Supplementary Figure 2: Comparison of membrane segmentation on ssET data using CDeep3M and three widely used machine learning algorithms.
From: CDeep3M—Plug-and-Play cloud-based deep learning for image segmentation
a, Challenging segmentation tasks, such as recognition of membranes in the electron tomography dataset, cannot be solved with sufficient accuracy using widely used machine learning tools, such as CHM (Front. Neuroanat. 8, 2014), Ilastik (Proc. Int. Symposium Biomed. Imaging 230–233, 2011), or the Trainable Weka Segmentation (Bioinformatics 33, 2424–2426, 2017) (resulting in large missing membranes or widespread introduction of false positive signal) and requires deep learning tools, such as CDeep3M, to achieve a high level of accuracy. b, Because of the high accuracy of the predictions of CDeep3M, simpler postprocessing such as watershed and region-growing algorithms can be used to accomplish dense segmentation on a small scale. In comparison, we were unable to produce meaningful results using this approach on the prediction maps of the aforementioned machine learning tools. On a larger scale, more sophisticated region agglomeration techniques should be used (Nat. Methods 14, 101–102, 2017; A deep structured learning approach towards automating connectome reconstruction from 3D electron micrographs. Preprint at arXiv, https://arxiv.org/abs/1709.02974 (2017)) and will allow one to take full advantage of the membrane segmentation.
