Linescanning - Package for (pre)processing of anatomical and (linescanning) fMRI data

Overview

line scanning repository

plot

This repository contains all of the tools used during the acquisition and postprocessing of line scanning data at the Spinoza Centre for Neuroimaging in Amsterdam. The script master controls the modules prefixed by spinoza_, which in turn call upon various scripts in the utils and bin directory. The scripts in the latter folders are mostly helper scripts to make life a tad easier. The repository contains a mix of languages in bash, python, and matlab.

In active development - do not use unless otherwise instructed by repo owners

Documentation for this package can be found at readthedocs (not up to date)

Policy & To Do

  • install using python setup.py develop
  • Docstrings in numpy format.
  • PEP8 - please set your editor to autopep8 on save!
  • Documentation with Sphinx (WIP)
  • Explore options to streamline code
  • Examples of applications for package (integration of pycortex & pRFpy)

overview of the pipeline

how to set up

Clone the repository: git clone https://github.com/gjheij/linescanning.git.

To setup the bash environment, edit setup file linescanning/shell/spinoza_setup:

  • line 76: add the path to your matlab installation if available (should be, for better anatomicall preprocessing)
  • line 87: add the path to your SPM installation
  • line 92: add your project name
  • line 97: add the path to project name as defined in line 92
  • line 102: add whether you're using (ME)MP(2)RAGE. This is required because the pipeline allows the usage of the average of an MP2RAGE and MP2RAGEME acquisition
  • line 105: add which type of data you're using (generally this will be the same as line 102)

Go to linescanning/shell and hit ./spinoza_setup setup setup. This will print a set of instructions that you need to follow. If all goes well this will make all the script executable, set all the paths, and install the python modules. The repository comes with a conda environment file, which can be activated with: conda create --name myenv --file environment.yml.

How to plan the line

plot

We currently aim to have two separate sessions: in the first session, we acquire high resolution anatomical scans and perform a population receptive field (pRF-) mapping paradigm (Dumoulin and Wandell, 2008) to delineate the visual field. After this session, we create surfaces of the brain and map the pRFs onto that via fMRIprep and pRFpy. We then select a certain vertex based on the parameters extracted from the pRF-mapping: eccentricity, size, and polar angle. Using these parameters, we can find an optimal vertex. We can obtain the vertex position, while by calculating the normal vector, we obtain the orientation that line should have (parellel to the normal vector and through the vertex point). Combining this information, we know how the line should be positioned in the first session anatomy. In the second session, we first acquire a low-resolution MP2RAGE with the volume coil. This is exported and registered to the first session anatomy during the second session to obtain the translations and rotations needed to map the line from the first session anatomy to the currently active second session by inputting the values in the MR-console. This procedure from registration to calculation of MR-console values is governed by spinoza_lineplanning and can be called with master -m 00 -s -h .

Owner
Jurjen Heij
Jurjen Heij
An end-to-end PyTorch framework for image and video classification

What's New: March 2021: Added RegNetZ models November 2020: Vision Transformers now available, with training recipes! 2020-11-20: Classy Vision v0.5 R

Facebook Research 1.5k Dec 31, 2022
This code is an implementation for Singing TTS.

MLP Singer This code is an implementation for Singing TTS. The algorithm is based on the following papers: Tae, J., Kim, H., & Lee, Y. (2021). MLP Sin

Heejo You 22 Dec 23, 2022
In the AI for TSP competition we try to solve optimization problems using machine learning.

