Interactive Segmentation of Glioblastoma for Post-surgical Treatment Follow-up
Ashis Kumar Dhara, Erik Arvids, Markus Fahlström, Johan Wikström, Elna‐Marie Larsson, Robin Strand · 2018
In this paper, we present a novel framework for interactive segmentation of glioblastoma in contrast-enhanced T1-weighted magnetic resonance images. U-net based-fully convolutional network is combined with an interactive refinement technique. Initial segmentation of brain tumor is performed using U-net, and the result is further improved by including complex foreground regions or removing background regions in an iterative manner. The method is evaluated on a research database containing post-operative glioblastoma of 15 patients. Radiologists can refine initial segmentation results in about 90 seconds, which is well below the time of interactive segmentation from scratch using state-of-the-art interactive segmentation tools. The experiments revealed that the segmentation results (Dice score) before and after the interaction step (performed by expert users) are similar. This is most likely due to the limited information in the contrast-enhanced T1-weighted magnetic resonance images used for evaluation. The proposed method is computationally fast and efficient, and could be useful for post-surgical treatment follow-up.