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CT 自动量化气胸

Automated quantification of pneumothorax in CT.

机构信息

Department of Radiology, Massachusetts General Hospital, 25 New Chardon Street, Suite 400B, Boston, MA 02114, USA.

出版信息

Comput Math Methods Med. 2012;2012:736320. doi: 10.1155/2012/736320. Epub 2012 Oct 3.

DOI:10.1155/2012/736320
PMID:23082091
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3469107/
Abstract

An automated, computer-aided diagnosis (CAD) algorithm for the quantification of pneumothoraces from Multidetector Computed Tomography (MDCT) images has been developed. Algorithm performance was evaluated through comparison to manual segmentation by expert radiologists. A combination of two-dimensional and three-dimensional processing techniques was incorporated to reduce required processing time by two-thirds (as compared to similar techniques). Volumetric measurements on relative pneumothorax size were obtained and the overall performance of the automated method shows an average error of just below 1%.

摘要

已经开发出一种用于从多排螺旋 CT（MDCT）图像定量气胸的自动化、计算机辅助诊断（CAD）算法。通过与专家放射科医生的手动分割进行比较来评估算法性能。结合了二维和三维处理技术，将所需的处理时间减少了三分之二（与类似技术相比）。获得了相对气胸大小的体积测量值，并且自动方法的整体性能显示平均误差仅略低于 1%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e38/3469107/c63c7589a471/CMMM2012-736320.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e38/3469107/20d92e3f6a5d/CMMM2012-736320.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e38/3469107/6b5bc86a273a/CMMM2012-736320.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e38/3469107/ed84dbfa7a5c/CMMM2012-736320.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e38/3469107/f5063cb95027/CMMM2012-736320.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e38/3469107/c63c7589a471/CMMM2012-736320.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e38/3469107/20d92e3f6a5d/CMMM2012-736320.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e38/3469107/6b5bc86a273a/CMMM2012-736320.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e38/3469107/ed84dbfa7a5c/CMMM2012-736320.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e38/3469107/f5063cb95027/CMMM2012-736320.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e38/3469107/c63c7589a471/CMMM2012-736320.005.jpg

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本文引用的文献

1
Treatment of primary spontaneous pneumothorax.原发性自发性气胸的治疗
Curr Opin Pulm Med. 2009 Jul;15(4):376-9. doi: 10.1097/MCP.0b013e32832ae314.
2
MDCT for automated detection and measurement of pneumothoraces in trauma patients.多层螺旋CT用于创伤患者气胸的自动检测与测量。
AJR Am J Roentgenol. 2009 Mar;192(3):830-6. doi: 10.2214/AJR.08.1339.
3
Pneumothorax.气胸
随着机器学习和数据科学的进步，人工智能（AI）的工程应用与临床应用：放射学引领未来之路。
Ther Adv Urol. 2021 Sep 20;13:17562872211044880. doi: 10.1177/17562872211044880. eCollection 2021 Jan-Dec.
4
Deep learning detection and quantification of pneumothorax in heterogeneous routine chest computed tomography.深度学习在异质常规胸部 CT 中检测和量化气胸。
Eur Radiol Exp. 2020 Apr 17;4(1):26. doi: 10.1186/s41747-020-00152-7.
5
Anatomical locations of air for rapid diagnosis of pneumothorax in blunt trauma patients.在钝性创伤患者中快速诊断气胸的空气解剖位置。
World J Emerg Surg. 2019 Sep 2;14:44. doi: 10.1186/s13017-019-0263-0. eCollection 2019.
6
Current Applications and Future Impact of Machine Learning in Radiology.机器学习在放射学中的当前应用和未来影响。
Radiology. 2018 Aug;288(2):318-328. doi: 10.1148/radiol.2018171820. Epub 2018 Jun 26.
7
Computerized Diagnostic Assistant for the Automatic Detection of Pneumothorax on Ultrasound: A Pilot Study.用于超声自动检测气胸的计算机诊断助手：一项初步研究。
West J Emerg Med. 2016 Mar;17(2):209-15. doi: 10.5811/westjem.2016.1.28087. Epub 2016 Mar 2.
8
Evaluation of three pneumothorax size quantification methods on digitized chest X-ray films using medical-grade grayscale and consumer-grade color displays.使用医学级灰度显示器和消费级彩色显示器对数字化胸部X光片上的三种气胸大小量化方法进行评估。
J Digit Imaging. 2014 Apr;27(2):280-6. doi: 10.1007/s10278-013-9651-2.
Respiration. 2008;76(2):121-7. doi: 10.1159/000135932. Epub 2008 Jun 26.
4
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Respirology. 2004 Jun;9(2):157-64. doi: 10.1111/j.1440-1843.2004.00577.x.
5
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6
BTS guidelines for the management of spontaneous pneumothorax.英国胸科学会自发性气胸管理指南。
Thorax. 2003 May;58 Suppl 2(Suppl 2):ii39-52. doi: 10.1136/thorax.58.suppl_2.ii39.
7
Traumatic pneumothorax detection with thoracic US: correlation with chest radiography and CT--initial experience.超声检查对创伤性气胸的诊断：与胸部X线和CT的相关性——初步经验
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8
A population-based study on pneumothorax in severely traumatized patients.一项针对严重创伤患者气胸的基于人群的研究。
J Trauma. 2001 Oct;51(4):677-82. doi: 10.1097/00005373-200110000-00009.
9
Management of spontaneous pneumothorax: an American College of Chest Physicians Delphi consensus statement.自发性气胸的管理：美国胸科医师学会德尔菲共识声明
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10
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Crit Care Med. 2000 May;28(5):1370-5. doi: 10.1097/00003246-200005000-00018.