Effective Hyperparameter Optimization with Proxy Data for Multi-organ Segmentation

The purpose of this paper is to introduce a practical framework of using proxy data in automatic hyperparameter optimization for 3D multi-organ segmentation. The automated segmentation of abdominal organs from CT volumes is a major task in the medical image analysis field. Much research has been investigated to handle this task based on the immense experience of machine learning. Deep learning approaches require enormous experiments to design the optimal configurations for the best performance. Automatic machine learning (AutoML) using hyperparameter optimization to search the optimal training strategy makes it possible to find the appropriate settings without much deep experience. However, biases of training data can be highly related to the AutoML performance and efficiency. In this paper, we propose an AutoML framework that uses pre-selected proxy data to represent the entire dataset which has the potential to reduce the computation time needed for efficient hyperparameter optimization in searching learning. Both quantitative and qualitative results showed that our framework can effectively build more powerful segmentation models than manually designed deep-learning-based methods, which use carefully tuned hyperparameters, or an AutoML method with randomly selected training subsets. Our method achieved an average Dice score for 10-class abdominal organ segmentation of 85.9%.