Unet and its variations have been standard in semantic image segmentation, especially for computer assisted radiology. Current Unet architectures iteratively downsample spatial resolution while increasing channel dimensions to preserve information content. Such a structure demands a large memory footprint, limiting training batch sizes and increasing inference latency. Channel pruning compresses Unet architecture without accuracy loss, but requires lengthy optimization and may not generalize across tasks and datasets. By investigating Unet pruning, we hypothesize that the final structure is the crucial factor, not the channel selection strategy of pruning. Based on our observations, we propose a lean Unet architecture (LUnet) with a compact, flat hierarchy where channels are not doubled as resolution is halved. We evaluate on a public MRI dataset allowing comparable reporting, as well as on two internal CT datasets. We show that a state-of-the-art pruning solution (STAMP) mainly prunes from the layers with the highest number of channels. Comparatively, simply eliminating a random channel at the pruning-identified layer or at the largest layer achieves similar or better performance. Our proposed LUnet with fixed architectures and over 30 times fewer parameters achieves performance comparable to both conventional Unet counterparts and data-adaptively pruned networks. The proposed lean Unet with constant channel count across layers requires far fewer parameters while achieving performance superior to standard Unet for the same total number of parameters. Skip connections allow Unet bottleneck channels to be largely reduced, unlike standard encoder-decoder architectures requiring increased bottleneck channels for information propagation.

翻译：Unet及其变体已成为语义图像分割领域的标准方法，尤其在计算机辅助放射学中应用广泛。当前的Unet架构通过迭代下采样空间分辨率并增加通道维度以保持信息完整性，但这种结构需要较大的内存占用，限制了训练批次大小并增加了推理延迟。通道剪枝技术可在不损失精度的情况下压缩Unet架构，但需要冗长的优化过程，且可能无法跨任务和数据集泛化。通过对Unet剪枝的研究，我们假设最终结构是关键因素，而非剪枝的通道选择策略。基于这一观察，我们提出了一种精简的Unet架构（LUnet），采用紧凑的扁平层次结构，在分辨率减半时通道数不再翻倍。我们在允许可比性报告的公开MRI数据集以及两个内部CT数据集上进行了评估。实验表明，最先进的剪枝方法（STAMP）主要针对通道数最多的层进行剪枝。相比之下，在剪枝识别层或最大层中随机删除一个通道，也能达到相似甚至更好的性能。我们提出的LUnet采用固定架构，参数量减少超过30倍，其性能与传统Unet模型及数据自适应剪枝网络相当。所提出的精简Unet在各层保持恒定通道数，所需参数量大幅减少，在总参数量相同的情况下性能优于标准Unet。跳跃连接使得Unet瓶颈通道可大幅缩减，这与标准编码器-解码器架构需增加瓶颈通道以传播信息的机制形成鲜明对比。