Brain tumor segmentation is the task of segmenting tumors from other brain artefacts in MRI image of the brain.
( Image credit: Brain Tumor Segmentation with Deep Neural Networks )
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Automatic brain tumor segmentation method plays an extremely important role in the whole process of brain tumor diagnosis and treatment.
We propose a deep learning-based interactive segmentation method to improve the results obtained by an automatic CNN and to reduce user interactions during refinement for higher accuracy.
For segmentation, we utilize an existing brain parcellation atlas in the MNI152 1mm space and map this parcellation to each individual subject data.
The model cascade (MC) strategy significantly alleviates the class imbalance issue via running a set of individual deep models for coarse-to-fine segmentation.
The segmentation of brain tumors in multimodal MRIs is one of the most challenging tasks in medical image analysis.
Dice coefficients for enhancing tumor, tumor core, and the whole tumor are 0. 737, 0. 807 and 0. 894 respectively on the validation dataset.
Since the proposal of fully convolutional neural network (FCNN), it has been widely used in semantic segmentation because of its high accuracy of pixel-wise classification as well as high precision of localization.
In this paper, we explore various techniques to explain the functional organization of brain tumor segmentation models and to extract visualizations of internal concepts to understand how these networks achieve highly accurate tumor segmentations.
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.