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MIALSRTK: Super-resolution reconstruction of fetal MRI | Multi-channel MRI segmentation of eye structures and tumors | MBIS: Multivariate Bayesian Image Segmentation tool | fP-CMC: Fast Patch-based Continuous Min-Cut segmentation

MIALSRTK: Super-resolution reconstruction of fetal MRI


Copyright © 2016-2017 Medical Image Analysis Laboratory, University Hospital Center and University of Lausanne (UNIL-CHUV), Switzerland

This software is distributed under the open-source BSD 3-Clause License. See LICENSE file for details.

The Medical Image Analysis Laboratory Super-Resolution ToolKit (MIALSRTK) consists of a set of C++ image processing tools necessary to perform motion-robust super-resolution fetal MRI reconstruction. This toolkit, supported by the Swiss National Science Foundation (grant SNSF-141283), includes all algorithms and methods for brain extraction [1], intensity standardization [1,2], motion estimation and super-resolution [2] developed during my PhD. It uses the CMake build system and depends on the open-source image processing Insight ToolKit (ITK) library, the command line parser TCLAP library and OpenMP for multi-threading. The USAGE message of each tool can be obained using either the -h or --help flag.

A Docker image is provided to facilitate the deployment and freely available @ Docker store.

The MIALSRTK can be downloaded here.

Please acknowledge this software in any work reporting results using MIALSRTK by citing the following articles:

[1] S. Tourbier, C. Velasco-Annis, V. Taimouri, P. Hagmann, R. Meuli, S. K. Warfield, M. Bach Cuadra, A. Gholipour, "Automated template-based brain localization and extraction for fetal brain MRI reconstruction", Neuroimage (2017) In Press. DOI

[2] S. Tourbier, X. Bresson, P. Hagmann, R. Meuli, M. Bach Cuadra, "An efficient total variation algorithm for super-resolution in fetal brain MRI with adaptive regularization", Neuroimage 118 (2015) 584-597. DOI



Multi-channel MRI segmentation of eye structures and tumors

An open source software for computing the automatic segmentation of eye structures and tumors in 3D Magnetic Resonance Imaging.

The paper is available inhttp://journals.plos.org/plosone/article?id=10.1371/journal.pone.0173900

The software is available inhttps://doi.org/10.5281/zenodo.400920

The testing data set is available inhttps://osf.io/5t3nq/

Works using this software should cite:



MBIS: Multivariate Bayesian Image Segmentation tool

An open source multivariate statistical n-classes clustering tool including graph-cuts optimization with specialization to brain Magnetic Resonance imaging.


Works using MBIS should cite:

Oscar Esteban, Gert Wollny, Sai Subrahmanyam Gorthi, María J. Ledesma-Carbayo, Jean-Philippe Thiran, Andrés Santos, Meritxell Bach Cuadra, MBIS: Multivariate Bayesian Image Segmentation tool, Comp Meth Prog Biomed 115(2):76–94, 2014.

DOI: 10.1016/j.cmpb.2014.03.003


You can download the code at https://github.com/oesteban/MBIS.




fP-CMC: Fast Patch-based Continuous Min-Cut segmentation

This software presents a semi-supervised segmentation framework for B-mode ultrasound imaging. It is applicable to any kind of target and imaging settings. Our methodology uses a graph of image patches to represent the ultrasound image and user-assisted initialization with labels, which acts as soft priors. The segmentation problem is formulated as a continuous minimum cut problem and solved with an efficient optimization algorithm.


Works using fP-CMC should cite:

Anca Ciurte, Xavier Bresson, Olivier Cuisenaire, Nawal Houhou, Sergiu Nedevschi, Jean-Philippe Thiran, Meritxell Bach Cuadra, Semi-Supervised Segmentation of Ultrasound Images based on Patch Representation and Continuous Min Cut. Plos One, Volume 9, Number 7, July 2014. DOI: 10.1371/journal.pone.0100972.


To download the code please click here.




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