Volumetric multimodality neural network for brain tumor segmentation
Silvana Castillo L, Alexandra Daza L, Rivera LC, Arbelaez P (2017)
Publication Type: Conference contribution
Publication year: 2017
Journal
Publisher: SPIE
Book Volume: 10572
Conference Proceedings Title: Proceedings of SPIE - The International Society for Optical Engineering
Event location: San Andres Island
ISBN: 9781510616332
DOI: 10.1117/12.2285942
Abstract
Brain lesion segmentation is one of the hardest tasks to be solved in computer vision with an emphasis on the medical field. We present a convolutional neural network that produces a semantic segmentation of brain tumors, capable of processing volumetric data along with information from multiple MRI modalities at the same time. This results in the ability to learn from small training datasets and highly imbalanced data. Our method is based on DeepMedic, the state of the art in brain lesion segmentation. We develop a new architecture with more convolutional layers, organized in three parallel pathways with different input resolution, and additional fully connected layers. We tested our method over the 2015 BraTS Challenge dataset, reaching an average dice coefficient of 84%, while the standard DeepMedic implementation reached 74%.
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APA:
Silvana Castillo, L., Alexandra Daza, L., Rivera, L.C., & Arbelaez, P. (2017). Volumetric multimodality neural network for brain tumor segmentation. In Natasha Lepore, Jorge Brieva, Juan David Garcia, Eduardo Romero (Eds.), Proceedings of SPIE - The International Society for Optical Engineering. San Andres Island, CO: SPIE.
MLA:
Silvana Castillo, Laura, et al. "Volumetric multimodality neural network for brain tumor segmentation." Proceedings of the 13th International Conference on Medical Information Processing and Analysis, SIPAIM 2017, San Andres Island Ed. Natasha Lepore, Jorge Brieva, Juan David Garcia, Eduardo Romero, SPIE, 2017.
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