17 citations · 21 across the 5 of their papers we have counts for
5 papers
VoxelEmbed: 3D Instance Segmentation and Tracking with Voxel Embedding based Deep Learning
Mengyang Zhao, Quan Liu, Aadarsh Jha +6
Recent advances in bioimaging have provided scientists a superior high spatial-temporal resolution to observe dynamics of living cells as 3D volumetric videos. Unfortunately, the 3…
ASIST: Annotation-free Synthetic Instance Segmentation and Tracking by Adversarial Simulations
Quan Liu, Isabella M. Gaeta, Mengyang Zhao +6
Background: The quantitative analysis of microscope videos often requires instance segmentation and tracking of cellular and subcellular objects. The traditional method consists of…
ASIST: Annotation-free synthetic instance segmentation and tracking for microscope video analysis
Quan Liu, Isabella M. Gaeta, Mengyang Zhao +6
Instance object segmentation and tracking provide comprehensive quantification of objects across microscope videos. The recent single-stage pixel-embedding based deep learning appr…
CaCL: Class-aware Codebook Learning for Weakly Supervised Segmentation on Diffuse Image Patterns
Ruining Deng, Quan Liu, Shunxing Bao +5
Weakly supervised learning has been rapidly advanced in biomedical image analysis to achieve pixel-wise labels (segmentation) from image-wise annotations (classification), as biome…
GAN based Unsupervised Segmentation: Should We Match the Exact Number of Objects
Quan Liu, Isabella M. Gaeta, Bryan A. Millis +2
The unsupervised segmentation is an increasingly popular topic in biomedical image analysis. The basic idea is to approach the supervised segmentation task as an unsupervised synth…