论文标题
映射：型号的平均型号，并进行中风病变的后处理
MAPPING: Model Average with Post-processing for Stroke Lesion Segmentation
论文作者
论文摘要
准确的中风病变分割在中风康复研究中起关键作用，以提供病变形状和大小信息，可用于定量中风程度并评估治疗效果。最近，使用深度学习技术的自动分割算法已经开发并取得了令人鼓舞的结果。在本报告中，我们基于NNU-NET框架介绍了中风病变细分模型，并将其应用于中风后（Atlas V2.0）数据集后病变的解剖图。此外，我们描述了一种有效的后处理策略，可以改善某些细分指标。我们的方法在2022 MICCAI ATLAS挑战中排名第一，平均骰子得分为0.6667，病变的F1得分为0.5643，简单病变计数分数为4.5367，体积差分别为8804.9102。我们的代码和训练有素的模型权重可以在https://github.com/king-haw/atlas-r2-docker-submission上公开获得。
Accurate stroke lesion segmentation plays a pivotal role in stroke rehabilitation research, to provide lesion shape and size information which can be used for quantification of the extent of the stroke and to assess treatment efficacy. Recently, automatic segmentation algorithms using deep learning techniques have been developed and achieved promising results. In this report, we present our stroke lesion segmentation model based on nnU-Net framework, and apply it to the Anatomical Tracings of Lesions After Stroke (ATLAS v2.0) dataset. Furthermore, we describe an effective post-processing strategy that can improve some segmentation metrics. Our method took the first place in the 2022 MICCAI ATLAS Challenge with an average Dice score of 0.6667, Lesion-wise F1 score of 0.5643, Simple Lesion Count score of 4.5367, and Volume Difference score of 8804.9102. Our code and trained model weights are publicly available at https://github.com/King-HAW/ATLAS-R2-Docker-Submission.
