mCNA: Multi-Clone Communication Neural Architecture Search for 3D Medical Image Segmentation

Volumetric medical image segmentation requires precise delineation of anatomical structures under substantial variations in organ size, shape, and appearance, while being constrained by high memory and computational costs of 3D processing. Neural Architecture Search (NAS) provides a principled approach for automated architecture design, but directly searching large 3D segmentation networks is prohibitively expensive due to the combinatorial complexity of jointly optimizing cell operations, skip connections, network topology, and model capacity under dense volumetric representations. To address this challenge, we propose Multi-Clone Communication Neural Architecture (mCNA), a search-efficient 3D NAS framework that decomposes 3D architecture optimization into compact cell search and clonable macro-level expansion. Instead of searching a complete large-scale U-Net topology, mCNA first discovers an optimized 3D U-Net backbone and then constructs a larger architecture by replicating the searched unit with lightweight Communication Units for inter-clone feature exchange and cooperative refinement. To avoid costly discrete skip-topology exploration, we introduce a Path Attention Module that performs differentiable adaptive fusion of multi-scale encoder features. Furthermore, we incorporate structure-preserving wavelet-based downsampling operations with pre-optimized and fixed filters into the search space to retain fine-grained volumetric information during downsampling. Experiments on Synapse and the Medical Segmentation Decathlon benchmark demonstrate that mCNA achieves the best overall performance among the evaluated methods under aligned preprocessing, training, and evaluation protocols. Extensive ablation studies verify the effectiveness of the proposed cell search, clone communication, adaptive skip fusion, and wavelet-based downsampling components. The searched architecture can also be transferred across datasets without additional architecture search, demonstrating improved search efficiency and architectural generalizability.