our results demonstrate significant differences in the cortico-cortical anatomical connectivity that is depicted by these two acquisitions. We perform a detailed assessment of the anatomical plausibility of these results with respect to the nonhuman primate (macaque) tract-tracing literature. We find that the high-resolution gSlider dataset is more successful at depicting a large number of true positive anatomical connections in the SWM. An additional cortical coverage analysis demonstrates significantly higher cortical coverage in the gSlider dataset for SWM streamlines under 40 mm in length. Overall, we conclude that the spatial resolution of the dMRI data is one important factor that can significantly affect the mapping of SWM. Considering the relatively long acquisition time, the application of dMRI tractography for SWM mapping in future work should consider the balance of data acquisition efforts and the efficacy of SWM depiction.

A diffusion MRI tractography atlas for concurrent white matter mapping across Eastern and Western populations

The study of brain differences across Eastern and Western populations provides vital insights for understanding potential cultural and genetic influences on cognition and mental health. Diffusion MRI (dMRI) tractography is an important tool in assessing white matter (WM) connectivity and brain tissue microstructure across different populations. However, a comprehensive investigation into WM fiber tracts between Eastern and Western populations is challenged due to the lack of a cross-population WM atlas and the large site-specific variability of dMRI data. This study presents a dMRI tractography atlas, namely the East-West WM Atlas, for concurrent WM mapping between Eastern and Western populations and creates a large, harmonized dMRI dataset (n=306) based on the Human Connectome Project and the Chinese Human Connectome Project. The curated WM atlas, as well as subject-specific data including the harmonized dMRI data, the whole brain tractography data, and parcellated WM fiber tracts and their diffusion measures, are publicly released. This resource is a valuable addition to facilitating the exploration of brain commonalities and differences across diverse cultural backgrounds.

Boosting knowledge diversity, accuracy, and stability via tri-enhanced distillation for domain continual medical image segmentation

Domain continual medical image segmentation plays a crucial role in clinical settings. This approach enables segmentation models to continually learn from a sequential data stream across multiple domains. However, it faces the challenge of catastrophic forgetting. Existing methods based on knowledge distillation show potential to address this challenge via a three-stage process: distillation, transfer, and fusion. Yet, each stage presents its unique issues that, collectively, amplify the problem of catastrophic forgetting. To address these issues at each stage, we propose a tri-enhanced distillation framework. (1) Stochastic Knowledge Augmentation reduces redundancy in knowledge, thereby increasing both the diversity and volume of knowledge derived from the old network. (2) Adaptive Knowledge Transfer selectively captures critical information from the old knowledge, facilitating a more accurate knowledge transfer. (3) Global Uncertainty-Guided Fusion introduces a global uncertainty view of the dataset to fuse the old and new knowledge with reduced bias, promoting a more stable knowledge fusion. Our experimental results not only validate the feasibility of our approach, but also demonstrate its superior performance compared to state-of-the-art methods. We suggest that our innovative tri-enhanced distillation framework may establish a robust benchmark for domain continual medical image segmentation.