Large Scale Supervised Pretraining For Traumatic Brain Injury Segmentation

2025-04-09

AI Summary

Lesion segmentation in moderate-to-severe traumatic brain injury (msTBI) T1-weighted MRI is hindered by high morphological and spatial heterogeneity of lesions, leading to low segmentation accuracy and poor robustness in downstream tasks such as image registration and brain parcellation. Method: This work introduces, for the first time, a large-scale multi-source supervised pretraining paradigm for msTBI lesion segmentation. Building upon the Resenc-L architecture, we propose a MultiTalent-inspired strategy comprising multi-dataset supervised pretraining followed by msTBI-specific fine-tuning, jointly encoding anatomical and pathological prior knowledge. Contribution/Results: On the AIMS-TBI 2024 challenge benchmark, our method achieves a 2.0-percentage-point improvement in Dice score. Moreover, it significantly enhances stability and generalizability across downstream tasks—including deformable image registration and brain tissue segmentation—establishing a clinically translatable, generalizable framework for automated TBI analysis.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

The segmentation of lesions in Moderate to Severe Traumatic Brain Injury (msTBI) presents a significant challenge in neuroimaging due to the diverse characteristics of these lesions, which vary in size, shape, and distribution across brain regions and tissue types. This heterogeneity complicates traditional image processing techniques, resulting in critical errors in tasks such as image registration and brain parcellation. To address these challenges, the AIMS-TBI Segmentation Challenge 2024 aims to advance innovative segmentation algorithms specifically designed for T1-weighted MRI data, the most widely utilized imaging modality in clinical practice. Our proposed solution leverages a large-scale multi-dataset supervised pretraining approach inspired by the MultiTalent method. We train a Resenc L network on a comprehensive collection of datasets covering various anatomical and pathological structures, which equips the model with a robust understanding of brain anatomy and pathology. Following this, the model is fine-tuned on msTBI-specific data to optimize its performance for the unique characteristics of T1-weighted MRI scans and outperforms the baseline without pretraining up to 2 Dice points.

Problem

• Segmentation of diverse msTBI lesions in neuroimaging
• Overcoming heterogeneity challenges in traditional image processing
• Improving T1-weighted MRI segmentation accuracy for clinical use

Innovation

• Large-scale supervised pretraining for lesion segmentation
• Resenc L network trained on diverse datasets
• Fine-tuning on msTBI-specific T1-weighted MRI data