Suppr超能文献

概率性肝脏图谱构建

Probabilistic liver atlas construction.

作者信息

Dura Esther, Domingo Juan, Ayala Guillermo, Marti-Bonmati Luis, Goceri E

机构信息

Department of Informatics, School of Engineering, University of Valencia, Avda. de la Universidad, 46100, Burjasot, Spain.

Department of Statistics and Operations Research, University of Valencia, Avda. Vicent Andrés Estellés, 1, 46100, Burjasot, Spain.

出版信息

Biomed Eng Online. 2017 Jan 13;16(1):15. doi: 10.1186/s12938-016-0305-8.

Abstract

BACKGROUND

Anatomical atlases are 3D volumes or shapes representing an organ or structure of the human body. They contain either the prototypical shape of the object of interest together with other shapes representing its statistical variations (statistical atlas) or a probability map of belonging to the object (probabilistic atlas). Probabilistic atlases are mostly built with simple estimations only involving the data at each spatial location.

RESULTS

A new method for probabilistic atlas construction that uses a generalized linear model is proposed. This method aims to improve the estimation of the probability to be covered by the liver. Furthermore, all methods to build an atlas involve previous coregistration of the sample of shapes available. The influence of the geometrical transformation adopted for registration in the quality of the final atlas has not been sufficiently investigated. The ability of an atlas to adapt to a new case is one of the most important quality criteria that should be taken into account. The presented experiments show that some methods for atlas construction are severely affected by the previous coregistration step.

CONCLUSION

We show the good performance of the new approach. Furthermore, results suggest that extremely flexible registration methods are not always beneficial, since they can reduce the variability of the atlas and hence its ability to give sensible values of probability when used as an aid in segmentation of new cases.

摘要

背景

解剖图谱是表示人体器官或结构的三维体积或形状。它们包含感兴趣对象的原型形状以及表示其统计变化的其他形状(统计图谱)或属于该对象的概率图(概率图谱)。概率图谱大多仅通过涉及每个空间位置数据的简单估计构建。

结果

提出了一种使用广义线性模型构建概率图谱的新方法。该方法旨在改进肝脏覆盖概率的估计。此外,构建图谱的所有方法都涉及对可用形状样本进行预先配准。用于配准的几何变换对最终图谱质量的影响尚未得到充分研究。图谱适应新病例的能力是应考虑的最重要质量标准之一。所展示的实验表明,一些图谱构建方法受到先前配准步骤的严重影响。

结论

我们展示了新方法的良好性能。此外,结果表明,极其灵活的配准方法并不总是有益的,因为它们可能会降低图谱的可变性,从而降低其在辅助新病例分割时给出合理概率值的能力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd0f/5237330/c0d223261a3d/12938_2016_305_Fig1_HTML.jpg

相似文献

1
Probabilistic liver atlas construction.
Biomed Eng Online. 2017 Jan 13;16(1):15. doi: 10.1186/s12938-016-0305-8.
2
Probabilistic atlas and geometric variability estimation to drive tissue segmentation.
Stat Med. 2014 Sep 10;33(20):3576-99. doi: 10.1002/sim.6156. Epub 2014 Apr 2.
5
Multi-atlas segmentation of the whole hippocampus and subfields using multiple automatically generated templates.
Neuroimage. 2014 Nov 1;101:494-512. doi: 10.1016/j.neuroimage.2014.04.054. Epub 2014 Apr 29.
6
Segmentation of liver and spleen based on computational anatomy models.
Comput Biol Med. 2015 Dec 1;67:146-60. doi: 10.1016/j.compbiomed.2015.10.007. Epub 2015 Oct 28.
7
Femur statistical atlas construction based on two-level 3D non-rigid registration.
Comput Aided Surg. 2009;14(4-6):83-99. doi: 10.3109/10929080903246543.
8
Unbiased diffeomorphic atlas construction for computational anatomy.
Neuroimage. 2004;23 Suppl 1:S151-60. doi: 10.1016/j.neuroimage.2004.07.068.
9
Unsupervised segmentation, clustering, and groupwise registration of heterogeneous populations of brain MR images.
IEEE Trans Med Imaging. 2014 Feb;33(2):201-24. doi: 10.1109/TMI.2013.2270114. Epub 2013 Jun 19.
10
Segmentation of 4D cardiac MR images using a probabilistic atlas and the EM algorithm.
Med Image Anal. 2004 Sep;8(3):255-65. doi: 10.1016/j.media.2004.06.005.

引用本文的文献

1
Findings from machine learning in clinical medical imaging applications - Lessons for translation to the forensic setting.
Forensic Sci Int. 2020 Nov;316:110538. doi: 10.1016/j.forsciint.2020.110538. Epub 2020 Oct 18.
2
Template Creation for High-Resolution Computed Tomography Scans of the Lung in R Software.
Acad Radiol. 2020 Aug;27(8):e204-e215. doi: 10.1016/j.acra.2019.10.030. Epub 2019 Dec 13.

本文引用的文献

1
Statistical shape analysis of the human spleen geometry for probabilistic occupant models.
J Biomech. 2016 Jun 14;49(9):1540-1546. doi: 10.1016/j.jbiomech.2016.03.027. Epub 2016 Mar 23.
3
Shape-intensity prior level set combining probabilistic atlas and probability map constrains for automatic liver segmentation from abdominal CT images.
Int J Comput Assist Radiol Surg. 2016 May;11(5):817-26. doi: 10.1007/s11548-015-1332-9. Epub 2015 Dec 8.
4
Automatic localization of the anterior commissure, posterior commissure, and midsagittal plane in MRI scans using regression forests.
IEEE J Biomed Health Inform. 2015 Jul;19(4):1362-74. doi: 10.1109/JBHI.2015.2428672. Epub 2015 Apr 30.
6
Quantitative evaluation of atlas-based high-density diffuse optical tomography for imaging of the human visual cortex.
Biomed Opt Express. 2014 Oct 13;5(11):3882-900. doi: 10.1364/BOE.5.003882. eCollection 2014 Nov 1.
7
A statistical geometrical description of the human liver for probabilistic occupant models.
J Biomech. 2014 Nov 28;47(15):3681-8. doi: 10.1016/j.jbiomech.2014.09.031. Epub 2014 Oct 2.
10
Automated abdominal multi-organ segmentation with subject-specific atlas generation.
IEEE Trans Med Imaging. 2013 Sep;32(9):1723-30. doi: 10.1109/TMI.2013.2265805. Epub 2013 Jun 3.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验