There has been exploding interest in embracing Transformer-based architectures for medical image segmentation. However, the lack of large-scale annotated medical datasets make achieving performances equivalent to those in natural images challenging. Convolutional networks, in contrast, have higher inductive biases and consequently, are easily trainable to high performance. Recently, the ConvNeXt architecture attempted to modernize the standard ConvNet by mirroring Transformer blocks. In this work, we improve upon this to design a modernized and scalable convolutional architecture customized to challenges of data-scarce medical settings. We introduce MedNeXt, a Transformer-inspired large kernel segmentation network which introduces - 1) A fully ConvNeXt 3D Encoder-Decoder Network for medical image segmentation, 2) Residual ConvNeXt up and downsampling blocks to preserve semantic richness across scales, 3) A novel technique to iteratively increase kernel sizes by upsampling small kernel networks, to prevent performance saturation on limited medical data, 4) Compound scaling at multiple levels (depth, width, kernel size) of MedNeXt. This leads to state-of-the-art performance on 4 tasks on CT and MRI modalities and varying dataset sizes, representing a modernized deep architecture for medical image segmentation. Our code is made publicly available at: https://github.com/MIC-DKFZ/MedNeXt.

翻译：近年来，基于Transformer架构的医学图像分割方法引发了爆炸性关注。然而，大规模标注医学数据集的缺乏使得在这些任务上达到与自然图像相当的性能面临挑战。相比之下，卷积网络具有更高的归纳偏置，因此更容易训练至高性能。近期，ConvNeXt架构试图通过模仿Transformer块来现代化标准卷积网络。在本工作中，我们对此进行改进，设计了一种适应数据稀缺医学场景挑战的现代化可扩展卷积架构。我们引入MedNeXt——一种受Transformer启发的卷积核分割网络，具有以下特点：1）用于医学图像分割的全ConvNeXt 3D编码器-解码器网络；2）保留跨尺度语义丰富性的残差ConvNeXt上下采样块；3）一种通过上采样小卷积核网络来迭代增大核尺寸的新技术，以防止在有限医学数据上出现性能饱和；4）在MedNeXt的多个层面（深度、宽度、核尺寸）进行复合缩放。该方法在CT和MRI模态的4个任务及不同数据集规模上均达到最先进性能，代表了医学图像分割的现代化深度架构。我们的代码已公开于：https://github.com/MIC-DKFZ/MedNeXt。