kreshuklab

plant-seg

A tool for cell instance aware segmentation in densely packed 3D volumetric images

spoco

PyTorch implementation of SPOCO

go-nuclear

Guides and code for 3D nuclear instance segmentation

MorphoFeatures

MorphoFeatures code and data

hylfm-net

teaching-dl-course-2019

EMBL Deep Learning course 2019 exercises and materials

teaching-dl-course-2022

Materials for the EMBL Deep Learning course

EMBL-Deep-Learning-course-2023-

EMBL Deep Learning course 2023 exercises and materials

vem-primer-examples

mouse-embryo-seg

3D segmentation of nuclei (fixed) and cells (live) of the developing mouse embryo

.github

Kreshuk Group - Machine learning for bioimage analysis

Kinetochores_segmentation

predoc-course

Kreshuk Lab's EMBL EIPP predoc course teaching material, each branch keeps the record of a specific season

shallow2deep

Shallow2deep: Exploiting feature-based classifiers for domain adaptation in semantic segmentation

teaching-dl-course-2020

EMBL Deep Learning course 2020-21 exercises and materials

3D-GT-tools

List of tools for creating ground truth annotations for 3D data

embryo_stage_classifier

Model for classifying drosophila embryo stages

gpu_envs

Setting up mixed conda and easybuild environments on the EMBL cluster

Plantseg-Cellpose-Augmentation

bioimg_rs

A rust implementation of bioimage spec parser

covid-if-2

drosophila_embryo_cells

Drosophila emryo cell classification

kreshuklab-utils

Python package for common lab code

neuroclear

Neuroclear is a deep-learning-based Python module to train a deep neural network for the task of applying super-resolution to degraded axial resolution in fluorescence microscopy, using a single image stack.

rewardchecking

Checking reward functions for lines and circles with line and circle data.

RLForSeg

sea-urchin-em-segmentation

takafumi_embryos_segmentation

torch-em

Deep-learning based semantic and instance segmentation for 3D Electron Microscopy and other bioimage analysis problems based on pytorch.

unsup_pix_embed

Experiments for unsupervised pixel embeddings