Large Scale Supervised Pretraining For Traumatic Brain Injury Segmentation
This work addresses the problem of accurate lesion segmentation for clinical neuroimaging in traumatic brain injury, but it is incremental as it builds on existing pretraining methods.
The paper tackled the challenge of segmenting lesions in Moderate to Severe Traumatic Brain Injury (msTBI) from T1-weighted MRI data, achieving a performance improvement of up to 2 Dice points over a baseline without pretraining.
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.