AI for TSP Competition Goal In the AI for TSP competition we try to solve optimization problems using machine learning. The competition will be hosted

Paulo da Costa 11 Nov 27, 2022
Fashion Landmark Estimation with HRNet

HRNet for Fashion Landmark Estimation (Modified from deep-high-resolution-net.pytorch) Introduction This code applies the HRNet (Deep High-Resolution

SVIP Lab 91 Dec 26, 2022
Research code for the paper "How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models"

Introduction This repository contains research code for the ACL 2021 paper "How Good is Your Tokenizer? On the Monolingual Performance of Multilingual

AdapterHub 20 Aug 04, 2022
An implementation for `Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction`

Text2Event An implementation for Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction Please contact Yaojie Lu (@

Roger 153 Jan 07, 2023
A PyTorch Lightning Callback for pushing models to the Hugging Face Hub 🤗⚡️

hf-hub-lightning A callback for pushing lightning models to the Hugging Face Hub. Note: I made this package for myself, mostly...if folks seem to be i

Nathan Raw 27 Dec 14, 2022
Package for extracting emotions from social media text. Tailored for financial data.

EmTract: Extracting Emotions from Social Media Text Tailored for Financial Contexts EmTract is a tool that extracts emotions from social media text. I

13 Nov 17, 2022
A PyTorch implementation of Learning to learn by gradient descent by gradient descent

Intro PyTorch implementation of Learning to learn by gradient descent by gradient descent. Run python main.py TODO Initial implementation Toy data LST

Ilya Kostrikov 300 Dec 11, 2022
Tracking code for the winner of track 1 in the MMP-Tracking Challenge at ICCV 2021 Workshop.

Tracking Code for the winner of track1 in MMP-Trakcing challenge This repository contains our tracking code for the Multi-camera Multiple People Track

DamoCV 29 Nov 13, 2022
labelpix is a graphical image labeling interface for drawing bounding boxes

Welcome to labelpix 👋 labelpix is a graphical image labeling interface for drawing bounding boxes. 🏠 Homepage Install pip install -r requirements.tx

schissmantics 26 May 24, 2022
Efficient training of deep recommenders on cloud.

HybridBackend Introduction HybridBackend is a training framework for deep recommenders which bridges the gap between evolving cloud infrastructure and

Alibaba 111 Dec 23, 2022
No-reference Image Quality Assessment(NIQA) Algorithms (BRISQUE, NIQE, PIQE, RankIQA, MetaIQA)

No-Reference Image Quality Assessment Algorithms No-reference Image Quality Assessment(NIQA) is a task of evaluating an image without a reference imag

Dae-Young Song 26 Jan 04, 2023
The all new way to turn your boring vector meshes into the new fad in town; Voxels!

Voxelator The all new way to turn your boring vector meshes into the new fad in town; Voxels! Notes: I have not tested this on a rotated mesh. With fu

6 Feb 03, 2022
Haze Removal can remove slight to extreme cases of haze affecting an image

Haze Removal can remove slight to extreme cases of haze affecting an image. Its most typical use is for landscape photography where the haze causes low contrast and low saturation, but it can also be

Grace Ugochi Nneji 3 Feb 15, 2022
Generic U-Net Tensorflow implementation for image segmentation

Tensorflow Unet Warning This project is discontinued in favour of a Tensorflow 2 compatible reimplementation of this project found under https://githu

Joel Akeret 1.8k Dec 10, 2022
Unicorn can be used for performance analyses of highly configurable systems with causal reasoning

Unicorn can be used for performance analyses of highly configurable systems with causal reasoning. Users or developers can query Unicorn for a performance task.

AISys Lab 27 Jan 05, 2023
Python implementation of MULTIseq barcode alignment using fuzzy string matching and GMM barcode assignment

Python implementation of MULTIseq barcode alignment using fuzzy string matching and GMM barcode assignment.

MT Schmitz 2 Feb 11, 2022
An End-to-End Machine Learning Library to Optimize AUC (AUROC, AUPRC).

Logo by Zhuoning Yuan LibAUC: A Machine Learning Library for AUC Optimization Website | Updates | Installation | Tutorial | Research | Github LibAUC a

Optimization for AI 176 Jan 07, 2023
Stitch it in Time: GAN-Based Facial Editing of Real Videos

STIT - Stitch it in Time [Project Page] Stitch it in Time: GAN-Based Facial Edit

1.1k Jan 04, 2023