Detecting Nanosheet Objects from Noisy CLSM Images Using Deep Learning Approach

Abstract

This paper considers a problem of detecting nanosheets which are moving in colloidal liquid from confocal laser scanning microscopy (CLSM) images. Introducing the deep learning approach, we particularly develop a scheme for constructing the so-called `detection map’ consisting of the brightness value information on the area of nanosheets in CLSM images. Therein, we use an architecture of deep learning network ‘U-net’ and present how to implement such a network. The performance is demonstrated by some experimental studies.