Volumetric parameterization of the placenta to a flattened template

Related tags

Deep Learningplacenta-flattening
Overview

placenta-flattening

A MATLAB algorithm for volumetric mesh parameterization. Developed for mapping a placenta segmentation derived from an MRI image to a flattened template for visualization. The code can work on NIFTI images or MATLAB matrices containing imaging information.

Requirements

Add the MATLAB packages to the working path.

Usage

main(grayImage, segImage): input a grayscale MRI image and the corresponding binary segmentation, where voxels labeled '1' correspond to the placenta. The inputs grayImage, segImage can either be full path locations of NIFTI image files, or image matrices. The grayImage input can be a 3D MRI volume, or a 4D series of MRI volumes. The script outputs the flattened meshes and images containing the mapped intensities.

Development

Please contact Mazdak Abulnaga, [email protected].

Citing and Paper

Please consider citing our paper

@inproceedings{abulnaga2019placenta,
title={Placental Flattening via Volumetric Parameterization},
author={Abulnaga, S. Mazdak and Abaci Turk, Esra and Bessmeltsev, Mikhail and Grant, P. Ellen and Solomon, Justin and Golland, Polina},
booktitle={Medical Image Computing and Computer Assisted Intervention -- MICCAI 2019},
year={2019},
pages={39--47},
}
You might also like...
This project uses Template Matching technique for object detecting by detection of template image over base image
This project uses Template Matching technique for object detecting by detection of template image over base image

Object Detection Project Using OpenCV This project uses Template Matching technique for object detecting by detection the template image over base ima

Pratham Bhatnagar 4 Nov 16, 2021
Generic template to bootstrap your PyTorch project with PyTorch Lightning, Hydra, W&B, and DVC.

NN Template Generic template to bootstrap your PyTorch project. Click on Use this Template and avoid writing boilerplate code for: PyTorch Lightning,

Luca Moschella 520 Dec 30, 2022
All the essential resources and template code needed to understand and practice data structures and algorithms in python with few small projects to demonstrate their practical application.

Data Structures and Algorithms Python INDEX 1. Resources - Books Data Structures - Reema Thareja competitiveCoding Big-O Cheat Sheet DAA Syllabus Inte

Shushrut Kumar 129 Dec 15, 2022
Grammar Induction using a Template Tree Approach

Gitta Gitta ("Grammar Induction using a Template Tree Approach") is a method for inducing context-free grammars. It performs particularly well on data

Thomas Winters 36 Nov 15, 2022
A template repository for submitting a job to the Slurm Cluster installed at the DISI - University of Bologna

Cluster di HPC con GPU per esperimenti di calcolo (draft version 1.0) Per poter utilizzare il cluster il primo passo è abilitare l'account istituziona

null 20 Dec 16, 2022
This is a template for the Non-autoregressive Deep Learning-Based TTS model (in PyTorch).

Non-autoregressive Deep Learning-Based TTS Template This is a template for the Non-autoregressive TTS model. It contains Data Preprocessing Pipeline D

Keon Lee 13 Dec 5, 2022
STMTrack: Template-free Visual Tracking with Space-time Memory Networks

STMTrack This is the official implementation of the paper: STMTrack: Template-free Visual Tracking with Space-time Memory Networks. Setup Prepare Anac

Zhihong Fu 62 Dec 21, 2022
A modified version of DeepMind's Alphafold2 to divide CPU part (MSA and template searching) and GPU part (prediction model)

ParallelFold Author: Bozitao Zhong This is a modified version of DeepMind's Alphafold2 to divide CPU part (MSA and template searching) and GPU part (p

Bozitao Zhong 77 Dec 22, 2022
Source Code For Template-Based Named Entity Recognition Using BART

Template-Based NER Source Code For Template-Based Named Entity Recognition Using BART Training Training train.py Inference inference.py Corpus ATIS (h

null 174 Dec 19, 2022
Comments
  • Is it possible to provide public input data?

    Is it possible to provide public input data?

    Hello, I am a master student majoring in computer science. I am very interested in your work and hope to reproduce the results, but because there is no MRI scan data, I cannot successfully run the program to observe the effect. If you can provide the program input data, I will really appreciate.

    opened by yuemos 3
  • 4d

    4d

    Now accepts 4D input grayscale images as input. Implemented by adding a loop when mapping the MRI images to the flattened space. I've tested with 4D nifti input, but not 4D image matrices.

    opened by PaddySlator 0
Releases(v1.0.0)
Owner
Mazdak Abulnaga
CS PhD student at MIT
Mazdak Abulnaga
GitHub Repository
On Out-of-distribution Detection with Energy-based Models

On Out-of-distribution Detection with Energy-based Models This repository contains the code for the experiments conducted in the paper On Out-of-distr

Sven 19 Aug 07, 2022
PASTRIE: A Corpus of Prepositions Annotated with Supersense Tags in Reddit International English

PASTRIE Official release of the corpus described in the paper: Michael Kranzlein, Emma Manning, Siyao Peng, Shira Wein, Aryaman Arora, and Nathan Schn

NERT @ Georgetown 4 Dec 02, 2021
This application is the basic of automated online-class-joiner(for YıldızEdu) within the right time. Gets the ZOOM link by scheduled date and time.

