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Greg M. Fleishman's Projects

ants icon ants

Advanced Normalization Tools (ANTs)

cellpose icon cellpose

a generalist algorithm for cellular segmentation

clusterwrap icon clusterwrap

Wrappers around dask-jobqueue functions for specific clusters

dask icon dask

Parallel computing with task scheduling

dask_stitch icon dask_stitch

Linear blend stitching for map_overlap calls in dask

decode icon decode

This is the official implementation of our publication "Deep learning enables fast and dense single-molecule localization with high accuracy" (Nature Methods)

fishspot icon fishspot

tools for finding discrete bright spots in images (for multiplexed FISH, includes background subtraction, deconv, and blob detection)

greedypy icon greedypy

Greedy weak diffeomorphic image registration in Python

hnccorr icon hnccorr

HNCcorr algorithm for neuron identification in calcium imaging datasets.

lddmem icon lddmem

Large Deformation Diffeomorphic Metric Embedding: embed smooth deformable transforms from ANTs, Greedy, Elastix, CMTK etc. in the LDDMM framework.

ngff-zarr icon ngff-zarr

A lean and kind Open Microscopy Environment (OME) Next Generation File Format (NGFF) Zarr implementation.

opencv icon opencv

Open Source Computer Vision Library

ossi-website icon ossi-website

Website showcasing projects supported through the Janelia Open Science Software Initiative (OSSI)

pyrpl icon pyrpl

PyRPL: Python Registration Prototyping Library. A Python package with deformable image registration algorithms, and modular components to build them.

regiprep icon regiprep

Preprocess confocal images for deformable registration.

simpleitk icon simpleitk

SimpleITK: a layer built on top of the Insight Toolkit (ITK), intended to simplify and facilitate ITK's use in rapid prototyping, education and interpreted languages.

spotgraph icon spotgraph

Tools for dealing with smFISH spots and nuclei

stardist icon stardist

StarDist - Object Detection with Star-convex Shapes

stream_old icon stream_old

Parallel deformable alignment of large N5 volumes using a cluster

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