Dissertation/Thesis Abstract

Kidney segmentat ion and image analysis in autosomal dominant polycystic kidney disease
by Warner, Joshua Dale, Ph.D., College of Medicine - Mayo Clinic, 2016, 267; 10111486
Abstract (Summary)

Autosomal Dominant Polycystic Kidney Disease (ADPKD) is among the most prevalent life-threatening genetic conditions. Despite this, no approved medical therapies exist to treat the disease. Until the recent past, no methods could reliably measure the course of the disease far in advance of end stage renal disease (ESRD). As normal tissue is progressively destroyed or blocked by enlarging cysts, remaining nephrons compensate in a process called hyperfiltration. This beneficial physiological response confounds tests of renal function. Thus, potential interventions could not be tested against a reliable measurement of disease progression.

However, progressive changes are visually apparent on medical imaging examinations throughout the course of ADPKD. The search for ADPKD proxy biomarkers is now focused on quantitative imaging, or the extraction of information from medical images for purposes of diagnosis or disease tracking. Recent studies from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)- sponsored Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) showed Total Kidney Volume (TKV) is a usable quantitative imaging biomarker which can track disease in the early, asymptomatic phase and register measurable changes in as little as 12 months. These findings launched several new trials into potential ADPKD therapies.

Advanced analysis of polycystic kidney images, however, has never been done. The method CRISP used to extract TKV was stereology, an efficient means to estimate volume. However, stereology was tradi- tionally a dead end for further advanced analysis. TKV is useful for clinical trials and large population-based studies, but cannot accurately predict disease progression or stratify risk due to known out- lier cases. Thus, the utility of TKV for individual patient prognosis is limited. This work builds upon stereology data, describing a reliable and accurate new semi-automatic method to fully segment images us- ing only labeled stereology grids. Then, two new second generation quantitative imaging biomarkers are introduced and analyzed: Cyst- Parenchyma Surface Area (CPSA) and cyst concentration. These new physiologically motivated biomarkers will complement or potentially replace TKV in efforts to bring quantitative imaging to individual patients.

The goal of this body of work is to enable a pathway for efficient advanced image analysis in ADPKD, never before attempted in this dis- order, and to define new quantitative imaging biomarkers which will complement or replace existing ones in hopes of making individualized disease tracking for ADPKD patients a reality.

Indexing (document details)
Advisor: Erickson, Bradley J.
Commitee: Harris, Peter C., Holmes, David R., King, Bernard F., Mitchell, J. Ross, Ottesen, Hal H.
School: College of Medicine - Mayo Clinic
Department: Biomedical Engineering
School Location: United States -- Minnesota
Source: DAI-B 77/10(E), Dissertation Abstracts International
Source Type: DISSERTATION
Subjects: Medicine, Medical imaging, Computer science
Keywords: ADPKD, Image biomarkers, Image segmentation, Polycystic kidney disease, Python, Quantitative imaging
Publication Number: 10111486
ISBN: 9781339746951
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