title: Towards Automated Three-Dimensional Tracking of Nephrons through Stacked Histological Image Sets.
abstract: An automated approach for tracking individual nephrons through three-dimensional histological image sets of mouse and rat kidneys is presented. In a previous study, the available images were tracked manually through the image sets in order to explore renal microarchitecture. The purpose of the current research is to reduce the time and effort required to manually trace nephrons by creating an automated, intelligent system as a standard tool for such datasets. The algorithm is robust enough to isolate closely packed nephrons and track their convoluted paths despite a number of nonideal, interfering conditions such as local image distortions, artefacts, and interstitial tissue interference. The system comprises image preprocessing, feature extraction, and a custom graph-based tracking algorithm, which is validated by a rule base and a machine learning algorithm. A study of a selection of automatically tracked nephrons, when compared with manual tracking, yields a 95% tracking accuracy for structures in the cortex, while those in the medulla have lower accuracy due to narrower diameter and higher density. Limited manual intervention is introduced to improve tracking, enabling full nephron paths to be obtained with an average of 17 manual corrections per mouse nephron and 58 manual corrections per rat nephron.
authors.affiliation: Biomedical Engineering Research Group, School of Electrical & Information Engineering, University of the Witwatersrand Johannesburg, Private Bag 3, Johannesburg 2050, South Africa.
authors.forename: Charita
authors.initials: C
authors.lastname: Bhikha
authors.affiliation: Department of Biomedicine, University of Aarhus, 8000 Aarhus C, Denmark.
authors.forename: Arne
authors.initials: A
authors.lastname: Andreasen
authors.affiliation: Department of Biomedicine, University of Aarhus, 8000 Aarhus C, Denmark.
authors.forename: Erik I
authors.initials: EI
authors.lastname: Christensen
authors.affiliation: Biomedical Engineering Research Group, School of Electrical & Information Engineering, University of the Witwatersrand Johannesburg, Private Bag 3, Johannesburg 2050, South Africa.
authors.forename: Robyn F R
authors.initials: RF
authors.lastname: Letts
authors.affiliation: Biomedical Engineering Research Group, School of Electrical & Information Engineering, University of the Witwatersrand Johannesburg, Private Bag 3, Johannesburg 2050, South Africa.
authors.forename: Adam
authors.initials: A
authors.lastname: Pantanowitz
authors.affiliation: Biomedical Engineering Research Group, School of Electrical & Information Engineering, University of the Witwatersrand Johannesburg, Private Bag 3, Johannesburg 2050, South Africa.
authors.forename: David M
authors.initials: DM
authors.lastname: Rubin
authors.affiliation: Department of Biomedicine, University of Aarhus, 8000 Aarhus C, Denmark.
authors.forename: Jesper S
authors.initials: JS
authors.lastname: Thomsen
authors.affiliation: Department of Histology and Embryology, China Medical University, Shenyang, Liaoning 110122, China.
authors.forename: Xiao-Yue
authors.initials: XY
authors.lastname: Zhai
mesh_terms: D000465:Algorithms; D000818:Animals; D001331:Automation; D003201:Computers; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D005189:False Positive Reactions; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional / Q000379:methods*; D007672:Kidney Cortex / Q000473:pathology; D000069550:Machine Learning; D008297:Male; D051379:Mice; D009399:Nephrons / Q000473:pathology*; D051381:Rats; D017208:Rats, Wistar; D015203:Reproducibility of Results