Image Processing, Computer-Assisted

"Image Processing, Computer-Assisted" is a descriptor in the National Library of Medicine's controlled vocabulary thesaurus, MeSH (Medical Subject Headings). Descriptors are arranged in a hierarchical structure, which enables searching at various levels of specificity.

MeSH information

Definition | Details | More General Concepts | Related Concepts | More Specific Concepts

A technique of inputting two-dimensional images into a computer and then enhancing or analyzing the imagery into a form that is more useful to the human observer.

Descriptor ID	D007091
MeSH Number(s)	L01.224.308
Concept/Terms	Image Processing, Computer-Assisted Image Processing, Computer-Assisted Image Processing, Computer Assisted Computer-Assisted Image Processing Computer Assisted Image Processing Image Reconstruction Image Reconstruction Image Reconstructions Reconstruction, Image Reconstructions, Image Image Analysis, Computer-Assisted Image Analysis, Computer-Assisted Image Analysis, Computer Assisted Computer-Assisted Image Analysis Computer Assisted Image Analysis Analysis, Computer-Assisted Image Computer-Assisted Image Analyses Image Analyses, Computer-Assisted

Below are MeSH descriptors whose meaning is more general than "Image Processing, Computer-Assisted".

Information Science [L]
Information Science [L01]
Computing Methodologies [L01.224]
Image Processing, Computer-Assisted [L01.224.308]

Below are MeSH descriptors whose meaning is related to "Image Processing, Computer-Assisted".

Below are MeSH descriptors whose meaning is more specific than "Image Processing, Computer-Assisted".

publications

Timeline | Most Recent

This graph shows the total number of publications written about "Image Processing, Computer-Assisted" by people in this website by year, and whether "Image Processing, Computer-Assisted" was a major or minor topic of these publications.

Bar chart showing 524 publications over 31 distinct years, with a maximum of 32 publications in 2011

To see the data from this visualization as text, click here.

Year	Major Topic	Minor Topic	Total
1995	1	5	6
1996	4	6	10
1997	2	3	5
1998	5	9	14
1999	4	8	12
2000	3	8	11
2001	5	6	11
2002	2	11	13
2003	7	13	20
2004	8	18	26
2005	3	22	25
2006	6	19	25
2007	4	20	24
2008	3	18	21
2009	4	15	19
2010	5	20	25
2011	10	22	32
2012	3	13	16
2013	6	22	28
2014	7	18	25
2015	8	13	21
2016	6	10	16
2017	5	8	13
2018	11	18	29
2019	8	6	14
2020	5	6	11
2021	10	10	20
2022	2	4	6
2023	4	7	11
2024	4	9	13
2025	1	1	2

Below are the most recent publications written about "Image Processing, Computer-Assisted" by people in Profiles.

Sprague DY, Rusch K, Dunn RL, Borchardt JM, Ban S, Bubnis G, Chiu GC, Wen C, Suzuki R, Chaudhary S, Lee HJ, Yu Z, Dichter B, Ly R, Onami S, Lu H, Kimura KD, Yemini E, Kato S. Unifying community whole-brain imaging datasets enables robust neuron identification and reveals determinants of neuron position in C.?elegans. Cell Rep Methods. 2025 Jan 27; 5(1):100964.

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Guo M, Wu Y, Hobson CM, Su Y, Qian S, Krueger E, Christensen R, Kroeschell G, Bui J, Chaw M, Zhang L, Liu J, Hou X, Han X, Lu Z, Ma X, Zhovmer A, Combs C, Moyle M, Yemini E, Liu H, Liu Z, Benedetto A, La Riviere P, Col?n-Ramos D, Shroff H. Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy. Nat Commun. 2025 Jan 02; 16(1):313.

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Elferich J, Kong L, Zottig X, Grigorieff N. CTFFIND5 provides improved insight into quality, tilt, and thickness of TEM samples. Elife. 2024 Dec 20; 13.

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Chen Y, Pretorius PH, Yang Y, King MA, Lindsay C. Investigation of scatter energy window width and count levels for deep learning-based attenuation map estimation in cardiac SPECT/CT imaging. Phys Med Biol. 2024 Nov 11; 69(22).

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Zhang X, Yang Y, Pretorius PH, Slomka PJ, King MA. Cardiac motion correction with a deep learning network for perfusion defect assessment in single-photon emission computed tomography myocardial perfusion imaging. J Nucl Cardiol. 2025 Jan; 43:102071.

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Brown EE, Guy AA, Holroyd NA, Sweeney PW, Gourmet L, Coleman H, Walsh C, Markaki AE, Shipley R, Rajendram R, Walker-Samuel S. Physics-informed deep generative learning for quantitative assessment of the retina. Nat Commun. 2024 Aug 10; 15(1):6859.

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Layman RR, Leng S, Boedeker KL, Burk LM, Dang H, Duan X, Jacobsen MC, Li B, Li K, Little K, Madhav P, Miller J, Nute JL, Giraldo JCR, Ruchala KJ, Tao S, Varchena V, Vedantham S, Zeng R, Zhang D. AAPM Task Group Report 299: Quality control in multi-energy computed tomography. Med Phys. 2024 Oct; 51(10):7012-7037.

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Jermain PR, Oswald M, Langdun T, Wright S, Khan A, Stadelmann T, Abdulkadir A, Yaroslavsky AN. Deep?learning-based cell segmentation for rapid optical cytopathology of thyroid cancer. Sci Rep. 2024 07 16; 14(1):16389.

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Tanny S, Dona Lemus OM, Wancura J, Sperling N, Webster M, Jung H, Zhou Y, Li F, Yoon J, Podgorsak A, Zheng D. MU variability in CBCT-guided online adaptive radiation therapy. J Appl Clin Med Phys. 2024 Sep; 25(9):e14440.

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Pells S, Zeraatkar N, Kalluri KS, Moore SC, May M, Furenlid LR, Kupinski MA, Kuo PH, King MA. Correction of multiplexing artefacts in multi-pinhole SPECT through temporal shuttering, de-multiplexing of projections, and alternating reconstruction. Phys Med Biol. 2024 Jun 06; 69(12).

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