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A Comparison of Lung Nodule Segmentation Algorithms: Methods and Results from a Multi-institutional Study

Overview of attention for article published in Journal of Digital Imaging, February 2016
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Title
A Comparison of Lung Nodule Segmentation Algorithms: Methods and Results from a Multi-institutional Study
Published in
Journal of Digital Imaging, February 2016
DOI 10.1007/s10278-016-9859-z
Pubmed ID
Authors

Jayashree Kalpathy-Cramer, Binsheng Zhao, Dmitry Goldgof, Yuhua Gu, Xingwei Wang, Hao Yang, Yongqiang Tan, Robert Gillies, Sandy Napel

Abstract

Tumor volume estimation, as well as accurate and reproducible borders segmentation in medical images, are important in the diagnosis, staging, and assessment of response to cancer therapy. The goal of this study was to demonstrate the feasibility of a multi-institutional effort to assess the repeatability and reproducibility of nodule borders and volume estimate bias of computerized segmentation algorithms in CT images of lung cancer, and to provide results from such a study. The dataset used for this evaluation consisted of 52 tumors in 41 CT volumes (40 patient datasets and 1 dataset containing scans of 12 phantom nodules of known volume) from five collections available in The Cancer Imaging Archive. Three academic institutions developing lung nodule segmentation algorithms submitted results for three repeat runs for each of the nodules. We compared the performance of lung nodule segmentation algorithms by assessing several measurements of spatial overlap and volume measurement. Nodule sizes varied from 29 μl to 66 ml and demonstrated a diversity of shapes. Agreement in spatial overlap of segmentations was significantly higher for multiple runs of the same algorithm than between segmentations generated by different algorithms (p < 0.05) and was significantly higher on the phantom dataset compared to the other datasets (p < 0.05). Algorithms differed significantly in the bias of the measured volumes of the phantom nodules (p < 0.05) underscoring the need for assessing performance on clinical data in addition to phantoms. Algorithms that most accurately estimated nodule volumes were not the most repeatable, emphasizing the need to evaluate both their accuracy and precision. There were considerable differences between algorithms, especially in a subset of heterogeneous nodules, underscoring the recommendation that the same software be used at all time points in longitudinal studies.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 60 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Israel 1 2%
United States 1 2%
Cuba 1 2%
Unknown 57 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 30%
Student > Master 10 17%
Student > Bachelor 8 13%
Unspecified 6 10%
Researcher 4 7%
Other 14 23%
Readers by discipline Count As %
Computer Science 18 30%
Engineering 15 25%
Unspecified 12 20%
Medicine and Dentistry 8 13%
Physics and Astronomy 3 5%
Other 4 7%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 October 2019.
All research outputs
#8,341,532
of 13,865,625 outputs
Outputs from Journal of Digital Imaging
#452
of 688 outputs
Outputs of similar age
#162,750
of 340,068 outputs
Outputs of similar age from Journal of Digital Imaging
#4
of 5 outputs
Altmetric has tracked 13,865,625 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 688 research outputs from this source. They receive a mean Attention Score of 4.5. This one is in the 32nd percentile – i.e., 32% of its peers scored the same or lower than it.
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