Among Brain Tumors, Gliomas Are The Most Common And Aggressive, Leading To A Very Short Life Expectancy In Their Highest Grade. Thus, Treatment Planning Is A Key Stage To Improve The Quality Of Life Of Oncological Patients. Magnetic Resonance Imaging (MRI) Is A Widely Used Imaging Technique To Assess These Tumors, But The Large Amount Of Data Produced By MRI Prevents Manual Segmentation In A Reasonable Time, Limiting The Use Of Precise Quantitative Measurements In The Clinical Practice. So, Automatic And Reliable Segmentation Methods Are Required; However, The Large Spatial And Structural Variability Among Brain Tumors Make Automatic Segmentation A Challenging Problem. In This Paper, We Propose An Automatic Segmentation Method Based On Convolutional Neural Networks (CNN), Exploring Small 3*3 Kernels. The Use Of Small Kernels Allows Designing A Deeper Architecture, Besides Having A Positive Effect Against Over Fitting, Given The Fewer Number Of Weights In The Network.

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