MultiATTUNet: Brain Tumor Segmentation and Survival Multitasking

Author(s)

Carmo, Diedre ; Rittner, Leticia ; Lotufo, Roberto ; Crimi, A ; Bakas, S

• Document type: Journal article
• Source: BRAINLESION: GLIOMA, MULTIPLE SCLEROSIS, STROKE AND TRAUMATIC BRAIN INJURIES (BRAINLES 2020), PT I; v. 12658, p. 11-pg., 2021-01-01.

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. (AU)

• FAPESP's process: 13/07559-3 - BRAINN - The Brazilian Institute of Neuroscience and Neurotechnology
• Grantee: Fernando Cendes
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
• FAPESP's process: 19/21964-4 - Lung diseases and COVID diagnosis from computed tomography images using explainable deep learning
• Grantee: Diedre Santos do Carmo
• Support Opportunities: Scholarships in Brazil - Doctorate