TY - GEN
T1 - Integration of spatial distribution in imaging-genetics
AU - Subramanian, Vaishnavi
AU - Tang, Weizhao
AU - Chidester, Benjamin
AU - Ma, Jian
AU - Do, Minh N.
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018
Y1 - 2018
N2 - To better understand diseases such as cancer, it is crucial for computational inference to quantify the spatial distribution of various cell types within a tumor. To this end, we used Ripley’s K-statistic, which captures the spatial distribution patterns at different scales of both individual point sets and interactions between multiple point sets. We propose to improve the expressivity of histopathology image features by incorporating this descriptor to capture potential cellular interactions, especially interactions between lymphocytes and epithelial cells. We demonstrate the utility of the Ripley’s K-statistic by analyzing digital slides from 710 TCGA breast invasive carcinoma (BRCA) patients. In particular, we consider its use in the context of imaging-genetics to understand correlations between gene expression and image features using canonical correlation analysis (CCA). Our analysis shows that including these spatial features leads to more significant associations between image features and gene expression.
AB - To better understand diseases such as cancer, it is crucial for computational inference to quantify the spatial distribution of various cell types within a tumor. To this end, we used Ripley’s K-statistic, which captures the spatial distribution patterns at different scales of both individual point sets and interactions between multiple point sets. We propose to improve the expressivity of histopathology image features by incorporating this descriptor to capture potential cellular interactions, especially interactions between lymphocytes and epithelial cells. We demonstrate the utility of the Ripley’s K-statistic by analyzing digital slides from 710 TCGA breast invasive carcinoma (BRCA) patients. In particular, we consider its use in the context of imaging-genetics to understand correlations between gene expression and image features using canonical correlation analysis (CCA). Our analysis shows that including these spatial features leads to more significant associations between image features and gene expression.
UR - http://www.scopus.com/inward/record.url?scp=85054071998&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85054071998&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-00934-2_28
DO - 10.1007/978-3-030-00934-2_28
M3 - Conference contribution
AN - SCOPUS:85054071998
SN - 9783030009335
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 245
EP - 253
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings
A2 - Fichtinger, Gabor
A2 - Davatzikos, Christos
A2 - Alberola-López, Carlos
A2 - Frangi, Alejandro F.
A2 - Schnabel, Julia A.
PB - Springer
T2 - 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
Y2 - 16 September 2018 through 20 September 2018
ER -