Multi-modal brain tumor segmentation utilizing convolutional neural networks

Marek Jakab; Marek Stevuliak; Wanda Benesova

doi:10.1117/12.2557599

31 January 2020 Multi-modal brain tumor segmentation utilizing convolutional neural networks

Marek Jakab, Marek Stevuliak, Wanda Benesova

Author Affiliations +

Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 1143318 (2020) https://doi.org/10.1117/12.2557599
Event: Twelfth International Conference on Machine Vision, 2019, Amsterdam, Netherlands

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.

Citation Download Citation

Marek Jakab, Marek Stevuliak, and Wanda Benesova "Multi-modal brain tumor segmentation utilizing convolutional neural networks", Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 1143318 (31 January 2020); https://doi.org/10.1117/12.2557599

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