Investigation on Encoder-Decoder Networks for Segmentation of Very Degraded X-Ray CT Tomograms

Field of View Nano-CT X-Ray synchrotron imaging is used for acquiring brain neuronal features from Golgi-stained bio-samples. It theoretically requires a large number of acquired radiographs for compensating reconstruction noise reinforced by the brain features sparsity. However reducing the number of radiographs is essential in routine applications but it results to degraded tomograms. In such a case, traditional segmentation methods are no longer able to distinguish neuronal structures from surrounding noise. We investigate several existing deep-learning networks and we define new ones to segment brain features from very degraded tomograms. We demonstrate the superiority of the proposed networks compared to existing ones.