High-Throughput Glomeruli Analysis of micro-CT Kidney Images Using Tree Priors and Scalable Sparse Computation

Correa Shokiche, Carlos Enrique; Baumann, Philipp; Hlushchuk, Ruslan; Djonov, Valentin; Reyes, Mauricio (17 October 2016). High-Throughput Glomeruli Analysis of micro-CT Kidney Images Using Tree Priors and Scalable Sparse Computation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Lecture Notes in Computer Science: Vol. 9901 (pp. 370-378). Cham: Springer International Publishing 10.1007/978-3-319-46723-8_43

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Kidney-related diseases have incrementally become one major cause of death. Glomeruli are the physiological units in the kidney responsible for the blood filtration. Therefore, their statistics including number and volume, directly describe the efficiency and health state of the kidney. Stereology is the current quantification method relying on histological sectioning, sampling and further 2D analysis, being laborious and sample destructive. New micro-Computed Tomography (μCT) imaging protocols resolute structures down to capillary level. However large-scale glomeruli analysis remains challenging due to object identifiability, allotted memory resources and computational time. We present a methodology for high-throughput glomeruli analysis that incorporates physiological apriori information relating the kidney vasculature with estimates of glomeruli counts. We propose an effective sampling strategy that exploits scalable sparse segmentation of kidney regions for refined estimates of both glomeruli count and volume. We evaluated the proposed approach on a database of μCT datasets yielding a comparable segmentation accuracy as an exhaustive supervised learning method. Furthermore we show the ability of the proposed sampling strategy to result in improved estimates of glomeruli counts and volume without requiring a exhaustive segmentation of the μCT image. This approach can potentially be applied to analogous organizations, such as for example the quantification of alveoli in lungs.

Item Type:

Conference or Workshop Item (Paper)

Division/Institute:

04 Faculty of Medicine > Pre-clinic Human Medicine > Institute for Surgical Technology & Biomechanics ISTB
04 Faculty of Medicine > Pre-clinic Human Medicine > Institute of Anatomy
03 Faculty of Business, Economics and Social Sciences > Department of Business Management > Institute of Financial Management > Professorship for Quantitative Methods in Business Administration

UniBE Contributor:

Correa Shokiche, Carlos Enrique; Baumann, Philipp; Hlushchuk, Ruslan; Djonov, Valentin and Reyes, Mauricio

Subjects:

000 Computer science, knowledge & systems
500 Science > 570 Life sciences; biology
600 Technology > 610 Medicine & health

ISBN:

978-3-319-46722-1

Series:

Lecture Notes in Computer Science

Publisher:

Springer International Publishing

Language:

English

Submitter:

Mauricio Antonio Reyes Aguirre

Date Deposited:

23 May 2017 16:24

Last Modified:

15 Sep 2017 14:28

Publisher DOI:

10.1007/978-3-319-46723-8_43

BORIS DOI:

10.7892/boris.97769

URI:

https://boris.unibe.ch/id/eprint/97769

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