Towards automated multiscale imaging and analysis in TEM: Glomerulus detection by fusion of CNN and LBP maps

Elisabeth Wetzer, Joakim Lindblad, Ida-Maria Sintorn, Kjell Hultenby, Natasa Sladoje; Proceedings of the European Conference on Computer Vision (ECCV) Workshops, 2018, pp. 0-0

Glomerulal structures in kidney tissue have to be analysed at a nanometer scale for several medical diagnoses. They are therefore commonly imaged using Transmission Electron Microscopy. The high resolution produces large amounts of data and requires long acquisition time, which makes automated imaging and glomerulus detection a desired option. This paper presents a deep learning approach for Glomerulus detection, using two architectures, VGG16 (with batch normalization) and ResNet50. To enhance the performance over training based only on intensity images, multiple approaches to fuse the input with texture information encoded in local binary patterns of different scales have been evaluated. The results show a consistent improvement in Glomerulus detection when fusing texture-based trained networks with intensity-based ones at a late classification stage.