This application is the basic of automated online-class-joiner(for YıldızEdu) within the right time. Gets the ZOOM link by scheduled date and time.

215355 1 Dec 16, 2021
BigDetection: A Large-scale Benchmark for Improved Object Detector Pre-training

BigDetection: A Large-scale Benchmark for Improved Object Detector Pre-training By Likun Cai, Zhi Zhang, Yi Zhu, Li Zhang, Mu Li, Xiangyang Xue. This

290 Dec 29, 2022
A concise but complete implementation of CLIP with various experimental improvements from recent papers

x-clip (wip) A concise but complete implementation of CLIP with various experimental improvements from recent papers Install $ pip install x-clip Usag

Phil Wang 515 Dec 26, 2022
N-Omniglot is a large neuromorphic few-shot learning dataset

N-Omniglot [Paper] || [Dataset] N-Omniglot is a large neuromorphic few-shot learning dataset. It reconstructs strokes of Omniglot as videos and uses D

11 Dec 05, 2022
Code for Discriminative Sounding Objects Localization (NeurIPS 2020)

Discriminative Sounding Objects Localization Code for our NeurIPS 2020 paper Discriminative Sounding Objects Localization via Self-supervised Audiovis

51 Dec 11, 2022
Semi-supervised Domain Adaptation via Minimax Entropy

Semi-supervised Domain Adaptation via Minimax Entropy (ICCV 2019) Install pip install -r requirements.txt The code is written for Pytorch 0.4.0, but s

Vision and Learning Group 243 Jan 09, 2023
Multi-Scale Vision Longformer: A New Vision Transformer for High-Resolution Image Encoding

Vision Longformer This project provides the source code for the vision longformer paper. Multi-Scale Vision Longformer: A New Vision Transformer for H

Microsoft 209 Dec 30, 2022
Code for "Contextual Non-Local Alignment over Full-Scale Representation for Text-Based Person Search"

Contextual Non-Local Alignment over Full-Scale Representation for Text-Based Person Search This is an implementation for our paper Contextual Non-Loca

Tencent YouTu Research 50 Dec 03, 2022
Learning Dynamic Network Using a Reuse Gate Function in Semi-supervised Video Object Segmentation.

Training Script for Reuse-VOS This code implementation of CVPR 2021 paper : Learning Dynamic Network Using a Reuse Gate Function in Semi-supervised Vi

HYOJINPARK 22 Jan 01, 2023
Revealing and Protecting Labels in Distributed Training

Revealing and Protecting Labels in Distributed Training

Google Interns 0 Nov 09, 2022
Official Implementation of SimIPU: Simple 2D Image and 3D Point Cloud Unsupervised Pre-Training for Spatial-Aware Visual Representations

Official Implementation of SimIPU SimIPU: Simple 2D Image and 3D Point Cloud Unsupervised Pre-Training for Spatial-Aware Visual Representations Since

Zhyever 37 Dec 01, 2022
T2F: text to face generation using Deep Learning

⭐ [NEW] ⭐ T2F - 2.0 Teaser (coming soon ...) Please note that all the faces in the above samples are generated ones. The T2F 2.0 will be using MSG-GAN

Animesh Karnewar 533 Dec 22, 2022
Self-Supervised Generative Style Transfer for One-Shot Medical Image Segmentation

Self-Supervised Generative Style Transfer for One-Shot Medical Image Segmentation This repository contains the Pytorch implementation of the proposed

Devavrat Tomar 19 Nov 10, 2022
Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving

SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving Abstract In this paper, we introduce SalsaNext f

308 Jan 04, 2023
ATAC: Adversarially Trained Actor Critic

ATAC: Adversarially Trained Actor Critic Adversarially Trained Actor Critic for Offline Reinforcement Learning by Ching-An Cheng*, Tengyang Xie*, Nan

Microsoft 41 Dec 08, 2022
Clean Machine Learning, a Coding Kata

Kata: Clean Machine Learning From Dirty Code First, open the Kata in Google Colab (or else download it) You can clone this project and launch jupyter-

Neuraxio 13 Nov 03, 2022
OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)

OCTIS : Optimizing and Comparing Topic Models is Simple! OCTIS (Optimizing and Comparing Topic models Is Simple) aims at training, analyzing and compa

MIND 478 Jan 01, 2023
The official implementation of CircleNet: Anchor-free Detection with Circle Representation, MICCAI 2030

CircleNet: Anchor-free Detection with Circle Representation The official implementation of CircleNet, MICCAI 2020 [PyTorch] [project page] [MICCAI pap

The Biomedical Data Representation and Learning Lab 45 Nov 18, 2022