[PhD thesis] Deep learning based segmentation of thin structures
Team: Magrit (https://magrit.loria.fr/index.en.html)
This doctoral work aims at designing deep learning-based image segmentation methods adapted to thin objects, in a medical context. Though highly efficient to segment objects from images in general, deep learning approaches have difficulties with thin structures, i.e. when at least one of the object dimensions is negligible. Moreover, annotated data are often scarce in a medical context. Limitations of deep learning methods against such conditions will be carried out and strategies will be proposed to raise them. This doctoral work will be applied to the segmentation of the diaphragm and intracranial blood vessels for validation.
Objectives and scientific context
Thin structures are objects with at least one very small dimension compared to the others. They are hardly preserved in a multiscale context, and deep convolutional neural networks (CNNs) thus have difficulties to accurately segment them in images [1,2,3]. However, variational methods were developed to precisely better preserve such structures. Their potential to improve the accuracy of CNNs has recentely been demonstrated .
The main objective of this doctoral work is to develop methods for the segmentation of thin structures, that both leverage the high performances of CNNs, and the geometric accuracy of variational methods so as to improve the segmentation of thin structures. Various levels of coupling will be investigated: post-processing, tailoring the last layers, down to designing new pooling layers. The integration or not of such operations in the training phase will also be adressed.
This work will be applied in a medical context to the segmentation from 3D images of the diaphragm (thin organ, attached to the lungs, 1 tiny dimension) and intracerebral blood vessels (2 tiny dimensions). PDE methods have been considered for diaphragm segmentation , but this problem was not addressed by deep learning approaches. Similarly, variational methods have inspired numerous works in vascular segmentation [6, 7, 8, 9]. Here, deep learning methods were also investigated but only in 2D (retinal images, X-ray angiography) .
Annotated data are difficult to collect in a medical context. A second objective will investigate patch-based approaches so as to demultiply the training data and enable segmentation in 3D.
The candidate will have the opportunity to rely on recent works in Magrit team [11, 4], including a Masters’ internship about diaphragm segmentation. 3 goals will be pursued:
- Critical review of the literature on prior works to improve the geometric accuracy of segmentation using deep learning methods [1, 2, 3].
- Coupling a CNN with a variational approach to preserve thin structures.
- Validation on medical images (3D rotational angiography for blood vessels, and abdominal CT for diaphragm). Our long-term collaboration with the Interventional Neuroradiology department of the University Hospital in Nancy  gives us priviledged access to medical data.
The applicant mush hold a Master of Science (or equiv.) in Applied Maths or Computer Science.
We are looking for a highly motivated student, with a solid background in mathematics (PDE), and/or computer science (computer vision). Strong knowledge in machine learning, as well as high skills within deep learning environments such as Keras, tensorflow and/or PyTorch, and proficiency in Python programming in general. Interest in multidisciplinary research and medical applications will be appreciated.
How to apply
Starting date: 10/01/2020 (3 years)
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