An easy-to-use, nonlinear filter for effective background identification in fluorescence microscopy images with dense and low-contrast foreground.

(Preprint on BioRxiv currently under review)

We have received R21 grant award from NCI's ITCR program to develop methods for characterizing mechanobiology of the tumor microenvironment landscape!

New work from our lab pushes forward the performance limits of unsupervised methods in the difficult task of segmenting cells and their nuclei in tissue samples in the context of immunofluorescence imaging and its highly multiplexed counterpart. As part of UNSEG, we have a developed a new perturbed watershed method that stabilizes and improves classical watershed performance for segmenting clustered nuclei.

A microscopy image submitted by Bogdan Kochetov and Rebecca Raphael to a world-wide Micrograph Awards competition held by Microscopy Society of America (MSA) was selected as one of the 25 finalists. It was published on July cover of Microscopy Today (Oxford University Press). It is the fourth image along the diagonal on the cover page linked below.

We were awarded the Biology Data Insights grant by the Chan Zuckerberg Initiative!

Rajdeep will be giving a talk at the ISCB 16th Great Lakes Bioinformatics (GLBIO) conference on his work on reconstructing 3D microenvironment from its 2D slices at full-resolution and with sub-cellular accuracy.

Quantitative phase imaging (QPI) offers a label-free approach to non-invasively characterize cellular processes by exploiting their refractive index based intrinsic contrast. QPI captures this contrast by translating refractive index associated phase shifts into intensity-based quantifiable data with nanoscale sensitivity. It, therefore, provides quantitative characterization of biophysical properties of cells and tissue in their natural states. Here we review QPI research, discuss its potential and identify its challenges in the context of advancing precision cancer medicine.

Caitlin is a first year graduate student in the Joint CMU-Pitt Ph.D. Program in Computational Biology (CPCB).

And she is a new member of the lab.

Welcome Caitlin!

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