用集成模型精准分割脑外伤病变，提升MRI分析可靠性。
Traumatic Brain Injury Segmentation using an Ensemble of Encoder-decoder Models
- 基于nnUNet框架融合多种编码器-解码器结构进行初步分割。
- 在AIMS-TBI 2025挑战中总体Dice达0.5973，无病灶图像达0.8514。
- 方法开源，适合医学影像分析与脑损伤研究者参考。
中重度创伤性脑损伤（TBI）病灶的识别与分割在神经影像学中面临巨大挑战，主要因其病灶大小、数量和侧别差异极大，影响后续图像配准与脑区划分，降低分析准确性。本研究旨在开发一种自动分割流程，用于在T1加权MRI中检测并分割TBI病灶。评估了多种方法以实现高精度分割，核心采用nnUNet框架中的多种架构进行初始分割，并结合后处理策略提升评价指标。最终在AIMS-TBI 2025挑战赛中取得0.8451准确率，有病灶图像的Dice分数为0.4711，无病灶图像为0.8514，总体Dice为0.5973，排名前六。该方法的Python实现已公开。
原文摘要 · Abstract (English)
The identification and segmentation of moderate-severe traumatic brain injury (TBI) lesions pose a significant challenge in neuroimaging. This difficulty arises from the extreme heterogeneity of these lesions, which vary in size, number, and laterality, thereby complicating downstream image processing tasks such as image registration and brain parcellation, reducing the analytical accuracy. Thus, developing methods for highly accurate segmentation of TBI lesions is essential for reliable neuroimaging analysis. This study aims to develop an effective automated segmentation pipeline to automatically detect and segment TBI lesions in T1-weighted MRI scans. We evaluate multiple approaches to achieve accurate segmentation of the TBI lesions. The core of our pipeline leverages various architectures within the nnUNet framework for initial segmentation, complemented by post-processing strategies to enhance evaluation metrics. Our final submission to the challenge achieved an accuracy of 0.8451, Dice score values of 0.4711 and 0.8514 for images with and without visible lesions, respectively, with an overall Dice score of 0.5973, ranking among the top-6 methods in the AIMS-TBI 2025 challenge. The Python implementation of our pipeline is publicly available.
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。