Paper Info
Title
Sensitivity analysis in digital pathology: Handling large number of parameters with compute expensive workflows.
Abstract
Digital pathology imaging enables valuable quantitative characterizations of tissue state at the sub-cellular level. While there is a growing set of methods for analysis of whole slide tissue images, many of them are sensitive to changes in input parameters. Evaluating how analysis results are affected by variations in input parameters is important for the development of robust methods. Executing algorithm sensitivity analyses by systematically varying input parameters is an expensive task because a single evaluation run with a moderate number of tissue images may take hours or days. Our work investigates the use of Surrogate Models (SMs) along with parallel execution to speed up parameter sensitivity analysis (SA). This approach significantly reduces the SA cost, because the SM execution is inexpensive. The evaluation of several SM strategies with two image segmentation workflows demonstrates that a SA study with SMs attains results close to a SA with real application runs (mean absolute error lower than 0.022), while the SM accelerates the SA execution by 51 × . We also show that, although the number of parameters in the example workflows is high, most of the uncertainty can be associated with a few parameters. In order to identify the impact of variations in segmentation results to downstream analyses, we carried out a survival analysis with 387 Lung Squamous Cell Carcinoma cases. This analysis was repeated using 3 values for the most significant parameters identified by the SA for the two segmentation algorithms; about 600 million cell nuclei were segmented per run. The results show that significance of the survival correlations of patient groups, assessed by a logrank test, are strongly affected by the segmentation parameter changes. This indicates that sensitivity analysis is an important tool for evaluating the stability of conclusions from image analyses.
Year
DOI
Venue
2019
10.1016/j.compbiomed.2019.03.006
Computers in Biology and Medicine
Keywords
Field
DocType
Whole Slide Image Analysis,Sensitivity analysis,Surrogate models,Microscopy,Survival analysis,Uncertainty propagation
Pattern recognition,Segmentation,Computer science,Mean absolute error,Lung squamous cell carcinoma,Digital pathology,Image segmentation,Artificial intelligence,Workflow,Speedup,Moderate number
Journal
Volume
ISSN
Citations 
108
0010-4825
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Jeremias Gomes100.34
Willian Barreiros211.03
Tahsin M. Kurç31423149.77
Alba Cristina Magalhaes Alves De Melo425333.90
Jun Kong510617.74
Joel H. Saltz64046569.91
George Teodoro715022.18