Comprehensive segmentation of deep grey nuclei from structural MRI data

Motivation: Lack of tools for comprehensive and complete segmentation of deep grey nuclei using a single software for reproducibility and repeatability Goal(s): A fast accurate and robust method for segmentation of deep grey nuclei (thalamic nuclei, basal ganglia, claustrum, red nucleus) from structural T1 MRI data at conventional field strengths Approach: We leverage the improved contrast of white-matter-nulled imaging by using the recently proposed Histogram-based Polynomial Synthesis (HIPS) to synthesize WMn-like images from standard T1 and then use a multi-atlas segmentation with joint label fusion to segment deep grey nuclei. Results: The method worked robustly on all field strengths (1.5/3/7) and Dice coefficients of 0.7 or more were achieved for all structures compared against manual segmentation ground truth. Impact: This method facilitates careful investigation of the role of deep grey nuclei by enabling the use of conventional T1 data from large public databases, which has not been possible, hitherto, due to lack of robust reproducible segmentation tools.

MetRoBERTa: Leveraging Traditional Customer Relationship Management Data to Develop a Transit-Topic-Aware Language Model

Transit riders' feedback provided in ridership surveys, customer relationship management (CRM) channels, and in more recent times, through social media is key for transit agencies to better gauge the efficacy of their services and initiatives. Getting a holistic understanding of riders' experience through the feedback shared in those instruments is often challenging, mostly due to the open-ended, unstructured nature of text feedback. In this paper, we propose leveraging traditional transit CRM feedback to develop and deploy a transit-topic-aware large language model (LLM) capable of classifying open-ended text feedback to relevant transit-specific topics. First, we utilize semi-supervised learning to engineer a training dataset of 11 broad transit topics detected in a corpus of 6 years of customer feedback provided to the Washington Metropolitan Area Transit Authority (WMATA). We then use this dataset to train and thoroughly evaluate a language model based on the RoBERTa architecture. We compare our LLM, MetRoBERTa, to classical machine learning approaches utilizing keyword-based and lexicon representations. Our model outperforms those methods across all evaluation metrics, providing an average topic classification accuracy of 90%. Finally, we provide a value proposition of this work demonstrating how the language model, alongside additional text processing tools, can be applied to add structure to open-ended text sources of feedback like Twitter. The framework and results we present provide a pathway for an automated, generalizable approach for ingesting, visualizing, and reporting transit riders' feedback at scale, enabling agencies to better understand and improve customer experience.