Computer analysis of computed tomography scans of the lung: a survey

By Ingrid C. Sluimer, Arnold M. R. Schilham, Mathias Prokop, B. van Ginneken. IEEE Transactions on Medical Imaging, 2006

615 citations recorded by the source corpus.

Subfields: Atomic and Molecular Physics, and Optics, Radiation, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/ecad8c357147604fc065e717f54758ad4c10552b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atomic and Molecular Physics, and Optics

1. Ling Shao
2. P. Heng

In Radiation

1. Huan Fu

In Radiology, Nuclear Medicine and Imaging

1. P. Heng
2. Hao Chen
3. Qi-Yan Dou
4. Jing Qin
5. Lequan Yu
6. Ling Shao
7. Jianbing Shen
8. Tao Zhou

Citations in the corpus

• Multilevel Contextual 3-D CNNs for False Positive Reduction in Pulmonary Nodule Detection (2017, background): “portant role in computer-aided lung cancer diagnosis and early treatment [1]–[3].”
• Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images (2020, methodology): “CT imaging is a popular technique for the diagnosis of lung diseases [23], [24].”
• Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network (2016, background): “This inherent property of ILDs, as well as the lack of strict clinical guidelines and the large quantity of radiological data that radiologists have to scrutinize, explains the low diagnostic accuracy and the high inter- and intra- observer variability, which has been reported to be as great as 50% ”
• Shape-Based Computer-Aided Detection of Lung Nodules in Thoracic CT Images (2009, background): “in a review of computer analysis of CT lung scans [11], research leading to improved detection of GGO nodules should have top priority.”