2020 Quantification analysis of actin filaments in microscopic images

The actin filament plays a fundamental role in numerous cellular processes such as cell growth, proliferation, migration, division, and locomotion. The actin cytoskeleton is highly dynamical and can polymerize and depolymerize in a very short time under different stimuli. To study the mechanics of actin filament, quantifying the length and number of actin filaments in each time frame of microscopic images is fundamental. Different from microtubules, actin filaments is considered as from intersections/endpoints to intersection/endpoints, namely indiviudal segments of the actin network. In this project, we adopt a Convolutional Neural Network (CNN) to segment actin filaments first, and then we utilize a modified Resnet to detect junctions and endpoints of filaments. With binary segmentation and detected keypoints, we apply a fast marching algorithm to obtain the number and length of each actin filament in microscopic images. We have also collected a dataset of 10 microscopic images of actin filaments to test our method. Our experiments show that our approach outperforms other existing approaches tackling this problem regarding both accuracy and inference time.

2019 Quantification analysis of microtubule in microscopic images

Filamentous structures play an important role in biolog-ical systems. Extracting individual filaments is fundamental for analyzing and quantifying related biological processes. However, segmenting filamentous structures at an instancelevel is hampered by their complex architecture, uniformappearance, and image quality. In this project, I propse an orientation-aware neural network, which contains six orientation-associated branches. Each branch detects filaments with specific range of orientations, thus separatingthem at junctions, and turning intersections to overpasses. A terminus pairing algorithm is also proposed to regroupfilaments from different branches, and achieve individual filaments extraction.