Multi-scale 3D UNet: Multi-scale 3D UNet for Brain Tumor Segmentation

Parvez Ahmad, Saqib Qamar, Linlin Shen, Syed Qasim Afser Rizvi, Aamir Ali, Girija Chetty

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

Abstract

A deep convolutional neural network (CNN) achieves remarkable performance for medical image analysis. UNet is the primary source in the performance of 3D CNN architectures for medical imaging tasks, including brain tumor segmentation. The skip connection in the UNet architecture concatenates multi-scale features from image data. The multi-scaled features play an essential role in brain tumor segmentation. Researchers presented numerous multi-scale strategies that have been excellent for the segmentation task. This paper proposes a multi-scale strategy that can further improve the final segmentation accuracy. We propose three multi-scale strategies in MS UNet. Firstly, we utilize densely connected blocks in the encoder and decoder for multi-scale features. Next, the proposed residual-inception blocks extract local and global information by merging features of different kernel sizes. Lastly, we utilize the idea of deep supervision for multiple depths at the decoder. We validate the MS UNet on the BraTS 2021 validation dataset. The dice (DSC) scores of the whole tumor (WT), tumor core (TC), and enhancing tumor (ET) are 91.938 %, 86.268 %, and 82.409 %, respectively.

Original languageEnglish
Title of host publicationInternational MICCAI Brainlesion Workshop
Subtitle of host publicationGlioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021
EditorsAlessandro Crimi, Spyridon Bakas
PublisherSpringer
Pages30-41
Number of pages12
ISBN (Electronic)9783031090028
ISBN (Print)9783031090011
DOIs
Publication statusPublished - 15 Jul 2022
Event7th International Brain Lesion Workshop, BrainLes 2021, held in conjunction with the Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 - Virtual, Online
Duration: 27 Sep 202127 Sep 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12963 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th International Brain Lesion Workshop, BrainLes 2021, held in conjunction with the Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
CityVirtual, Online
Period27/09/2127/09/21

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