 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.
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
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| MeSH Number(s) |
L01.224.308
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| Concept/Terms |
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
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Below are MeSH descriptors whose meaning is more general than "Image Processing, Computer-Assisted".
Below are MeSH descriptors whose meaning is more specific than "Image Processing, Computer-Assisted".
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.
To see the data from this visualization as text, click here.
| Year | Major Topic | Minor Topic | Total |
|---|
| 1992 | 2 | 2 | 4 | | 1993 | 0 | 1 | 1 | | 1994 | 1 | 3 | 4 | | 1995 | 2 | 1 | 3 | | 1996 | 3 | 5 | 8 | | 1997 | 6 | 6 | 12 | | 1998 | 1 | 7 | 8 | | 1999 | 1 | 4 | 5 | | 2000 | 3 | 7 | 10 | | 2001 | 0 | 5 | 5 | | 2002 | 5 | 9 | 14 | | 2003 | 5 | 7 | 12 | | 2004 | 3 | 12 | 15 | | 2005 | 7 | 9 | 16 | | 2006 | 3 | 9 | 12 | | 2007 | 7 | 13 | 20 | | 2008 | 4 | 12 | 16 | | 2009 | 6 | 9 | 15 | | 2010 | 5 | 8 | 13 | | 2011 | 3 | 7 | 10 | | 2012 | 6 | 13 | 19 | | 2013 | 5 | 12 | 17 | | 2014 | 6 | 14 | 20 | | 2015 | 8 | 13 | 21 | | 2016 | 8 | 13 | 21 | | 2017 | 5 | 22 | 27 | | 2018 | 9 | 7 | 16 | | 2019 | 11 | 5 | 16 | | 2020 | 6 | 4 | 10 | | 2021 | 6 | 5 | 11 | | 2022 | 2 | 0 | 2 |
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Below are the most recent publications written about "Image Processing, Computer-Assisted" by people in Profiles.
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Antonelli M, Reinke A, Bakas S, Farahani K, Kopp-Schneider A, Landman BA, Litjens G, Menze B, Ronneberger O, Summers RM, van Ginneken B, Bilello M, Bilic P, Christ PF, Do RKG, Gollub MJ, Heckers SH, Huisman H, Jarnagin WR, McHugo MK, Napel S, Pernicka JSG, Rhode K, Tobon-Gomez C, Vorontsov E, Meakin JA, Ourselin S, Wiesenfarth M, Arbeláez P, Bae B, Chen S, Daza L, Feng J, He B, Isensee F, Ji Y, Jia F, Kim I, Maier-Hein K, Merhof D, Pai A, Park B, Perslev M, Rezaiifar R, Rippel O, Sarasua I, Shen W, Son J, Wachinger C, Wang L, Wang Y, Xia Y, Xu D, Xu Z, Zheng Y, Simpson AL, Maier-Hein L, Cardoso MJ. The Medical Segmentation Decathlon. Nat Commun. 2022 Jul 15; 13(1):4128.
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van 't Hof SR, Van Oudenhove L, Janssen E, Klein S, Reddan MC, Kragel PA, Stark R, Wager TD. The Brain Activation-Based Sexual Image Classifier (BASIC): A Sensitive and Specific fMRI Activity Pattern for Sexual Image Processing. Cereb Cortex. 2022 Jul 12; 32(14):3014-3030.
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Hong JS, Hermann I, Zöllner FG, Schad LR, Wang SJ, Lee WK, Chen YL, Chang Y, Wu YT. Acceleration of Magnetic Resonance Fingerprinting Reconstruction Using Denoising and Self-Attention Pyramidal Convolutional Neural Network. Sensors (Basel). 2022 Feb 07; 22(3).
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Peng J, Kim DD, Patel JB, Zeng X, Huang J, Chang K, Xun X, Zhang C, Sollee J, Wu J, Dalal DJ, Feng X, Zhou H, Zhu C, Zou B, Jin K, Wen PY, Boxerman JL, Warren KE, Poussaint TY, States LJ, Kalpathy-Cramer J, Yang L, Huang RY, Bai HX. Deep learning-based automatic tumor burden assessment of pediatric high-grade gliomas, medulloblastomas, and other leptomeningeal seeding tumors. Neuro Oncol. 2022 02 01; 24(2):289-299.
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Calderon CP, Ripple DC, Srinivasan C, Ma Y, Carrier MJ, Randolph TW, O'Connor TF. Testing Precision Limits of Neural Network-Based Quality Control Metrics in High-Throughput Digital Microscopy. Pharm Res. 2022 Feb; 39(2):263-279.
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Liu Z, Zhao H, Fang X, Huo D. Abdominal computed tomography localizer image generation: A deep learning approach. Comput Methods Programs Biomed. 2022 Feb; 214:106575.
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Xing F, Cornish TC, Bennett TD, Ghosh D. Bidirectional Mapping-Based Domain Adaptation for Nucleus Detection in Cross-Modality Microscopy Images. IEEE Trans Med Imaging. 2021 10; 40(10):2880-2896.
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Böhland M, Tharun L, Scherr T, Mikut R, Hagenmeyer V, Thompson LDR, Perner S, Reischl M. Machine learning methods for automated classification of tumors with papillary thyroid carcinoma-like nuclei: A quantitative analysis. PLoS One. 2021; 16(9):e0257635.
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Weber KA, Abbott R, Bojilov V, Smith AC, Wasielewski M, Hastie TJ, Parrish TB, Mackey S, Elliott JM. Multi-muscle deep learning segmentation to automate the quantification of muscle fat infiltration in cervical spine conditions. Sci Rep. 2021 08 16; 11(1):16567.
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Plassard AJ, Bao S, McHugo M, Beason-Held L, Blackford JU, Heckers S, Landman BA. Automated, open-source segmentation of the Hippocampus and amygdala with the open Vanderbilt archive of the temporal lobe. Magn Reson Imaging. 2021 09; 81:17-23.
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