Brain tumor image segmentation based on Semantic Flow Guided Sampling and Attention Mechanism

SONG Jianli; LÜ Xiaoqi; GU Yu

doi:10.37188/OPE.20243204.0565

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Brain tumor image segmentation based on Semantic Flow Guided Sampling and Attention Mechanism

Information Sciences | 更新时间：2024-02-29

- Brain tumor image segmentation based on Semantic Flow Guided Sampling and Attention Mechanism
- Optics and Precision Engineering Vol. 32, Issue 4, Pages: 565-577(2024)
- 作者机构：
  
  1.内蒙古科技大学信息工程学院，内蒙古包头市 014010
  2.内蒙古工业大学信息工程学院，内蒙古呼和浩特 010051
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20243204.0565
  CLC： TP394.1;TH691.9
- Published：25 February 2024，
  
  Received：05 April 2023，
  
  Revised：09 May 2023，
- 稿件说明：
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宋建丽,吕晓琪,谷宇.语义流引导采样结合注意力机制的脑肿瘤图像分割[J].光学精密工程,2024,32(04):565-577.

SONG Jianli,LÜ Xiaoqi,GU Yu.Brain tumor image segmentation based on Semantic Flow Guided Sampling and Attention Mechanism[J].Optics and Precision Engineering,2024,32(04):565-577.
宋建丽,吕晓琪,谷宇.语义流引导采样结合注意力机制的脑肿瘤图像分割[J].光学精密工程,2024,32(04):565-577. DOI： 10.37188/OPE.20243204.0565.

SONG Jianli,LÜ Xiaoqi,GU Yu.Brain tumor image segmentation based on Semantic Flow Guided Sampling and Attention Mechanism[J].Optics and Precision Engineering,2024,32(04):565-577. DOI： 10.37188/OPE.20243204.0565.

摘要

U型网络结构的脑肿瘤自动分割方法由于多次卷积和采样操作会造成信息损失，导致分割效果不佳。为解决这一问题，提出了能够利用语义信息流引导上采样特征恢复的特征对齐单元，并在此基础上设计轻量级的双重注意力特征对齐网络（DAFANet）。首先，将特征对齐单元分别引入3D UNet、DMFNet和HDCNet三个经典网络，以验证其有效性和泛化性。其次，在DMFNet基础上构造轻量级的双重注意力特征对齐网络DAFANet，利用特征对齐单元强化上采样过程中的特征恢复，3D期望最大化注意力机制同时作用于特征对齐路径和级联路径，用于重点获取上下文的全程依赖关系。同时使用广义Dice损失函数提升数据不平衡时的分割精度并加快模型收敛。最后，在BraTS2018和BraTS2019公开数据集进行验证，文中所提算法在ET，WT和TC区域的分割精度分别达到80.44%，90.07%，84.57%和78.11%，90.10%，82.21%。相较于当前流行的分割网络，具有对增强肿瘤区域更好的分割效果，更擅长处理细节和边缘信息。

Abstract

The automatic segmentation method for brain tumors based on a U-shaped network structure often suffers from information loss due to multiple convolution and sampling operations， resulting in suboptimal segmentation results. To address this issue， this study proposed a feature alignment unit that utilizes semantic information flow to guide the up-sampling feature recovery and design designed a lightweight Dual Attention Feature Alignment Network （DAFANet） based on this unit.Firstly， to validate its effectiveness and generalization， the feature alignment unit was introduced separately into three classic networks， namely 3D UNet， DMFNet， and HDCNet. Secondly， a lightweight dual-attention feature alignment network named DAFANet was proposed based on DMFNet. The feature alignment unit enhanced feature restoration in the up-sampling process， and a 3D Expectation-Maximization attention mechanism was applied to both the feature alignment path and cascade path to capture the full contextual dependency. The generalized Dice loss function was also used to improve segmentation accuracy in the case of data imbalance and accelerate model convergence.Finally， the proposed algorithm is validated on the BraTS2018 and BraTS2019 public datasets， achieving segmentation accuracies of 80.44%， 90.07%， 84.57% and 78.11%， 90.10%， 82.21% in the ET， WT， and TC regions， respectively.Compared to current popular segmentation networks， the proposed algorithm demonstrates better segmentation performance in enhancing tumor regions and is more adept at handling details and edge information.

