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Simultaneous Brain Structures Segmentation Combining Shape and Pose Forces

机译：结合形状和姿势力的同时脑结构分割

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摘要

This paper presents a new supervised learning based method for brain structure segmentation. We learn moment-based signatures of structures of interest and formulate the segmentation as a maximum a-posteriori estimation problem employing nonparametric multivariate kernel densities. For this problem, we propose a gradient flow solution. We have compared our method with state-of-the-art methods such as FSL-FIRST and Free-Surfer using volumetric 3T from IBSR. In addition, we have evaluated our algorithm on 7T MR data. We report comparative results of accuracy and significantly improved time-efficiency.

机译：本文提出了一种新的基于监督学习的脑结构分割方法。我们学习感兴趣结构的基于矩的签名，并将分段公式化为采用非参数多元核密度的最大后验估计问题。针对此问题，我们提出了一种梯度流解决方案。我们已经将我们的方法与最先进的方法（例如，使用IBSR的体积3T进行的FSL-FIRST和Free-Surfer）进行了比较。此外，我们已经根据7T MR数据评估了我们的算法。我们报告了准确性的比较结果，并显着提高了时间效率。

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来源
《Multimodal Brain Image Analysis》|2011年|p.143-151|共9页
会议地点 Toronto(CA);Toronto(CA);Toronto(CA);Toronto(CA)
作者
Octavian Soldea; Trung Doan; Andrew Webb; Mark van Buchem; Julien Milles; Radu Jasinschi;
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作者单位

Philips Research, Eindhoven, 5656AE, The Netherlands;

Division of Image Processing, Department of Radiology, Leiden University Medical Center, The Netherlands;

CJ Gorter center for High Field MRI, Department of Radiology, Leiden University Medical Center, The Netherlands;

Department of Radiology, Leiden University Medical Center, The Netherlands;

Division of Image Processing, Department of Radiology, Leiden University Medical Center, The Netherlands;

Philips Research, Eindhoven, 5656AE, The Netherlands;

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原文格式 PDF
正文语种 eng
中图分类医用物理学;脑部疾病;
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专利

1. Simultaneous Multi-Structure Segmentation and 3D Nonrigid Pose Estimation in Image-Guided Robotic Surgery [J] . Nosrati Masoud S., Abugharbieh Rafeef, Peyrat Jean-Marc, Medical Imaging, IEEE Transactions on . 2016,第1期

机译：图像引导机器人手术中的同时多结构分割和3D非刚性姿势估计
2. Coupled Nonparametric Shape and Moment-Based Intershape Pose Priors for Multiple Basal Ganglia Structure Segmentation [J] . Uzunbas M. G., Soldea O., Unay D., Medical Imaging, IEEE Transactions on . 2010,第12期

机译：耦合非参数形状和基于矩的互形姿势先验，用于多个基底神经节结构分割
3. An SPM8-based approach for attenuation correction combining segmentation and nonrigid template formation: Application to simultaneous PET/MR Brain Imaging [J] . Izquierdo-GarciaD., HansenA.E., F?rsterS., The Journal of Nuclear Medicine . 2014,第11期

机译：基于SPM8的衰减校正方法，结合了分段和非刚性模板形成：在同步PET / MR脑成像中的应用
4. Simultaneous Brain Structures Segmentation Combining Shape and Pose Forces [C] . Octavian Soldea, Trung Doan, Andrew Webb, International Workshop on Multimodal Brain Image Analysis . 2011

机译：同时脑结构分割组合形状和姿态力
5. A combined machine-learning and graph-based framework for the 3-D automated segmentation of retinal structures in SD-OCT images. [D] . Antony, Bhavna Josephine. 2013

机译：结合了机器学习和基于图的框架，可对SD-OCT图像中的视网膜结构进行3-D自动分割。
6. Segmentation of Neuronal Structures Using SARSA (λ)-Based Boundary Amendment with Reinforced Gradient-Descent Curve Shape Fitting [O] . Fei Zhu, Quan Liu, Yuchen Fu, -1

机译：使用基于SARSA（λ）的边界修正与增强的梯度下降曲线拟合拟合对神经元结构进行分割
7. Shape Constrained CNN for Cardiac MR Segmentation with Simultaneous Prediction of Shape and Pose Parameters [O] . Sofie Tilborghs, Tom Dresselaers, Piet Claus, 2021

机译：具有同时预测形状和姿势参数的心MR分割的形状约束CNN

Simultaneous Brain Structures Segmentation Combining Shape and Pose Forces

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