Deep neural network for brain tumor segmentation in magnetic resonance imaging
keywords DEEP NEURAL NETWORKS, MEDICAL IMAGING
Reference persons LIA MORRA
Research Groups DAUIN - GR-09 - GRAphics and INtelligent Systems - GRAINS
Description Brain tumor segmentation from neuroimaging modalities is a critical step towards improving disease diagnosis, treatment planning and post-treatment monitoring. Segmentation methods are mainly grouped into three categories: manual, semi-automatic and fully automatic. As manual segmentation of brain tumors is time-consuming and subject to rater variability, the interest for automatic and semi-automatic segmentation of brain tumors has received a great impulse over the last decades. While many efforts have been done to improve the segmentation of the pre-operative scans, artificial intelligence has not obtained comparable results for post-operative images.
The aim of this study is to make a further step in this field by the application of deep learning technologies to post-operative neuroimaging. The main focus is the creation of a reliable algorithm for the semi-automatic analysis of MR images, including both pre-operative and post-operative scans obtained with standardized technique and timing. This achievement would have relevant implications for the post-operative management of patients and for the possible prediction of recurrent disease features, including timing and site.
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2. Eijgelaar, R. S. et al. Robust Deep Learning–based Segmentation of Glioblastoma on Routine Clinical MRI Scans Using Sparsified Training. https://pubs.rsna.org/doi/pdf/10.1148/ryai.2020190103
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4. Ermiş, E. et al. Fully automated brain resection cavity delineation for radiation target volume definition in glioblastoma patients using deep learning. https://ro-journal.biomedcentral.com/track/pdf/10.1186/s13014-020-01553-z.pdf
Required skills deep learning, machine learning, python
Deadline 25/11/2023 PROPONI LA TUA CANDIDATURA