DeepAI AI Chat
Log In Sign Up

MRI brain tumor segmentation and uncertainty estimation using 3D-UNet architectures

by   Laura Mora Ballestar, et al.

Automation of brain tumor segmentation in 3D magnetic resonance images (MRIs) is key to assess the diagnostic and treatment of the disease. In recent years, convolutional neural networks (CNNs) have shown improved results in the task. However, high memory consumption is still a problem in 3D-CNNs. Moreover, most methods do not include uncertainty information, which is especially critical in medical diagnosis. This work studies 3D encoder-decoder architectures trained with patch-based techniques to reduce memory consumption and decrease the effect of unbalanced data. The different trained models are then used to create an ensemble that leverages the properties of each model, thus increasing the performance. We also introduce voxel-wise uncertainty information, both epistemic and aleatoric using test-time dropout (TTD) and data-augmentation (TTA) respectively. In addition, a hybrid approach is proposed that helps increase the accuracy of the segmentation. The model and uncertainty estimation measurements proposed in this work have been used in the BraTS'20 Challenge for task 1 and 3 regarding tumor segmentation and uncertainty estimation.


Brain Tumor Segmentation using 3D-CNNs with Uncertainty Estimation

Automation of brain tumors in 3D magnetic resonance images (MRIs) is key...

Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation

Automatic brain tumor segmentation plays an important role for diagnosis...

Robust Semantic Segmentation of Brain Tumor Regions from 3D MRIs

Multimodal brain tumor segmentation challenge (BraTS) brings together re...

Demystifying Brain Tumour Segmentation Networks: Interpretability and Uncertainty Analysis

The accurate automatic segmentation of gliomas and its intra-tumoral str...

Multiple Instance Learning for Brain Tumor Detection from Magnetic Resonance Spectroscopy Data

We apply deep learning (DL) on Magnetic resonance spectroscopy (MRS) dat...