Attention U-Net with Dimension-Hybridized Fast Data Density Functional Theory for Automatic Brain Tumor Image Segmentation

Zi Jun Su, Tang Chen Chang, Yen Ling Tai, Shu Jung Chang, Chien Chang Chen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In the article, we proposed a hybridized method for brain tumor image segmentation by fusing topological heterogeneities of images and the attention mechanism in the neural networks. The three-dimensional image datasets were first pre-processed using the histogram normalization for the standardization of pixel intensities. Then the normalized images were parallel fed into the procedures of affine transformations and feature pre-extractions. The technique of fast data density functional theory (fDDFT) was adopted for the topological feature extractions. Under the framework of fDDFT, 3-dimensional topological features were extracted and then used for the 2-dimensional tumor image segmentation, then those 2-dimensional significant images are reconstructed back to the 3-dimensional intensity feature maps by utilizing physical perceptrons. The undesired image components would be filtered out in this procedure. Thus, at the pre-processing stage, the proposed framework provided dimension-hybridized intensity feature maps and image sets after the affine transformations simultaneously. Then the feature maps and the transformed images were concatenated and then became the inputs of the attention U-Net. By employing the concept of gate controlling of the data flow, the encoder can perform as a masked feature tracker to concatenate the features produced from the decoder. Under the proposed algorithmic scheme, we constructed a fast method of dimension-hybridized feature pre-extraction for the training procedure in the neural network. Thus, the model size as well as the computational complexity might be reduced safely by applying the proposed algorithm.

Original languageEnglish
Title of host publicationBrainlesion
Subtitle of host publicationGlioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Revised Selected Papers
EditorsAlessandro Crimi, Spyridon Bakas
PublisherSpringer Science and Business Media Deutschland GmbH
Pages81-92
Number of pages12
ISBN (Print)9783030720865
DOIs
StatePublished - 2021
Event6th International MICCAI Brainlesion Workshop, BrainLes 2020 Held in Conjunction with 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020 - Virtual, Online
Duration: 4 Oct 20204 Oct 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12659 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference6th International MICCAI Brainlesion Workshop, BrainLes 2020 Held in Conjunction with 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020
CityVirtual, Online
Period4/10/204/10/20

Keywords

  • Attention U-Net
  • Dimensional hybridization
  • fDDFT

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