Neural Architecture Search for Medical Image Segmentation

Abstract

Deep Neural Networks (DNNs) have the potential to make various clinical procedures more time-efficient by automating medical image segmentation; largely due to their strong, in some cases human-level, performance. The design of the best possible medical image segmentation DNN, however, is task-specific. Neural Architecture Search (NAS), i.e., the automation of neural network design, has been shown to have the capability to outperform manually designed networks. However, the existing NAS methods for medical image segmentation have explored a quite limited range of types of DNN architectures that can be discovered, and, more importantly, do not evaluate the accuracy of performance estimation methods.

In this thesis, various performance estimation methods for DNNs are analysed for medical image segmentation tasks. Due to the use of different metrics, small datasets, and inter-physician variability, DNN performance values are susceptible to considerable noise. Through experiments on multiple datasets, it is shown that performance estimation needs to be more elaborate than proposed in previous literature on NAS for medical image segmentation. Only then can the noise induced by the problem be overcome. Based on evaluations of NAS performance with different levels of noise, a method is put forward to evaluate this noise, such that a more informed decision on performance estimation can be made.

The second contribution of this thesis, is the proposal of a novel NAS search space for medical image segmentation networks. This search space combines the strength of a generalised encoder-decoder structure, well known from U-Net, with network blocks that have been proven to have a strong performance in image classification tasks. The search is performed by looking for the best topology of the network, and simultaneously searching the configuration of each cell. This allows for interactions between topology- and cell-level attributes to be found. Experiments were performed on two publicly available datasets. The networks discovered by the proposed NAS method perform better than well-known handcrafted segmentation networks, and outperform networks found with other NAS approaches that perform only topology search, and topology-level search followed by cell-level search.

Finally, three search algorithms are compared for different performance estimation methods on a realistic clinical medical image segmentation task. The results show that the performance of these algorithms is very similar in noisy environments for initial runs, and show deterioration of performance for all algorithms when correlation with the validation performance values are low. This supports the findings that not adapting performance estimation to the task at hand will lead to poor NAS performance, no matter the chosen search algorithm.