关键词

脑肿瘤图像分割特征对齐注意力机制轻量化

Keywords

brain tumorsimage segmentationfeature alignmentattention mechanismlightweight

references

BALWANT M K. A review on convolutional neural networks for brain tumor segmentation： methods， datasets， libraries， and future directions［J］. IRBM， 2022， 43（6）： 521-537. doi: 10.1016/j.irbm.2022.05.002http://dx.doi.org/10.1016/j.irbm.2022.05.002

LOUIS D N， PERRY A， WESSELING P， et al. The 2021 WHO Classification of Tumors of the Central Nervous System： a summary［J］. Neuro-oncology， 2021， 23（8）： 1231-1251. doi: 10.1093/neuonc/noab106http://dx.doi.org/10.1093/neuonc/noab106

MOHAMMED Y M A， GAROUANI SEL， JELLOULI I. A survey of methods for brain tumor segmentation-based MRI images［J］. Journal of Computational Design and Engineering， 2023， 10（1）： 266-293. doi: 10.1093/jcde/qwac141http://dx.doi.org/10.1093/jcde/qwac141

GU Y， CHI J Q， LIU J Q， et al. A survey of computer-aided diagnosis of lung nodules from CT scans using deep learning［J］. Computers in Biology and Medicine， 2021， 137： 104806. doi: 10.1016/j.compbiomed.2021.104806http://dx.doi.org/10.1016/j.compbiomed.2021.104806

LIU Z H， TONG L， CHEN L， et al. Deep learning based brain tumor segmentation： a survey［J］. Complex & Intelligent Systems， 2023， 9（1）： 1001-1026. doi: 10.1007/s40747-022-00815-5http://dx.doi.org/10.1007/s40747-022-00815-5

谷宇. 基于深度卷积神经网络的CT影像肺结节检测技术研究［D］. 上海：上海大学， 2019.

GU Y. Research on Lung Nodule Detection Based on Deep Convolutional Neural Network in Computed Tomography［D］.Shanghai： Shanghai University， 2019. （in Chinese）

徐胜军，张若暄，孟月波，等. 融合分形几何特征Resnet遥感图像建筑物分割［J］. 光学精密工程， 2022， 30（16）： 2006-2020. doi: 10.37188/ope.20223016.2006http://dx.doi.org/10.37188/ope.20223016.2006

XU S J， ZHANG R X， MENG Y B， et al. Fusion of fractal geometric features Resnet remote sensing image building segmentation［J］. Opt. Precision Eng.， 2022， 30（16）： 2006-2020.（in Chinese）. doi: 10.37188/ope.20223016.2006http://dx.doi.org/10.37188/ope.20223016.2006

RONNEBERGER O， FISCHER P， BROX T. U-Net： Convolutional Networks for Biomedical Image Segmentation［M］. Lecture Notes in Computer Science. Cham： Springer International Publishing， 2015： 234-241. doi: 10.1007/978-3-319-24574-4_28http://dx.doi.org/10.1007/978-3-319-24574-4_28

SHELHAMER E， LONG J， DARRELL T. Fully convolutional networks for semantic segmentation［C］. IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE， 2017： 640-651. doi: 10.1109/tpami.2016.2572683http://dx.doi.org/10.1109/tpami.2016.2572683

ÇIÇEK Ö， ABDULKADIR A， LIENKAMP S S， et al. 3D U-Net： Learning Dense Volumetric Segmentation from Sparse Annotation［M］. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016. Cham： Springer International Publishing， 2016： 424-432. doi: 10.1007/978-3-319-46723-8_49http://dx.doi.org/10.1007/978-3-319-46723-8_49

IBTEHAZ N， RAHMAN M S. MultiResUNet： Rethinking the U-Net architecture for multimodal biomedical image segmentation［J］. Neural Networks， 2020， 121： 74-87. doi: 10.1016/j.neunet.2019.08.025http://dx.doi.org/10.1016/j.neunet.2019.08.025

OKTAY O， SCHLEMPER J， LE FOLGOC L， et al. Attention U-Net： Learning Where to Look for the Pancreas［EB/OL］. 2018： arXiv： 1804.03999. http：//arxiv.org/abs/1804.03999.pdfhttp://arxiv.org/abs/1804.03999.pdf

WANG W， CHEN C， DING M， et al. Transbts： multimodal brain tumor segmentation using transformer［C］. Medical Image Computing and Computer Assisted Intervention-MICCAI 2021： 24th International Conference， Strasbourg， France， September 27-October 1， 2021， Proceedings， Part I 24. Springer International Publishing， 2021： 109-119. doi: 10.1007/978-3-030-87193-2_11http://dx.doi.org/10.1007/978-3-030-87193-2_11

