MRI brain tumor segmentation and uncertainty estimation using 3D-UNet architectures
Visualitza/Obre
10.1007/978-3-030-72084-1_34
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/360072
Tipus de documentCapítol de llibre
Data publicació2021
EditorSpringer Nature
Condicions d'accésAccés obert
Tots els drets reservats. Aquesta obra està protegida pels drets de propietat intel·lectual i
industrial corresponents. Sense perjudici de les exempcions legals existents, queda prohibida la seva
reproducció, distribució, comunicació pública o transformació sense l'autorització del titular dels drets
Abstract
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.
CitacióMora, L.; Vilaplana, V. MRI brain tumor segmentation and uncertainty estimation using 3D-UNet architectures. A: "Brainlesion: Glioma, multiple sclerosis, stroke and traumatic brain injuries: 6th International Workshop, BrainLes 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4, 2020: revised selected papers, part I". Springer Nature, 2021, p. 376-390.
ISBN978-3-030-72084-1
Versió de l'editorhttps://link.springer.com/book/10.1007/978-3-030-72084-1
Fitxers | Descripció | Mida | Format | Visualitza |
---|---|---|---|---|
BratsExtendedPaper.pdf | 1,259Mb | Visualitza/Obre |