Using Noisy Evaluation to Accelerate Parameter Optimization of Medical Image Segmentation Ensembles

Abstract

An important concern with regard to the ensembles of algorithms is that using the individually optimal parameter settings of the members does not necessarily maximize the performance of the ensemble itself. In this paper, we propose a novel evaluation method for simulated annealing that combines dataset sampling and image downscaling to accelerate the parameter optimization of medical image segmentation ensembles. The scaling levels and sample sizes required to maintain the convergence of the search are theoretically determined by adapting previous results for simulated annealing with imprecise energy measurements. To demonstrate the efficiency of the proposed method, we optimize the parameters of an ensemble for lung segmentation in CT scans. Our experimental results show that the proposed method can maintain the solution quality of the base method with significantly lower runtime. In our problem, optimization with simulated annealing yielded an F1 score of 0.9397 and an associated MCC of 0.7757. Our proposed method maintained the solution quality with an F1 score of 0.9395 and MCC of 0.7755 while exhibiting a 42.01% reduction in runtime. It was also shown that the proposed method is more efficient than simulated annealing with only sampling-based evaluation when the dataset size is below a problem-specific threshold.