利用融合拉普拉斯方程的深度学习框架改进皮质灰质深部脑沟的分割
图像与视频处理
2023-03-06 v2 计算机视觉与模式识别
摘要
在开发自动化皮质分割工具时，生成拓扑正确的分割结果对于计算几何有效的形态测量指标十分重要。在实践中，准确的皮质分割受到图像伪影以及皮质本身高度折叠解剖结构的挑战。为此，我们提出了一种新颖的基于深度学习的皮质分割方法，该方法在训练过程中将关于皮质几何结构的先验知识融入网络。我们设计了一个损失函数，利用应用于皮质的拉普拉斯方程理论，对紧密折叠脑沟之间未分辨的边界进行局部惩罚。使用人内侧颞叶标本的离体MRI数据集，我们证明所提方法在定量与定性上均优于基线分割网络。
引用
@article{arxiv.2303.00795,
title = {Improved Segmentation of Deep Sulci in Cortical Gray Matter Using a Deep Learning Framework Incorporating Laplace's Equation},
author = {Sadhana Ravikumar and Ranjit Ittyerah and Sydney Lim and Long Xie and Sandhitsu Das and Pulkit Khandelwal and Laura E. M. Wisse and Madigan L. Bedard and John L. Robinson and Terry Schuck and Murray Grossman and John Q. Trojanowski and Edward B. Lee and M. Dylan Tisdall and Karthik Prabhakaran and John A. Detre and David J. Irwin and Winifred Trotman and Gabor Mizsei and Emilio Artacho-Pérula and Maria Mercedes Iñiguez de Onzono Martin and Maria del Mar Arroyo Jiménez and Monica Muñoz and Francisco Javier Molina Romero and Maria del Pilar Marcos Rabal and Sandra Cebada-Sánchez and José Carlos Delgado González and Carlos de la Rosa-Prieto and Marta Córcoles Parada and David A. Wolk and Ricardo Insausti and Paul A. Yushkevich},
journal= {arXiv preprint arXiv:2303.00795},
year = {2023}
}
备注
Accepted at the 28th biennial international conference on Information Processing in Medical Imaging (IPMI 2023)