We present {\mu}Split, a dedicated approach for trained image decomposition in the context of fluorescence microscopy images. We find that best results using regular deep architectures are achieved when large image patches are used during training, making memory consumption the limiting factor to further improving performance. We therefore introduce lateral contextualization (LC), a memory efficient way to train powerful networks and show that LC leads to consistent and significant improvements on the task at hand. We integrate LC with U-Nets, Hierarchical AEs, and Hierarchical VAEs, for which we formulate a modified ELBO loss. Additionally, LC enables training deeper hierarchical models than otherwise possible and, interestingly, helps to reduce tiling artefacts that are inherently impossible to avoid when using tiled VAE predictions. We apply {\mu}Split to five decomposition tasks, one on a synthetic dataset, four others derived from real microscopy data. LC achieves SOTA results (average improvements to the best baseline of 2.36 dB PSNR), while simultaneously requiring considerably less GPU memory.

翻译：我们提出μSplit，一种专门针对荧光显微镜图像训练的分解方法。研究发现，使用常规深度架构时，训练时采用大图像块可取得最佳效果，但内存消耗成为进一步提升性能的瓶颈。为此，我们引入侧向上下文化（LC）这一内存高效训练策略，并证明该方法在目标任务中能持续带来显著改进。我们将LC与U-Net、分层自编码器及分层变分自编码器集成，其中为后者推导了改进的ELBO损失函数。此外，LC可训练更深层的分层模型，且有趣的是能有效减少分块VAE预测中不可避免的拼接伪影。我们将μSplit应用于五项分解任务（一项基于合成数据集，四项源自真实显微数据）。LC实现了SOTA结果（相较于最优基线平均提升2.36 dB PSNR），同时大幅降低GPU内存占用。