The association between uterus shape and development of chronic conditions in a retrospective corhort of diethylstilbestrol-exposed women in Minnesota and Texas

Diethylstilbestrol (DES) exposed women are well known to be at increased risk of gynecologic cancers and infertility. Infertility may result from DES associated abnormalities in the shape of women's uteri, yet little research has addressed the effect of uterine abnormalities on risk of infertility and reproductive tract infection. Changes in uterine shape may also influence the risk of autoimmune disease and women's subsequent mental health. A sample of consenting women exposed in utero to hormone who were recruited into the DESAD project, underwent hysterosalpingogram (HSG) from 1978 to 1984. These women also completed a comprehensive health questionnaire in 1994 which included women's self-reports of chronic conditions. HSG data were used to categorize uterine shape abnormalities as arcuate shape, hypoplastic, wide lower segment, and constricted. Women were recruited from two of the four DESAD study sites in Houston (Baylor) and Minnesota (Mayo). All women were DES-exposed. Adjusted relative risk estimates were calculated comparing the range of abnormal uterine shaped to women with normal shaped uteri for each of the four outcomes: infertility, reproductive tract infection, autoimmune disease and depressive symptoms. Only the arcuate shape (n=80) was associated with a higher risk of infertility (relative risk [RR]= 1.53, 95% CI = 1.09, 2.15) as well as reproductive tract infection (RR= 1.74, 95% CI = 1.11, 2.73). In conclusion, DES-associated arcuate shaped uteri appeared to be associated with the higher risk of a reproductive tract infection and infertility while no other abnormal uterine shapes were associated with these two outcomes.^

Volume and shape in feature space on adaptive FCM in MRI segmentation.

Intensity non-uniformity (bias field) correction, contextual constraints over spatial intensity distribution and non-spherical cluster's shape in the feature space are incorporated into the fuzzy c-means (FCM) for segmentation of three-dimensional multi-spectral MR images. The bias field is modeled by a linear combination of smooth polynomial basis functions for fast computation in the clustering iterations. Regularization terms for the neighborhood continuity of either intensity or membership are added into the FCM cost functions. Since the feature space is not isotropic, distance measures, other than the Euclidean distance, are used to account for the shape and volumetric effects of clusters in the feature space. The performance of segmentation is improved by combining the adaptive FCM scheme with the criteria used in Gustafson-Kessel (G-K) and Gath-Geva (G-G) algorithms through the inclusion of the cluster scatter measure. The performance of this integrated approach is quantitatively evaluated on normal MR brain images using the similarity measures. The improvement in the quality of segmentation obtained with our method is also demonstrated by comparing our results with those produced by FSL (FMRIB Software Library), a software package that is commonly used for tissue classification.