TY - JOUR
T1 - Multireference level set for the characterization of nuclear morphology in glioblastoma multiforme
AU - Chang, Hang
AU - Han, Ju
AU - Spellman, Paul T.
AU - Parvin, Bahram
N1 - Funding Information:
Manuscript received February 29, 2012; revised May 10, 2012 and July 13, 2012; accepted August 10, 2012. Date of publication September 10, 2012; date of current version November 22, 2012. This work was supported by National Institutes of Health (NIH) under Grant U24 CA1437991 at Lawrence Berkeley National Laboratory under Contract DE-AC02-05CH11231. Asterisk indicates corresponding author.
PY - 2012
Y1 - 2012
N2 - Histological tissue sections provide rich information and continue to be the gold standard for the assessment of tissue neoplasm. However, there are a significant amount of technical and biological variations that impede analysis of large histological datasets. In this paper, we have proposed a novel approach for nuclear segmentation in tumor histology sections, which addresses the problem of technical and biological variations by incorporating information from both manually annotated reference patches and the original image. Subsequently, the solution is formulated within a multireference level set framework. This approach has been validated on manually annotated samples and then applied to the TCGA glioblastoma multiforme (GBM) dataset consisting of 440 whole mount tissue sections scanned with either a 20× or 40 × objective, in which, each tissue section varies in size from 40k × 40k pixels to 100k × 100k pixels. Experimental results show a superior performance of the proposed method in comparison with present state of art techniques.
AB - Histological tissue sections provide rich information and continue to be the gold standard for the assessment of tissue neoplasm. However, there are a significant amount of technical and biological variations that impede analysis of large histological datasets. In this paper, we have proposed a novel approach for nuclear segmentation in tumor histology sections, which addresses the problem of technical and biological variations by incorporating information from both manually annotated reference patches and the original image. Subsequently, the solution is formulated within a multireference level set framework. This approach has been validated on manually annotated samples and then applied to the TCGA glioblastoma multiforme (GBM) dataset consisting of 440 whole mount tissue sections scanned with either a 20× or 40 × objective, in which, each tissue section varies in size from 40k × 40k pixels to 100k × 100k pixels. Experimental results show a superior performance of the proposed method in comparison with present state of art techniques.
KW - Multireference level set
KW - nuclear segmentation
KW - tumor histology sections
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U2 - 10.1109/TBME.2012.2218107
DO - 10.1109/TBME.2012.2218107
M3 - Article
C2 - 22987497
AN - SCOPUS:84870498543
SN - 0018-9294
VL - 59
SP - 3460
EP - 3467
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
IS - 12
M1 - 6298001
ER -