A statistical method (cross-validation) for bone loss region detection after spaceflight.
Astronauts experience bone loss after the long spaceflight missions. Identifying specific regions that undergo the greatest losses (e.g. the proximal femur) could reveal information about the processes of bone loss in disuse and disease. Methods for detecting such regions, however, remains an open problem. This paper focuses on statistical methods to detect such regions. We perform statistical parametric mapping to get t-maps of changes in images, and propose a new cross-validation method to select an optimum suprathreshold for forming clusters of pixels. Once these candidate clusters are formed, we use permutation testing of longitudinal labels to derive significant changes.
Zhao
Qian
Q
Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China.
Li
Wenjun
W
Li
Caixia
C
Chu
Philip W
PW
Kornak
John
J
Lang
Thomas F
TF
Fang
Jiqian
J
Lu
Ying
Y
D018480:Astronauts; D056808:Biostatistics; D015519:Bone Density; D016000:Cluster Analysis; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D010024:Osteoporosis / Q000175:diagnosis* / Q000209:etiology*; D013026:Space Flight; D014893:Weightlessness / Q000009:adverse effects