MultiATTUNet: Brain Tumor Segmentation and Survival Multitasking

Diedre Carmo, Leticia Rittner and Roberto Lotufo,

Abstract:

Segmentation of Glioma from three dimensional magnetic
resonance imaging (MRI) is useful for diagnosis and surgical treatment of
patients with brain tumor. Manual segmentation is expensive, requiring
medical specialists. In the recent years, the Brain Tumor Segmentation
Challenge (BraTS) has been calling researchers to submit automated
glioma segmentation and survival prediction methods for evaluation and
discussion over their public, multimodality MRI dataset, with manual
annotations. This work presents an exploration of different solutions to
the problem, using 3D UNets and self attention for multitasking both
predictions and also training (2D) EfficientDet derived segmentations,
with the best results submitted for the official challenge leaderboard. We
show that end-to-end multitasking survival and segmentation, in this
case, led to better results.