In this work, we deal with a brain tumor segmentation problem from magnetic resonance imaging (MRI), considered financially and time demanding when carrying out manually. To tackle this specific and complex domain problem, convolutional networks have proved competent due to significantly better performance than standard segmentation approaches. Therefore, within our research, we propose an approach which is dealing with tumor segmentation. During the elaboration, we propose multiple architectures, training phases and evaluation metrics in order to facilitate reliable and automatic delineation of tumorous tissues. For this purpose, we proposed a novel adaptation of the Tversky index loss formula to avoid label imbalance.
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Multi-modal brain tumor segmentation utilizing convolutional neural networks
Semantic Scholar · Computer Science · 2020
Abstract
In this work, we deal with a brain tumor segmentation problem from magnetic resonance imaging (MRI), considered financially and time demanding when carrying out manually. To tackle this specific and complex domain problem, convolutional networks have proved competent due to significantly better performance than standard segmentation approaches. Therefore, within our research, we propose an approach which is dealing with tumor segmentation. During the elaboration, we propose multiple architectures, training phases and evaluation metrics in order to facilitate reliable and automatic delineation of tumorous tissues. For this purpose, we proposed a novel adaptation of the Tversky index loss formula to avoid label imbalance.