用大规模数据预训练提升脑外伤病灶分割精度
Large Scale Supervised Pretraining For Traumatic Brain Injury Segmentation
- 基于多数据集监督预训练，增强模型对脑结构与病理的理解
- 在msTBI数据上微调后，分割性能比基线高2个Dice点
- 适合医学图像分割研究者，尤其关注脑损伤分析的团队
中重度脑外伤（msTBI）病灶分割在神经影像学中面临巨大挑战，因病灶大小、形状和分布差异显著，跨越不同脑区与组织类型。这种异质性使传统图像处理方法难以奏效，导致图像配准与脑区划分出现关键错误。为应对这一难题，AIMS-TBI分割挑战赛2024旨在推动针对T1加权MRI数据的创新分割算法发展。本文提出一种受MultiTalent启发的大规模多数据集监督预训练方案，采用Resenc L网络在涵盖多种解剖与病理结构的综合数据集上进行训练，使模型具备扎实的脑部解剖与病理先验知识。随后在msTBI特定数据上微调，优化其对T1-MRI特征的适应能力，在无预训练基线基础上实现最高达2 Dice点的性能提升。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。