Deep learning plays a critical role in medical image segmentation. Nevertheless, manually designing a neural network for a specific segmentation problem is a very difficult and time-consuming task due to the massive hyperparameter search space, long training time and large volumetric data. Therefore, most designed networks are highly complex, task specific and over-parametrized. Recently, multiobjective neural architecture search (NAS) methods have been proposed to automate the design of accurate and efficient segmentation architectures. However, they only search for either the micro- or macro-structure of the architecture, do not use the information produced during the optimization process to increase the efficiency of the search, or do not consider the volumetric nature of medical images. In this work, we present EMONAS-Net, an Efficient MultiObjective NAS framework for 3D medical image segmentation that optimizes both the segmentation accuracy and size of the network. EMONAS-Net has two key components, a novel search space that considers the configuration of the micro- and macro-structure of the architecture and a Surrogate-assisted Multiobjective Evolutionary based Algorithm (SaMEA algorithm) that efficiently searches for the best hyperparameter values. The SaMEA algorithm uses the information collected during the initial generations of the evolutionary process to identify the most promising subproblems and select the best performing hyperparameter values during mutation to improve the convergence speed. Furthermore, a Random Forest surrogate model is incorporated to accelerate the fitness evaluation of the candidate architectures. EMONAS-Net is tested on the tasks of prostate segmentation from the MICCAI PROMISE12 challenge, hippocampus segmentation from the Medical Segmentation Decathlon challenge, and cardiac segmentation from the MICCAI ACDC challenge. In all the benchmarks, the proposed framework finds architectures that perform better or comparable with competing state-of-the-art NAS methods while being considerably smaller and reducing the architecture search time by more than 50%.

深度学习在医学图像分割中起着至关重要的作用。然而，由于超参数搜索空间庞大、训练时间长和体积数据大，为特定的分割问题手动设计神经网络是一项非常困难且耗时的任务。因此，大多数设计的网络都是高度复杂的、特定于任务的和过度参数化的。最近，已经提出了多目标神经架构搜索（NAS）方法来自动设计准确有效的分割架构。然而，他们只搜索架构的微观或宏观结构，没有使用优化过程中产生的信息来提高搜索效率，或者没有考虑医学图像的体积特性。在这项工作中，我们提出了 EMONAS-Net，一个È fficient中号ULTI ö BJECTIVE NAS用于3D医学图像分割的框架，优化分割精度和网络的大小两者。EMONAS-Net的具有两个关键部件，考虑了微观和体系结构的宏观结构和的结构的新颖的搜索空间小号urrogate-一个ssisted中号ultiobjective ê volutionary基于甲有效搜索最佳超参数值的算法（SaMEA 算法）。SaMEA 算法使用在进化过程的初始阶段收集的信息来识别最有希望的子问题，并在变异过程中选择性能最佳的超参数值以提高收敛速度。此外，还结合了随机森林代理模型来加速候选架构的适应度评估。EMONAS-Net 在来自 MICCAI PROMISE12 挑战的前列腺分割、来自医学分割十项全能挑战的海马体分割和来自 MICCAI ACDC 挑战的心脏分割任务上进行了测试。在所有的基准测试中，