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
Introducing neural networks to predict stock prices

IntroNeuralNetworks in Python: A Template Project IntroNeuralNetworks is a project that introduces neural networks and illustrates an example of how o

Vivek Palaniappan 637 Jan 04, 2023
pytorch implementation for PointNet

PointNet.pytorch This repo is implementation for PointNet in pytorch. The model is in pointnet/model.py. It is teste

Fei Xia 1.7k Dec 30, 2022
TorchOk - The toolkit for fast Deep Learning experiments in Computer Vision

TorchOk - The toolkit for fast Deep Learning experiments in Computer Vision

52 Dec 23, 2022
DirectVoxGO reconstructs a scene representation from a set of calibrated images capturing the scene.

DirectVoxGO reconstructs a scene representation from a set of calibrated images capturing the scene. We achieve NeRF-comparable novel-view synthesis quality with super-fast convergence.

sunset 709 Dec 31, 2022
UniMoCo: Unsupervised, Semi-Supervised and Full-Supervised Visual Representation Learning

UniMoCo: Unsupervised, Semi-Supervised and Full-Supervised Visual Representation Learning This is the official PyTorch implementation for UniMoCo pape

dddzg 49 Jan 02, 2023
Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology (LMRL Workshop, NeurIPS 2021)

Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology Self-Supervised Vision Transformers Learn Visual Concepts in Histopatholog

Richard Chen 95 Dec 24, 2022
TCPNet - Temporal-attentive-Covariance-Pooling-Networks-for-Video-Recognition

Temporal-attentive-Covariance-Pooling-Networks-for-Video-Recognition This is an implementation of TCPNet. Introduction For video recognition task, a g

Zilin Gao 21 Dec 08, 2022
Repositorio oficial del curso IIC2233 Programación Avanzada 🚀✨

IIC2233 - Programación Avanzada Evaluación Las evaluaciones serán efectuadas por medio de actividades prácticas en clases y tareas. Se calculará la no

IIC2233 @ UC 0 Dec 15, 2022
Code release for NeuS

NeuS We present a novel neural surface reconstruction method, called NeuS, for reconstructing objects and scenes with high fidelity from 2D image inpu

Peng Wang 813 Jan 04, 2023
A more easy-to-use implementation of KPConv based on PyTorch.

A more easy-to-use implementation of KPConv This repo contains a more easy-to-use implementation of KPConv based on PyTorch. Introduction KPConv is a

Zheng Qin 36 Dec 29, 2022
BRNet - code for Automated assessment of BI-RADS categories for ultrasound images using multi-scale neural networks with an order-constrained loss function

BRNet code for "Automated assessment of BI-RADS categories for ultrasound images using multi-scale neural networks with an order-constrained loss func

Yong Pi 2 Mar 09, 2022
The code from the paper Character Transformations for Non-Autoregressive GEC Tagging

Character Transformations for Non-Autoregressive GEC Tagging Milan Straka, Jakub Náplava, Jana Straková Charles University Faculty of Mathematics and

ÚFAL 5 Dec 10, 2022
ViSER: Video-Specific Surface Embeddings for Articulated 3D Shape Reconstruction

ViSER: Video-Specific Surface Embeddings for Articulated 3D Shape Reconstruction. NeurIPS 2021.

Gengshan Yang 59 Nov 25, 2022
Implementations of orthogonal and semi-orthogonal convolutions in the Fourier domain with applications to adversarial robustness

Orthogonalizing Convolutional Layers with the Cayley Transform This repository contains implementations and source code to reproduce experiments for t

CMU Locus Lab 36 Dec 30, 2022
[CVPR2021] DoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datasets

DoDNet This repo holds the pytorch implementation of DoDNet: DoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datase

116 Dec 12, 2022
Implementation of Heterogeneous Graph Attention Network

HetGAN Implementation of Heterogeneous Graph Attention Network This is the code repository of paper "Prediction of Metro Ridership During the COVID-19

5 Dec 28, 2021
Fashion Entity Classification

Fashion-Entity-Classification - Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grays

ADITYA SHAH 1 Jan 04, 2022
A curated list of resources for Image and Video Deblurring

A curated list of resources for Image and Video Deblurring

Subeesh Vasu 1.7k Jan 01, 2023
GndNet: Fast ground plane estimation and point cloud segmentation for autonomous vehicles using deep neural networks.

GndNet: Fast Ground plane Estimation and Point Cloud Segmentation for Autonomous Vehicles. Authors: Anshul Paigwar, Ozgur Erkent, David Sierra Gonzale

Anshul Paigwar 114 Dec 29, 2022
Pseudo lidar - (CVPR 2019) Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving

Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving This paper has been accpeted by Conference o

Yan Wang 881 Dec 27, 2022