Thesis Defense of Sylvain Guinebert
Sylvain Guinebert, a PhD candidate from the SIMO team, will defend his thesis on 2022-03-30 in the RT Amphitheater at the Vitry-sur-Seine campus of UPEC—120 rue Paul Armangot, 94400 Vitry-sur-Seine.
Title: Research and Development: AI-Assisted Interpretation of Spinal Pathologies
Thesis Supervisor(s): Yacine Amirat
Abstract:
Objective: To develop a tool for the automatic segmentation and identification of lumbar discopathies and fractures in MRI images using CNN networks.
Materials and Methods: We developed a PACS prototype with a DICOM viewer aimed at extracting training data and two CNN networks, one dedicated to segmentation and the other to the analysis of simple lumbar pathologies. A total of 204 MRIs were selected from the Pasteur 2 University Hospital in NICE. After segmenting and classifying all structures of interest, we trained two neural networks (U-Net++ and Yolov5x) to segment and detect discs and vertebrae.
Results: The neural networks provided semantic segmentations with high precision, achieving DICE scores of 0.96 and 0.93 for intervertebral discs and vertebral bodies, respectively. However, they were less effective in detecting common pathologies (degenerative disc diseases, disc herniations, vertebral fractures), with an area under the sensitivity-specificity curve of 0.85 for fractures and 0.76 for degenerative discopathies.
Conclusion: Our work demonstrated good efficiency in segmenting vertebral bodies and intervertebral discs, though improvements are still needed for detecting discal pathologies and vertebral fractures. We believe that the lower performance in these areas can be attributed to a lack of training images.
Keywords: artificial intelligence, AI, deep learning, magnetic resonance imaging, MRI, spinal pathologies.