  Biomedical images are noisy. The imaging equipment itself has physical
limitations, and the consequent experimental trade-offs between signal-to-noise
ratio, acquisition speed, and imaging depth exacerbate the problem. Denoising
is, therefore, an essential part of any image processing pipeline, and
convolutional neural networks are currently the method of choice for this task.
One popular approach, Noise2Noise, does not require clean ground truth, and
instead, uses a second noisy copy as a training target. Self-supervised
methods, like Noise2Self and Noise2Void, relax data requirements by learning
the signal without an explicit target but are limited by the lack of
information in a single image. Here, we introduce Noise2Stack, an extension of
the Noise2Noise method to image stacks that takes advantage of a shared signal
between spatially neighboring planes. Our experiments on magnetic resonance
brain scans and newly acquired multiplane microscopy data show that learning
only from image neighbors in a stack is sufficient to outperform Noise2Noise
and Noise2Void and close the gap to supervised denoising methods. Our findings
point towards low-cost, high-reward improvement in the denoising pipeline of
multiplane biomedical images. As a part of this work, we release a microscopy
dataset to establish a benchmark for the multiplane image denoising.
