AIMC Topic: Cell Separation
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Fast Cell Segmentation Using Scalable Sparse Manifold Learning and Affine Transform-approximated Active Contour.
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Nov 18, 2015
Efficient and effective cell segmentation of neuroendocrine tumor (NET) in whole slide scanned images is a difficult task due to a large number of cells. The weak or misleading cell boundaries also present significant challenges. In this paper, we pr...
Automated, high-throughput derivation, characterization and differentiation of induced pluripotent stem cells.
Nature methods
Aug 3, 2015
Induced pluripotent stem cells (iPSCs) are an essential tool for modeling how causal genetic variants impact cellular function in disease, as well as an emerging source of tissue for regenerative medicine. The preparation of somatic cells, their repr...
Robotic adherent cell injection for characterizing cell-cell communication.
IEEE transactions on bio-medical engineering
Jul 24, 2014
Compared to robotic injection of suspended cells (e.g., embryos and oocytes), fewer attempts were made to automate the injection of adherent cells (e.g., cancer cells and cardiomyocytes) due to their smaller size, highly irregular morphology, small t...
AI-guided laser purification of human iPSC-derived cardiomyocytes for next-generation cardiac cell manufacturing.
Communications biology
May 13, 2025
Current methods for producing cardiomyocytes from human induced pluripotent stem cells (hiPSCs) using 2D monolayer differentiation are often hampered by batch-to-batch variability and inefficient purification processes. Here, we introduce CM-AI, a no...
User-friendly image-activated microfluidic cell sorting technique using an optimized, fast deep learning algorithm.
Lab on a chip
May 4, 2021
Image-activated cell sorting is an essential biomedical research technique for understanding the unique characteristics of single cells. Deep learning algorithms can be used to extract hidden cell features from high-content image information to enabl...
Intelligent image-activated cell sorting 2.0.
Lab on a chip
Jun 30, 2020
The advent of intelligent image-activated cell sorting (iIACS) has enabled high-throughput intelligent image-based sorting of single live cells from heterogeneous populations. iIACS is an on-chip microfluidic technology that builds on a seamless inte...
Label-free optical hemogram of granulocytes enhanced by artificial neural networks.
Optics express
May 13, 2019
An outstanding challenge for immunology is the classification of immune cells in a label-free fashion with high speed. For this purpose, optical techniques such as Raman spectroscopy or digital holographic microscopy have been used successfully to id...
A platform for artificial intelligence based identification of the extravasation potential of cancer cells into the brain metastatic niche.
Lab on a chip
Mar 27, 2019
Brain metastases are the most lethal complication of advanced cancer; therefore, it is critical to identify when a tumor has the potential to metastasize to the brain. There are currently no interventions that shed light on the potential of primary t...
Leukocyte recognition in human fecal samples using texture features.
Journal of the Optical Society of America. A, Optics, image science, and vision
Nov 1, 2018
Unlike urine or blood samples with a single background, human fecal samples contain large amounts of food debris, amorphous particles, and undigested plant cells. It is difficult to segment such impurities when mixed with leukocytes. Cell degradation...
Gating mass cytometry data by deep learning.
Bioinformatics (Oxford, England)
Nov 1, 2017
MOTIVATION: Mass cytometry or CyTOF is an emerging technology for high-dimensional multiparameter single cell analysis that overcomes many limitations of fluorescence-based flow cytometry. New methods for analyzing CyTOF data attempt to improve autom...
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