News


Postdoc position available

posted Nov 29, 2017, 7:41 AM by Juan Eugenio Iglesias   [ updated Nov 29, 2017, 7:50 AM ]

We are looking for a postdoc to work on my ERC project "Building Next-Generation Computational Tools for High Resolution Neuroimaging Studies" at the Translational Imaging Group (University College London). If you are interested, please apply through the link at the bottom of this page.
Further details on the position can be found here below.



Two year postdoctoral position in medical image analysis (Machine Learning for 3D Histology)
Translational Imaging Group, University College London, United Kingdom.

The Translational Imaging Group (TIG) at University College London (UCL) is seeking to fill a postdoctoral position in medical image analysis within an exciting ERC Starting Grant project: "Building Next-Generation Computational Tools for High Resolution Neuroimaging Studies", awarded to Dr. J. Eugenio Iglesias. The project ultimately aims for creating an ultra-high resolution probabilistic atlas of the whole human brain using ex vivo data, as well as companion image analysis tools that apply the atlas to the automated segmentation of in vivo MRI scans.

We are seeking a talented and enthusiastic post-doctoral research associate (PDRA) to develop image analysis methods for the 3D reconstruction of the histological data in this exciting project. The PDRA will combine synthesis and segmentation techniques (Bayesian and/or deep learning based, e.g., U-Net, CycleGAN) with registration methods developed at the Translational Imaging Group. These techniques will be used to recover the 3D structure of histological samples from human brain specimens provided by the UCL Brain Bank. The resulting reconstructed data will be used to build next-generation brain atlases in later stages of the ERC project. 

This project provides an exciting opportunity for a PDRA at the interface between machine learning, medical image analysis and neuroscience, with a huge potential impact on the neuroimaging field.

The post is funded for 2 years in the first instance.

Applicants should have:

- A Ph.D. degree in computer vision, neuroimaging, or biomedical image analysis. Applicants with degrees in related areas (e.g., Engineering, Physics, Applied Mathematics) will also be considered. Applicants close to submission of their PhD thesis will be considered as well. 
- Strong mathematical and problem solving abilities.
- Expertise in programming, at least with high-level languages / packages.
- An established publication track record.
- Ideally, experience with one/some of the following: neuroimaging, deep learning, analysis of histology data, image registration.

The postdoctoral researcher would be hired at grade 7 of the UCL salary scale. The deadline for applications is December 29th. The duration of the contract would be two years, with possibility of further extension.

You can apply for the job here:



PNAS paper

posted Nov 15, 2017, 5:55 AM by Juan Eugenio Iglesias   [ updated Nov 15, 2017, 5:58 AM ]

 


It is my pleasure to announce that our paper "Sparsity enables estimation of both subcortical and cortical activity from MEG and EEG" (first author: Pavitra Krishnaswamy) has been published at PNAS. 

Localizing the sources of electromagnetic fields measured with magnetoencephalogray is very difficult, because the fields generated by deep brain structures are very weak. This paper shows that cortical and subcortical fields can be distinguished if the cortical sources are sparse.

Thanks, Pavitra (et al.) for the hard work!

Atlas of nuclei of the amygdala

posted Nov 14, 2017, 5:08 AM by Juan Eugenio Iglesias

 



Our atlas of the nuclei of the amygdala and its associated segmentation code are finally available in the development version of FreeSurfer. 

You can read about the atlas here.

The code can be found here.



Postdoc position available

posted Aug 31, 2017, 3:47 AM by Juan Eugenio Iglesias

We are looking for a postdoc to work on my ERC project "Building Next-Generation Computational Tools for High Resolution Neuroimaging Studies" at the Translational Imaging Group (University College London). If you are interested, please apply through the link at the bottom of this page.
Further details on the position can be found here below.



Two year postdoctoral position in medical image analysis
Translational Imaging Group, University College London, United Kingdom.

The Translational Imaging Group (TIG) at University College London (UCL) is seeking to fill a postdoctoral position in medical image analysis within an exciting EU / ERC project. 

The postdoctoral researcher will be involved in the development of algorithms to recover the 3D structure of histological slices. The researcher will work in close collaboration with researchers at the UCL Brain Bank, who are carrying out the histological processing of the (brain) specimens.

These tasks are part of an EU/ERC project with the title "Building Next-Generation Computational Tools for High Resolution Neuroimaging Studies", awarded to Dr. J. Eugenio Iglesias. The project ultimately aims for creating an ultra-high resolution probabilistic atlas of the whole human brain using ex vivo data, as well as companion image analysis tools that apply the atlas to the automated segmentation of in vivo MRI scans. 

Applicant should have:

- A Ph.D. degree in (bio)medical image analysis or a related area (e.g., computer vision). 
- Strong mathematical and problem solving abilities.
- Strong programming skills.
- An established publication track record.
- Ideally, experience with analysis of histology data and / or image registration.

