Deep Neural Networks Comparison for MRI Segmentation of the Brainstem

Deep learning networks are the standard for medical image segmentation, yet the network architectures in medical applications are poorly understood. A precise segmentation of the brainstem is crucial in neurological conditions like Amyotrophic Lateral Sclerosis (ALS), which is a rare neurodegenerati...

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Bibliographic Details
Published in2024 IEEE International Symposium on Biomedical Imaging (ISBI) pp. 1 - 5
Main Authors Oh, Seoyoung, Pelegrini-Issac, Melanie, Urien, Helene, Marchand-Pauvert, Veronique, Sublime, Jeremie
Format Conference Proceeding
LanguageEnglish
Published IEEE 27.05.2024
Subjects
Biological system modeling
Brain modeling
Brainstem
Deep learning
Image segmentation
Magnetic resonance imaging
MRI
Neural Network Properties
Segmentation
Transfer learning
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Summary:Deep learning networks are the standard for medical image segmentation, yet the network architectures in medical applications are poorly understood. A precise segmentation of the brainstem is crucial in neurological conditions like Amyotrophic Lateral Sclerosis (ALS), which is a rare neurodegenerative disease affecting respiratory muscles by weakening motor neurons in the brain and spinal cord, but it is challenging due to the lack and low resolution of Magnetic Resonance Imaging (MRI) data. In this context, this paper explores neural network properties for brainstem segmentation and presents an efficient model with strong results. We find that minimal gains come from transfer learning in the encoder while optimizing the decoder and loss function improves performance. Our work also provides valuable insights into model components for MRI segmentation of the brainstem.
ISSN:1945-8452
DOI:10.1109/ISBI56570.2024.10635856