Extracting regions of interest from biological images with convolutional sparse block coding
Biological tissue is often composed of cells with similar morphologies replicated throughout large volumes and many biological applications rely on the accurate identification of these cells and their locations from image data. Here we develop a generative model that captures the regularities present in images composed of repeating elements of a few different types. Formally, the model can be described as convolutional sparse block coding. For inference we use a variant of convolutional matching pursuit adapted to block-based representations. We extend the K-SVD learning algorithm to subspaces by retaining several principal vectors from the SVD decomposition instead of just one. Good models with little cross-talk between subspaces can be obtained by learning the blocks incrementally. We perform extensive experiments on simulated images and the inference algorithm consistently recovers a large proportion of the cells with a small number of false positives. We fit the convolutional model to noisy GCaMP6 two-photon images of spiking neurons and to Nissl-stained slices of cortical tissue and show that it recovers cell body locations without supervision. The flexibility of the block-based representation is reflected in the variability of the recovered cell shapes.
Summary: the authors developed a novel method 
      specifically to address an increasingly important problem in neuroscience 
      and cell biology more generally: extract cell bodies from noisy images. 
      they adapt a well-known dictionary learning method, k-svd, to this domain, 
      and generalize the inference scheme to make computations for efficient. 
      The images demonstrate fruitful results, and the quantitative results 
      demonstrate useful performance, although not at the level of a human 
      expert. Quality: i very much like the paper. goal & methods 
      were defined clearly, performance is useful. some suggestions for possible 
      improvements/extensions: the method does not take into account 
      time-varying information. when 2 cells are spatially overlapping (eg, in 
      different planes), then the only information enabling separation might be 
      time-varying information. moreover, if there is drift in the image, 
      potentially due to animal movement, then averaging across all the images 
      will suffer, and some additional tracking of cells might be useful. also, 
      it is relatively common for experimentalists to collect multiple channels, 
      say one structural and one functional. this method could utilize that 
      information to help improve SNR. It would have been nice to see a 
      quantitative comparison between this method and any other previously 
      proposed method. many others have proposed methods to extract shapes, and 
      specifically cells, from images. i imagine this method would outperform 
      the others, at least in terms of computational complexity for some of 
      them, but maybe not. Clarity: i found the paper to be clear. 
      Originality: a few modifications of k-svd, and a useful and 
      interesting application, is sufficiently original for me. 
      Significance: if the code is shared, i imagine this work could be 
      highly significant. otherwise, i imagine the authors will have to write 
      another paper targeted at the neuroscience/biology community to express 
      the ideas in a language they are more comfortable 
      with.