The postdoctoral researcher would be hired at grade 7 of the UCL salary scale (http://www.ucl.ac.uk/hr/salary_scales/final_grades.php). We are looking for fill this position as soon as possible, but there is some flexibility with the dates. The duration of the contract would be two years, with possibility of further extension.

You can apply for the job here:


Workshop paper on part-to-whole histology registration

posted Aug 25, 2017, 11:06 AM by Juan Eugenio Iglesias   [ updated Aug 25, 2017, 11:07 AM ]

 

Our paper "Part-to-whole Registration of Histology and MRI using Shape Elements" has been accepted for publication at the Bioimage Computing Workshop, part of ICCV 2017. The paper addresses the problem of registering histology and MRI, when specimens that cannot be processed all at once must be cut into smaller blocks. This greatly complicates the problem, since each block requires manual pre-alignment. In this paper, we present a fully automated method to solve this problem, based on a level-line representation of the images, which is robust to contrast changes. We extract bitangents to obtain shape elements which, encoded in a projective-invariant manner, enable the identification of common pieces in both modalities.

You can find the paper under Publications.

Thanks to Jonas, Sotiris, Tarek, Seb and Marc for the hard work. 



Workshop paper on template-free estimation of intracranial volume

posted Jul 28, 2017, 1:44 AM by Juan Eugenio Iglesias



Our paper "Template-free estimation of intracranial volume: A preterm birth animal model study" has been accepted for publication at the MICCAI 2017 workshop on Fetal and Infant Image analysis. We introduce an groupwise ICV estimation framework that is robust against registration errors and that does not require a template. This latter feature enables application of the method to brain MR scans of animal species for which labeled data or templates might be inexistent or scarce, e.g., preterm born animal models. We also show that the proposed method can yield reduced sample sizes and increased accuracy in regression and discrimination experiments on human brain MRI, compared with using a reference atlas. You can find the paper under Publications.


Caffe model for MICCAI paper

posted Jul 5, 2017, 3:12 AM by Juan Eugenio Iglesias   [ updated Jul 5, 2017, 3:18 AM ]

We have uploaded the Caffe model files for the 3D U-net we used in our recently accepted MICCAI paper:

Iglesias JE, Lerma-Usabiaga G, Garcia-Peraza-Herrera L, Martinez S, Paz-Alonso PM: "Retrospective head motion estimation in structural brain MRI with 3D CNNs", MICCAI 2017, accepted.

You can find the network model files under [Software -> MOTION ESTIMATION], and the paper under Publications.

IPMI 2017 best poster award

posted Jul 3, 2017, 5:16 AM by Juan Eugenio Iglesias   [ updated Jul 28, 2017, 1:33 AM ]

It is my pleasure to share that my work on "Globally optimal coupled surfaces for semi-automatic segmentation of medical images" has won the Best Poster Award at IPMI 2017, shared with Adrian Dalca et al's "Population Based Image Imputation". It is twice the pleasure to share the award when you are friends with the authors of the other paper; congrats, Adrian, Mert, Polina, et al, and thanks again, Dr. Leo Grady for your useful feedback, which greatly improved my paper. 

You can find my article under Publications, and you can download Adrian's from [here].

You can read a bit more about the award on the TIG website.





MICCAI paper on retrospective head motion estimation

posted May 16, 2017, 7:26 AM by Juan Eugenio Iglesias   [ updated May 16, 2017, 2:29 PM ]

 
 
Our paper "Retrospective head motion estimation in structural brain MRI with 3D CNNs" has been accepted at MICCAI 2017. 

In this study, we propose a supervised method to retrospectively detect whether voxels of a brain MRI scan show signs of being corrupted by motion or not. We can average the probability of motion across the voxels in a region of interest (e.g., the cerebral cortex) and analyze its impact on morphometric features (e.g., cortical thickness). We show that, when we factor in our estimate of motion in group studies, the conclusions of the analyses can be very different, particularly when one group is more prone to moving in the scanner than the other - in the paper, we illustrate this with a public, autism MRI dataset (ABIDE).

You can find a preprint of the manuscript under Publications.

Thanks Gari, Luis, Sara and Kepa for the hard work!

Computational atlas of the human amygdala and its nuclei

posted Apr 21, 2017, 1:52 PM by Juan Eugenio Iglesias   [ updated Aug 25, 2017, 10:51 AM ]

 
Our paper "High-resolution magnetic resonance imaging reveals nuclei of the human amygdala: manual segmentation to automatic atlas" has been accepted for publication in NeuroImage. We used ultra-high resolution ex vivo MRI data to manually label the nuclei of the amygdala, and then used these manual segmentations to build a computational atlas of the nuclei - and surrounding structures. This atlas can be used to automatically segment the nuclei from in vivo brain MRI scans.  The atlas and associated segmentation code will be made publicly available as part of future versions of FreeSurfer. Thanks Zeynep, Dorit, Jean and the rest of coauthors, for the amazing work.

A preprint of the paper can be found here.

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