Ensembled ResUnet for Anatomical Brain Barriers Segmentation
This paper addresses the problem of accurate brain structure segmentation for medical professionals involved in glioma and radiotherapy planning, offering an incremental improvement.
This paper addresses the challenge of accurate segmentation of brain structures for glioma and radiotherapy planning by developing an ensembled ResUnet. The method aims to improve segmentation accuracy despite visual and anatomical differences across modalities.
Accuracy segmentation of brain structures could be helpful for glioma and radiotherapy planning. However, due to the visual and anatomical differences between different modalities, the accurate segmentation of brain structures becomes challenging. To address this problem, we first construct a residual block based U-shape network with a deep encoder and shallow decoder, which can trade off the framework performance and efficiency. Then, we introduce the Tversky loss to address the issue of the class imbalance between different foreground and the background classes. Finally, a model ensemble strategy is utilized to remove outliers and further boost performance.