Access to the proper infrastructure is critical when performing medical image segmentation with Deep Learning. This requirement makes it difficult to run state-of-the-art segmentation models in resource-constrained scenarios like primary care facilities in rural areas and during crises. The recently emerging field of Neural Cellular Automata (NCA) has shown that locally interacting one-cell models can achieve competitive results in tasks such as image generation or segmentations in low-resolution inputs. However, they are constrained by high VRAM requirements and the difficulty of reaching convergence for high-resolution images. To counteract these limitations we propose Med-NCA, an end-to-end NCA training pipeline for high-resolution image segmentation. Our method follows a two-step process. Global knowledge is first communicated between cells across the downscaled image. Following that, patch-based segmentation is performed. Our proposed Med-NCA outperforms the classic UNet by 2% and 3% Dice for hippocampus and prostate segmentation, respectively, while also being 500 times smaller. We also show that Med-NCA is by design invariant with respect to image scale, shape and translation, experiencing only slight performance degradation even with strong shifts; and is robust against MRI acquisition artefacts. Med-NCA enables high-resolution medical image segmentation even on a Raspberry Pi B+, arguably the smallest device able to run PyTorch and that can be powered by a standard power bank.

翻译：在深度学习医学图像分割中，获取适当的基础设施至关重要。这一要求使得在资源受限场景（如农村初级医疗机构及危机时期）中运行先进分割模型变得困难。新兴的神经细胞自动机（NCA）领域已证明，局部交互的单细胞模型可在低分辨率输入下的图像生成或分割等任务中取得有竞争力的结果。然而，它们受限于高显存需求及高分辨率图像收敛困难的问题。为克服这些限制，我们提出Med-NCA——一种用于高分辨率图像分割的端到端NCA训练流程。该方法遵循两步过程：首先在降采样图像上的细胞间传递全局知识，随后进行基于分块的分割。我们提出的Med-NCA在海马体和前列腺分割任务中分别以2%和3%的Dice系数超越经典UNet，同时模型体积缩小500倍。我们还证明，Med-NCA在设计上对图像尺度、形状和平移具有不变性，即使发生强烈位移也仅有轻微性能下降；且对MRI采集伪影具有鲁棒性。Med-NCA甚至可在树莓派B+上实现高分辨率医学图像分割——该设备是能运行PyTorch的最小机型之一，可由标准充电宝供电。