New Segmentation Models for the Radiologic Characterization of Polycystic Kidney Disease Patients from MR and CT Images

Recent advances in genomics have contributed to a better understanding of the pathogenesis of the polycystic kidney disease (PKD), suggesting new treatment strategies to inhibit or delay cyst formation and expansion. The efficacy of these therapies is evaluated by estimation of cystic burden measured by magnetic resonange imaging (MRI) as total kidney volume (TKV). In this Thesis, different imaging approaches are proposed for a correct characterization of the PKD patient by the estimation of renal and cyst volume from magnetic resonance and computed tomography (CT) images. TKV estimation method from MRI relies on a previously validated method developed for axial images that has been adapted and validated to work on coronal images. The results have been compared with the ones obtained from axial images and validated with volume estimation obtained from manual tracing. The performace of the semi-automated method in terms of misclassification of the PKD patient was also evaluated in comparison with other radiologic approaches currently usedfor TKV assessment such as the ellipsoid method and the mid-slice method. A novel method for TKV computation from CT images is proposed. This multi- step approach is completely automated and includes the use of a level set approach to identify the renal contour and so extrapolate the renal volume. The segmented kidneys obtained with the developed methods where used for the segmentation of the cysts. A similar strategy was used for cyst segmentation and counting from MR images. Every cyst agglomerate underwent a voting mechanism based on the curvature of the object interface to distinguish the single cysts. The results of this approach for TCV computation was validated through comparison with TCV obtained by manual segmentation. The last chapter is dedicated to the research activity conducted in the area of diffussion weighted imaging

Deep learning-enabled technologies for bioimage analysis.

Deep learning (DL) is a subfield of machine learning (ML), which has recently demonstrated its potency to significantly improve the quantification and classification workflows in biomedical and clinical applications. Among the end applications profoundly benefitting from DL, cellular morphology quantification is one of the pioneers. Here, we first briefly explain fundamental concepts in DL and then we review some of the emerging DL-enabled applications in cell morphology quantification in the fields of embryology, point-of-care ovulation testing, as a predictive tool for fetal heart pregnancy, cancer diagnostics via classification of cancer histology images, autosomal polycystic kidney disease, and chronic kidney diseases

Deep learning in medical imaging and radiation therapy

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/1/mp13264_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/2/mp13264.pd

Smoking and Second Hand Smoking in Adolescents with Chronic Kidney Disease: A Report from the Chronic Kidney Disease in Children (CKiD) Cohort Study

The goal of this study was to determine the prevalence of smoking and second hand smoking [SHS] in adolescents with CKD and their relationship to baseline parameters at enrollment in the CKiD, observational cohort study of 600 children (aged 1-16 yrs) with Schwartz estimated GFR of 30-90 ml/min/1.73m2. 239 adolescents had self-report survey data on smoking and SHS exposure: 21 [9%] subjects had “ever” smoked a cigarette. Among them, 4 were current and 17 were former smokers. Hypertension was more prevalent in those that had “ever” smoked a cigarette (42%) compared to non-smokers (9%), p\u3c0.01. Among 218 non-smokers, 130 (59%) were male, 142 (65%) were Caucasian; 60 (28%) reported SHS exposure compared to 158 (72%) with no exposure. Non-smoker adolescents with SHS exposure were compared to those without SHS exposure. There was no racial, age, or gender differences between both groups. Baseline creatinine, diastolic hypertension, C reactive protein, lipid profile, GFR and hemoglobin were not statistically different. Significantly higher protein to creatinine ratio (0.90 vs. 0.53, p\u3c0.01) was observed in those exposed to SHS compared to those not exposed. Exposed adolescents were heavier than non-exposed adolescents (85th percentile vs. 55th percentile for BMI, p\u3c 0.01). Uncontrolled casual systolic hypertension was twice as prevalent among those exposed to SHS (16%) compared to those not exposed to SHS (7%), though the difference was not statistically significant (p= 0.07). Adjusted multivariate regression analysis [OR (95% CI)] showed that increased protein to creatinine ratio [1.34 (1.03, 1.75)] and higher BMI [1.14 (1.02, 1.29)] were independently associated with exposure to SHS among non-smoker adolescents. These results reveal that among adolescents with CKD, cigarette use is low and SHS is highly prevalent. The association of smoking with hypertension and SHS with increased proteinuria suggests a possible role of these factors in CKD progression and cardiovascular outcomes

Abstracts from the 50th European Society of Human Genetics Conference: Posters

International audienc