Automated cell analysis in 2D and 3D: A comparative study

Meyer, Michaël; Fauver, Mark; Rahn, John; Neumann, Thomas; Patten, Florence W.; Seibel, Eric J.; Nelson, Aimee J.

doi:10.1016/j.patcog.2008.06.018

Cited by 35 publications

(37 citation statements)

References 7 publications

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“…In our previous work, the level set algorithm has proven its capability in bistatic, monostatic, and limited view configurations in the microwave frequency range [7][8][9][10]. However, advances in micro-fabrication and micro-fluidics have allowed the development of systems that provide recordings every 0.72 • in order to achieve several hundred measurements in the bistatic configuration at 510 THz [5]. Although the level set algorithm capability was proved using experimental data in the microwave frequency range [20,21], the experimental validation of the results obtained in this work will require a coherent radiation system that can measure the amplitude and the phase at high frequencies.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…For lower values of SNR, obtaining meaningful reconstruction results of any target using almost any method will be challenging. In the future, the level set algorithm will be examined using experimental data in the high frequency, once made available from systems similar to those reported in [5,6].…”

Section: Resultsmentioning

confidence: 99%

“…Advances in micro-fabrication and micro-fluidics had facilitated the development of systems to allow 3D tomography of a single cell [5,6]. In the cell tomography system developed by Meyera et al, cell samples were rotated from 0 • to 360 • in steps of 0.72 • in order to achieve 500 projections [5]. A three dimensional filtered back-projection algorithm was implemented to reconstruct the shape of cancerous cells [6].…”

Section: Introductionmentioning

confidence: 99%

“…As reported in [12], the BWA are most effectively delivered as aerosols in air and they represent a serious threat because they are invisible and tasteless. However, investigating bacteria immersed in other media is also important for validation with controlled laboratory measurements similar to those conducted in [5,6]. The later will be the subject of future work.…”

Section: Introductionmentioning

confidence: 99%

“…1 shows a synthetic system to collect the scattering fields at different incident and receiving directions (angles) and at multiple high frequencies, conceptually similar to the single cell system in [5,6]. The single cell system reported in [5] demonstrated that the 3D shape reconstruction increased the accuracy of cancer cells detection three fold, compared with the 2D shape reconstruction. Therefore, the current 3D shape reconstruction of bacteria has a high potential for the detection and classification of these bacteria.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Inverse Scattering Shape Reconstruction of 3d Bacteria Using the Level Set Algorithm

Hassan¹,

Hajihashemi²,

El-Shenawee³

2012

PIER B

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Abstract-Bacteria exist in a variety of groups of shapes, sizes, and single or multiple cell formations. In this paper, the level set shape reconstruction technique, the method of moments, and the marching cubes methods are integrated in the high frequency band for imaging three dimensional bacteria. The time step and the resolution of the marching cubes method are investigated to smooth the error function of the level set and hence speed up the convergence at high frequencies. The numerical results demonstrate the robustness of the level set algorithm for the detection of bacteria based on their shapes. The three dimensional shape reconstructions of unknown bacteria can be utilized to classify biological warfare agents.

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Section: Numerical Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Inverse Scattering Shape Reconstruction of 3d Bacteria Using the Level Set Algorithm

Hassan¹,

Hajihashemi²,

El-Shenawee³

2012

PIER B

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Premalignant and malignant cells in sputum from lung cancer patients

Neumann

Meyer²,

Patten³

et al. 2009

Cancer Cytopathology

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BACKGROUND:The objective of this study was to assess the frequency of premalignant and malignant cells in sputum from patients with lung cancer and to measure the dependence of these cells on cancer stage, histologic type, tumor size, and tumor location.METHODS:This analysis included 444 patients with lung cancer. First, all patients were asked to produce sputum spontaneously; then, they underwent sputum induction. Slide preparations of the sputa were screened for the presence of abnormal cells.RESULTS:Of all patients with lung cancer who had produced adequate specimens, 74.6% had sputum that was positive for premalignant or worse cells, whereas 48.7% had sputum that was positive for malignant cells alone. Surprisingly, the presence of premalignant or worse cells in sputum depended only moderately on disease stage (82.9% of stage IV cancers vs 65.9% of stage I cancers), tumor size (78.6% of tumors >2 cm vs 64.7% of tumors ≤2 cm), and location (83.3% of central lesions vs 68% of peripheral lesions) and was found to be independent of histologic tumor type (78.4% of squamous cell carcinomas vs 71.5% of adenocarcinomas, 74.5% of small cell carcinomas, and 75% of large cell carcinomas).CONCLUSIONS:The findings of the current study suggested the important potential of sputum cytology for lung cancer detection and risk assessment across all stages, histologic types, tumor sizes, and locations. However, the high sensitivities in this study were achieved with a level of scrutiny not feasible in the laboratory routine. The diagnostic potential of sputum cytology may be exploited better through the standardization and automation of sputum preparation and analysis. Cancer (Cancer Cytopathol) 2009. © 2009 American Cancer Society.

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The Cell‐CT 3‐dimensional cell imaging technology platform enables the detection of lung cancer using the noninvasive LuCED sputum test

Meyer¹,

Hayenga²,

Neumann

et al. 2015

Cancer Cytopathology

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The war against cancer has yielded important advances in the early diagnosis and treatment of certain cancer types, but the poor detection rate and 5-year survival rate for lung cancer remains little changed over the past 40 years. Early detection through emerging lung cancer screening programs promises the most reliable means of improving mortality. Sputum cytology has been tried without success because sputum contains few malignant cells that are difficult for cytologists to detect. However, research has shown that sputum contains diagnostic malignant cells and could serve as a means of lung cancer detection if those cells could be detected and correctly characterized. Recently, the National Lung Cancer Screening Trial reported that screening by three consecutive low-dose X-ray CT scans provides a 20% reduction in lung cancer mortality compared to chest X-ray. This reduction in mortality, however, comes with an unacceptable false positive rate that increases patient risks and the overall cost of lung cancer screening. This article reviews the LuCED® test for detecting early lung cancer. LuCED is based on patient sputum that is enriched for bronchial epithelial cells. The enriched sample is then processed on the Cell-CT®, which images cells in three dimensions with sub-micron resolution. Algorithms are applied to the 3D cell images to extract morphometric features that drive a classifier to identify cells that have abnormal characteristics. The final status of these candidate abnormal cells is established by the pathologist's manual review. LuCED promotes accurate cell classification which could enable cost effective detection of lung cancer.

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Automated cell analysis in 2D and 3D: A comparative study

Cited by 35 publications

References 7 publications

Inverse Scattering Shape Reconstruction of 3d Bacteria Using the Level Set Algorithm

Inverse Scattering Shape Reconstruction of 3d Bacteria Using the Level Set Algorithm

Premalignant and malignant cells in sputum from lung cancer patients

The Cell‐CT 3‐dimensional cell imaging technology platform enables the detection of lung cancer using the noninvasive LuCED sputum test

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