TY - GEN
T1 - Automatic identification and delineation of germ layer components in H&E stained images of teratomas derived from human and nonhuman primate embryonic stem cells
AU - Bhagavatula, Ramamurthy
AU - Fickus, Matthew
AU - Kelly, W.
AU - Guo, Chenlei
AU - Ozolek, John A.
AU - Castro, Carlos A.
AU - Kovačević, Jelena
PY - 2010
Y1 - 2010
N2 - We present a methodology for the automatic identification and delineation of germ-layer components in H&E stained images of teratomas derived from human and nonhuman primate embryonic stem cells. A knowledge and understanding of the biology of these cells may lead to advances in tissue regeneration and repair, the treatment of genetic and developmental syndromes, and drug testing and discovery. As a teratoma is a chaotic organization of tissues derived from the three primary embryonic germ layers, H&E teratoma images often present multiple tissues, each of having complex and unpredictable positions, shapes, and appearance with respect to each individual tissue as well as with respect to other tissues. While visual identification of these tissues is timeconsuming, it is surprisingly accurate, indicating that there exist enough visual cues to accomplish the task. We propose automatic identification and delineation of these tissues by mimicking these visual cues. We use pixel-based classification, resulting in an encouraging range of classification accuracies from 74.9% to 93.2% for 2- to 5-tissue classification experiments at different scales.
AB - We present a methodology for the automatic identification and delineation of germ-layer components in H&E stained images of teratomas derived from human and nonhuman primate embryonic stem cells. A knowledge and understanding of the biology of these cells may lead to advances in tissue regeneration and repair, the treatment of genetic and developmental syndromes, and drug testing and discovery. As a teratoma is a chaotic organization of tissues derived from the three primary embryonic germ layers, H&E teratoma images often present multiple tissues, each of having complex and unpredictable positions, shapes, and appearance with respect to each individual tissue as well as with respect to other tissues. While visual identification of these tissues is timeconsuming, it is surprisingly accurate, indicating that there exist enough visual cues to accomplish the task. We propose automatic identification and delineation of these tissues by mimicking these visual cues. We use pixel-based classification, resulting in an encouraging range of classification accuracies from 74.9% to 93.2% for 2- to 5-tissue classification experiments at different scales.
KW - Classification
KW - Feature extraction
KW - Image analysis
KW - Stem cell biology
UR - http://www.scopus.com/inward/record.url?scp=77955196330&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77955196330&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2010.5490168
DO - 10.1109/ISBI.2010.5490168
M3 - Conference contribution
AN - SCOPUS:77955196330
SN - 9781424441266
T3 - 2010 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010 - Proceedings
SP - 1041
EP - 1044
BT - 2010 7th IEEE International Symposium on Biomedical Imaging
T2 - 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010
Y2 - 14 April 2010 through 17 April 2010
ER -