Abstract
Segmentation of intact cell nuclei from three-dimensional (3D) images of thick tissue sections is an important basic capability necessary for many biological research studies. However, segmentation is often difficult because of the tight clustering of nuclei in many specimen types. We present a 3D segmentation approach that combines the recognition capabilities of the human visual system with the efficiency of automatic image analysis algorithms. The approach first uses automatic algorithms to separate the 3D image into regions of fluorescence-stained nuclei and unstained background. This includes a novel step, based on the Hough transform and an automatic focusing algorithm to estimate the size of nuclei. Then, using an interactive display, each nuclear region is shown to the analyst, who classifies it as either an individual nucleus, a cluster of multiple nuclei, partial nucleus or debris. Next, automatic image analysis based on morphological reconstruction and the watershed algorithm divides clusters into smaller objects, which are reclassified by the analyst. Once no more clusters remain, the analyst indicates which partial nuclei should be joined to form complete nuclei. The approach was assessed by calculating the fraction of correctly segmented nuclei for a variety of tissue types: Caenorhabditis elegans embryos (839 correct out of a total of 848), normal human skin (343/362), benign human breast tissue (492/525) a human breast cancer cell line grown as a xenograft in mice (425/479) and invasive human breast carcinoma (260/335). Furthermore, due to the analyst's involvement in the segmentation process, it is always known which nuclei in a population are correctly segmented and which not, assuming that the analyst's visual judgement is correct.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 212-226 |
| Number of pages | 15 |
| Journal | Journal of Microscopy |
| Volume | 193 |
| Issue number | 3 |
| DOIs | |
| State | Published - 1999 |
| Externally published | Yes |
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Keywords
- Confocal microscopy
- Image analysis
- Image segmentation
- Three-dimensional analysis
ASJC Scopus subject areas
- Instrumentation
Cite this
Segmentation of confocal microscope images of cell nuclei in thick tissue sections. / Ortiz De Solórzano, Carlos; García Rodriguez, E.; Jones, A.; Pinkel, D.; Gray, Joe; Sudar, D.; Lockett, S. J.
In: Journal of Microscopy, Vol. 193, No. 3, 1999, p. 212-226.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Segmentation of confocal microscope images of cell nuclei in thick tissue sections
AU - Ortiz De Solórzano, Carlos
AU - García Rodriguez, E.
AU - Jones, A.
AU - Pinkel, D.
AU - Gray, Joe
AU - Sudar, D.
AU - Lockett, S. J.
PY - 1999
Y1 - 1999
N2 - Segmentation of intact cell nuclei from three-dimensional (3D) images of thick tissue sections is an important basic capability necessary for many biological research studies. However, segmentation is often difficult because of the tight clustering of nuclei in many specimen types. We present a 3D segmentation approach that combines the recognition capabilities of the human visual system with the efficiency of automatic image analysis algorithms. The approach first uses automatic algorithms to separate the 3D image into regions of fluorescence-stained nuclei and unstained background. This includes a novel step, based on the Hough transform and an automatic focusing algorithm to estimate the size of nuclei. Then, using an interactive display, each nuclear region is shown to the analyst, who classifies it as either an individual nucleus, a cluster of multiple nuclei, partial nucleus or debris. Next, automatic image analysis based on morphological reconstruction and the watershed algorithm divides clusters into smaller objects, which are reclassified by the analyst. Once no more clusters remain, the analyst indicates which partial nuclei should be joined to form complete nuclei. The approach was assessed by calculating the fraction of correctly segmented nuclei for a variety of tissue types: Caenorhabditis elegans embryos (839 correct out of a total of 848), normal human skin (343/362), benign human breast tissue (492/525) a human breast cancer cell line grown as a xenograft in mice (425/479) and invasive human breast carcinoma (260/335). Furthermore, due to the analyst's involvement in the segmentation process, it is always known which nuclei in a population are correctly segmented and which not, assuming that the analyst's visual judgement is correct.
AB - Segmentation of intact cell nuclei from three-dimensional (3D) images of thick tissue sections is an important basic capability necessary for many biological research studies. However, segmentation is often difficult because of the tight clustering of nuclei in many specimen types. We present a 3D segmentation approach that combines the recognition capabilities of the human visual system with the efficiency of automatic image analysis algorithms. The approach first uses automatic algorithms to separate the 3D image into regions of fluorescence-stained nuclei and unstained background. This includes a novel step, based on the Hough transform and an automatic focusing algorithm to estimate the size of nuclei. Then, using an interactive display, each nuclear region is shown to the analyst, who classifies it as either an individual nucleus, a cluster of multiple nuclei, partial nucleus or debris. Next, automatic image analysis based on morphological reconstruction and the watershed algorithm divides clusters into smaller objects, which are reclassified by the analyst. Once no more clusters remain, the analyst indicates which partial nuclei should be joined to form complete nuclei. The approach was assessed by calculating the fraction of correctly segmented nuclei for a variety of tissue types: Caenorhabditis elegans embryos (839 correct out of a total of 848), normal human skin (343/362), benign human breast tissue (492/525) a human breast cancer cell line grown as a xenograft in mice (425/479) and invasive human breast carcinoma (260/335). Furthermore, due to the analyst's involvement in the segmentation process, it is always known which nuclei in a population are correctly segmented and which not, assuming that the analyst's visual judgement is correct.
KW - Confocal microscopy
KW - Image analysis
KW - Image segmentation
KW - Three-dimensional analysis
UR - http://www.scopus.com/inward/record.url?scp=0032966937&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0032966937&partnerID=8YFLogxK
U2 - 10.1046/j.1365-2818.1999.00463.x
DO - 10.1046/j.1365-2818.1999.00463.x
M3 - Article
C2 - 10199001
AN - SCOPUS:0032966937
VL - 193
SP - 212
EP - 226
JO - Journal of Microscopy
JF - Journal of Microscopy
SN - 0022-2720
IS - 3
ER -