PhD Position in Machine Learning Université catholique de Louvain

Open PhD position in the "DeepContour" project (deep learning for automatic organ contouring of medical images in adaptive radiation therapy) at UCLouvain (Louvain-la-Neuve and Brussels, Belgium,www.uclouvain.be); 2+2 years. Description The DeepContour project takes place at the crossing of machine learning and cancer treatment with photon or proton therapy). To target the tumor and avoid organs at risk, radiation oncology heavily relies on medical images that must be annotated with the contours of these structures. This difficult task is still performed mainly manually by physicians. On the other hand, automatic recognition and delineation of organs in medical images is often turned into an image registration problem: a reference image, called atlas and manually segmented by an expert, is non-rigidly deformed to match the image to be segmented. Registration can however suffer from inaccuracies, especially when trying to match anatomies of different patients. DeepContour follows a different approach of the atlas, based on machine learning techniques. Reference images will be used to train classifiers (deep neural networks), which can afterwards process new images and label pixels or homogeneous groups of pixels, called superpixels. A previous proof-of-concept has demonstrated the validity of this approach and the project aims to improve the methodology by: * Defining new features to optimise prior segmentation into superpixels and therefore final, accurate delineation of organs (shape and texture features could be used in addition to gray level, distance and contiguity to organs). * Managing unexpected or unpredictable objects (like medical devices, tumours, etc.), particularly difficult to deal with in image registration. * Transpose from natural scenes to medical images an architecture that combines deep neural networks and superpixels. * Make the methodology interactive (ask the user whenever the risk of error is significant) and adaptive (learn from new images segmented by the atlas and approved by physicians). The project also includes the collection of reference data with a clinical partner and the comparison with registration-based atlases. The ideal PhD student should have a master degree in engineering, computer science, or applied mathematics. S/he should have a good background in mathematics, statistics, and programming (Python, Matlab), completed with scientific curiosity and good communication skills. S/he will work in a multidisciplinary and dynamic environment (engineers, mathematicians, physicists, physicians), sharing its time between the ICTEAM Institute (Information and Communication Technologies, Electronics, and Applied Mathematics, Louvain-la-Neuve) and the IREC (Institut de Recherche Expérimentale et Clinique, Brussels). S/he should speak at least English or French and be willing to learn the other language. The contract is a 2+2 scholarship. The candidate should be eligible for a Belgian PhD scholarship (EU citizen and holder of a Master degree). If you apply for this position please say you saw it on Computeroxy

Open PhD position in the "DeepContour" project (deep learning for automatic organ contouring of medical images in adaptive radiation therapy) at UCLouvain (Louvain-la-Neuve and Brussels, Belgium,www.uclouvain.be); 2+2 years.

Description

The DeepContour project takes place at the crossing of machine learning and cancer treatment with photon or proton therapy).
To target the tumor and avoid organs at risk, radiation oncology heavily relies on medical images that must be annotated with the contours of these structures.

This difficult task is still performed mainly manually by physicians.

On the other hand, automatic recognition and delineation of organs in medical images is often turned into an image registration problem: a reference image, called atlas and manually segmented by an expert, is non-rigidly deformed to match the image to be segmented.

Registration can however suffer from inaccuracies, especially when trying to match anatomies of different patients.

DeepContour follows a different approach of the atlas, based on machine learning techniques.

Reference images will be used to train classifiers (deep neural networks), which can afterwards process new images and label pixels or homogeneous groups of pixels, called superpixels.

A previous proof-of-concept has demonstrated the validity of this approach and the project aims to improve the methodology by:

* Defining new features to optimise prior segmentation into superpixels and therefore final, accurate delineation of organs (shape and texture features could be used in addition to gray level, distance and contiguity to organs).

* Managing unexpected or unpredictable objects (like medical devices, tumours, etc.), particularly difficult to deal with in image registration.

* Transpose from natural scenes to medical images an architecture that combines deep neural networks and superpixels.

* Make the methodology interactive (ask the user whenever the risk of error is significant) and adaptive (learn from new images segmented by the atlas and approved by physicians).

The project also includes the collection of reference data with a clinical partner and the comparison with registration-based atlases.

The ideal PhD student should have a master degree in engineering, computer science, or applied mathematics.
S/he should have a good background in mathematics, statistics, and programming (Python, Matlab), completed with scientific curiosity and good communication skills.
S/he will work in a multidisciplinary and dynamic environment (engineers, mathematicians, physicists, physicians), sharing its time between the ICTEAM Institute (Information and Communication Technologies, Electronics, and Applied Mathematics, Louvain-la-Neuve) and the IREC (Institut de Recherche Expérimentale et Clinique, Brussels).
S/he should speak at least English or French and be willing to learn the other language.

The contract is a 2+2 scholarship.
The candidate should be eligible for a Belgian PhD scholarship (EU citizen and holder of a Master degree).

If you apply for this position please say you saw it on Computeroxy