PMID- 22971117
OWN - NLM
STAT- MEDLINE
DCOM- 20130628
LR  - 20211021
IS  - 1471-2105 (Electronic)
IS  - 1471-2105 (Linking)
VI  - 13
DP  - 2012 Sep 12
TI  - Ranked retrieval of segmented nuclei for objective assessment of cancer gene 
      repositioning.
PG  - 232
LID - 10.1186/1471-2105-13-232 [doi]
AB  - BACKGROUND: Correct segmentation is critical to many applications within 
      automated microscopy image analysis. Despite the availability of advanced 
      segmentation algorithms, variations in cell morphology, sample preparation, and 
      acquisition settings often lead to segmentation errors. This manuscript 
      introduces a ranked-retrieval approach using logistic regression to automate 
      selection of accurately segmented nuclei from a set of candidate segmentations. 
      The methodology is validated on an application of spatial gene repositioning in 
      breast cancer cell nuclei. Gene repositioning is analyzed in patient tissue 
      sections by labeling sequences with fluorescence in situ hybridization (FISH), 
      followed by measurement of the relative position of each gene from the nuclear 
      center to the nuclear periphery. This technique requires hundreds of 
      well-segmented nuclei per sample to achieve statistical significance. Although 
      the tissue samples in this study contain a surplus of available nuclei, automatic 
      identification of the well-segmented subset remains a challenging task. RESULTS: 
      Logistic regression was applied to features extracted from candidate segmented 
      nuclei, including nuclear shape, texture, context, and gene copy number, in order 
      to rank objects according to the likelihood of being an accurately segmented 
      nucleus. The method was demonstrated on a tissue microarray dataset of 43 breast 
      cancer patients, comprising approximately 40,000 imaged nuclei in which the HES5 
      and FRA2 genes were labeled with FISH probes. Three trained reviewers 
      independently classified nuclei into three classes of segmentation accuracy. In 
      man vs. machine studies, the automated method outperformed the inter-observer 
      agreement between reviewers, as measured by area under the receiver operating 
      characteristic (ROC) curve. Robustness of gene position measurements to boundary 
      inaccuracies was demonstrated by comparing 1086 manually and automatically 
      segmented nuclei. Pearson correlation coefficients between the gene position 
      measurements were above 0.9 (p < 0.05). A preliminary experiment was conducted to 
      validate the ranked retrieval in a test to detect cancer. Independent manual 
      measurement of gene positions agreed with automatic results in 21 out of 26 
      statistical comparisons against a pooled normal (benign) gene position 
      distribution. CONCLUSIONS: Accurate segmentation is necessary to automate 
      quantitative image analysis for applications such as gene repositioning. However, 
      due to heterogeneity within images and across different applications, no 
      segmentation algorithm provides a satisfactory solution. Automated assessment of 
      segmentations by ranked retrieval is capable of reducing or even eliminating the 
      need to select segmented objects by hand and represents a significant improvement 
      over binary classification. The method can be extended to other high-throughput 
      applications requiring accurate detection of cells or nuclei across a range of 
      biomedical applications.
FAU - Cukierski, William J
AU  - Cukierski WJ
AD  - Rutgers University, New Brunswick, NJ 08903, USA. wcuk@eden.rutgers.edu
FAU - Nandy, Kaustav
AU  - Nandy K
FAU - Gudla, Prabhakar
AU  - Gudla P
FAU - Meaburn, Karen J
AU  - Meaburn KJ
FAU - Misteli, Tom
AU  - Misteli T
FAU - Foran, David J
AU  - Foran DJ
FAU - Lockett, Stephen J
AU  - Lockett SJ
LA  - eng
GR  - HHSN261200800001E/CA/NCI NIH HHS/United States
GR  - P30 CA072720/CA/NCI NIH HHS/United States
GR  - R01 LM009239/LM/NLM NIH HHS/United States
GR  - 1R01CA161375-01A1/CA/NCI NIH HHS/United States
GR  - 1R01LM011119-0/LM/NLM NIH HHS/United States
GR  - R01 LM011119/LM/NLM NIH HHS/United States
GR  - 5R01CA156386-06/CA/NCI NIH HHS/United States
GR  - R01 CA156386/CA/NCI NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, U.S. Gov't, Non-P.H.S.
PT  - Validation Study
DEP - 20120912
PL  - England
TA  - BMC Bioinformatics
JT  - BMC bioinformatics
JID - 100965194
SB  - IM
MH  - Algorithms
MH  - Breast Neoplasms/genetics/ultrastructure
MH  - Cell Nucleus/*genetics/ultrastructure
MH  - Female
MH  - *Genes, Neoplasm
MH  - Humans
MH  - *Image Processing, Computer-Assisted
MH  - In Situ Hybridization, Fluorescence
MH  - Logistic Models
MH  - ROC Curve
PMC - PMC3484015
EDAT- 2012/09/14 06:00
MHDA- 2013/07/03 06:00
PMCR- 2012/09/12
CRDT- 2012/09/14 06:00
PHST- 2012/04/02 00:00 [received]
PHST- 2012/08/28 00:00 [accepted]
PHST- 2012/09/14 06:00 [entrez]
PHST- 2012/09/14 06:00 [pubmed]
PHST- 2013/07/03 06:00 [medline]
PHST- 2012/09/12 00:00 [pmc-release]
AID - 1471-2105-13-232 [pii]
AID - 10.1186/1471-2105-13-232 [doi]
PST - epublish
SO  - BMC Bioinformatics. 2012 Sep 12;13:232. doi: 10.1186/1471-2105-13-232.