Fully Convolutional Networks (FCNs) have emerged as powerful segmentation models but are usually designed manually, which requires extensive time and can result in large and complex architectures. There is a growing interest to automatically design efficient architectures that can accurately segment 3D medical images. However, most approaches either do not fully exploit volumetric information or do not optimize the model's size. To address these problems, we propose a self-adaptive 2D-3D ensemble of FCNs called AdaEn-Net for 3D medical image segmentation that incorporates volumetric data and adapts to a particular dataset by optimizing both the model's performance and size. The AdaEn-Net consists of a 2D FCN that extracts intra-slice information and a 3D FCN that exploits inter-slice information. The architecture and hyperparameters of the 2D and 3D architectures are found through a multiobjective evolutionary based algorithm that maximizes the expected segmentation accuracy and minimizes the number of parameters in the network. The main contribution of this work is a model that fully exploits volumetric information and automatically searches for a high-performing and efficient architecture. The AdaEn-Net was evaluated for prostate segmentation on the PROMISE12 Grand Challenge and for cardiac segmentation on the MICCAI ACDC challenge. In the first challenge, the AdaEn-Net ranks 9 out of 297 submissions and surpasses the performance of an automatically-generated segmentation network while producing an architecture with 13× fewer parameters. In the second challenge, the proposed model is ranked within the top 8 submissions and outperforms an architecture designed with reinforcement learning while having 1.25× fewer parameters.

中文翻译：

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AdaEn-Net：自适应2D-3D全卷积网络的集成，用于医学图像分割。

完全卷积网络（FCN）已经成为强大的分段模型，但通常是手动设计的，这需要大量时间，并且会导致大型复杂的体系结构。自动设计可以准确分割3D医学图像的高效体系结构的兴趣日益浓厚。但是，大多数方法要么没有充分利用体积信息，要么没有优化模型的大小。为了解决这些问题，我们提出了一种称为AdaEn-Net的FCN自适应2D-3D集成，用于3D医学图像分割，该合并了体积数据并通过优化模型的性能和大小来适应特定的数据集。AdaEn-Net由提取切片内信息的2D FCN和利用切片间信息的3D FCN组成。2D和3D架构的体系结构和超参数是通过基于多目标进化的算法找到的，该算法最大程度地提高了预期的分割精度，并最小化了网络中的参数数量。这项工作的主要贡献是一个可以充分利用体积信息并自动搜索高性能和高效架构的模型。在PROMISE12 Grand Challenge中评估了AdaEn-Net的前列腺分割，在MICCAI ACDC挑战中评估了心脏分割。在第一个挑战中，AdaEn-Net在297个提交中排名9，并超越了自动生成的分段网络的性能，同时生成的参数减少了13倍。在第二个挑战中