JIANG Y， ZHANG Y， LIN X， et al. SwinBTS： a method for 3D multimodal brain tumor segmentation using swin transformer［J］. Brain Sciences， 2022， 12（6）： 797. doi: 10.3390/brainsci12060797http://dx.doi.org/10.3390/brainsci12060797

杨坚华，张浩，花海洋. 并行路径与强注意力机制遥感图像建筑物分割［J］. 光学精密工程， 2023， 31（2）： 234-245. doi: 10.37188/ope.20233102.0234http://dx.doi.org/10.37188/ope.20233102.0234

YANG J H， ZHANG H， HUA H Y. Parallel path and strong attention mechanism for building segmentation in remote sensing images［J］. Opt. Precision Eng.， 2023， 31（2）： 234-245.（in Chinese）. doi: 10.37188/ope.20233102.0234http://dx.doi.org/10.37188/ope.20233102.0234

梁礼明，周珑颂，冯骏，等. 基于高分辨率复合网络的皮肤病变分割［J］. 光学精密工程， 2022， 30（16）： 2021-2038. doi: 10.37188/ope.20223016.2021http://dx.doi.org/10.37188/ope.20223016.2021

LIANG L M， ZHOU L S， FENG J， et al. Skin lesion segmentation based on high-resolution composite network［J］. Opt. Precision Eng.， 2022， 30（16）： 2021-2038.（in Chinese）. doi: 10.37188/ope.20223016.2021http://dx.doi.org/10.37188/ope.20223016.2021

HUANG Z， ZHAO Y W， LIU Y H， et al. GCAUNet： a group cross-channel attention residual UNet for slice based brain tumor segmentation［J］. Biomedical Signal Processing and Control， 2021， 70： 102958. doi: 10.1016/j.bspc.2021.102958http://dx.doi.org/10.1016/j.bspc.2021.102958

ABOELENEIN N M， PIAO S H， KOUBAA A， et al. HTTU-net： hybrid two track U-net for automatic brain tumor segmentation［J］. IEEE Access， 2020， 8： 101406-101415. doi: 10.1109/access.2020.2998601http://dx.doi.org/10.1109/access.2020.2998601

LU X Q， GU Y， YANG L D， et al. Multi-level 3D densenets for false-positive reduction in lung nodule detection based on chest computed tomography［J］. Current Medical Imaging， 2020， 16（8）： 1004-1021. doi: 10.2174/1573405615666191113122840http://dx.doi.org/10.2174/1573405615666191113122840

CHEN C， LIU X P， DING M， et al. 3D Dilated Multi-Fiber Network for Real-Time Brain Tumor Segmentation in MRI［M］. Lecture Notes in Computer Science. Cham： Springer International Publishing， 2019： 184-192. doi: 10.1007/978-3-030-32248-9_21http://dx.doi.org/10.1007/978-3-030-32248-9_21

LUO Z R， JIA Z D， YUAN Z M， et al. HDC-net： hierarchical decoupled convolution network for brain tumor segmentation［J］. IEEE Journal of Biomedical and Health Informatics， 2021， 25（3）： 737-745. doi: 10.1109/jbhi.2020.2998146http://dx.doi.org/10.1109/jbhi.2020.2998146

ZHOU X Y， LI X Y， HU K， et al. ERV-Net： an efficient 3D residual neural network for brain tumor segmentation［J］. Expert Systems with Applications， 2021， 170： 114566. doi: 10.1016/j.eswa.2021.114566http://dx.doi.org/10.1016/j.eswa.2021.114566

LI X T， YOU A S， ZHU Z， et al. Semantic Flow for Fast and Accurate Scene Parsing［M］. Computer Vision – ECCV 2020. Cham： Springer International Publishing， 2020： 775-793. doi: 10.1007/978-3-030-58452-8_45http://dx.doi.org/10.1007/978-3-030-58452-8_45

LI X， ZHONG Z S， WU J L， et al. Expectation-maximization attention networks for semantic segmentation［C］. 2019 IEEE/CVF International Conference on Computer Vision （ICCV）. Seoul， Korea （South）. IEEE， 2019： 9166-9175. doi: 10.1109/iccv.2019.00926http://dx.doi.org/10.1109/iccv.2019.00926

NUECHTERLEIN N， MEHTA S. 3D-ESPNet with pyramidal refinement for volumetric brain tumor image segmentation［C］. Brainlesion： Glioma， Multiple Sclerosis， Stroke and Traumatic Brain Injuries： 4th International Workshop， Springer International Publishing， 2019： 245-253. doi: 10.1007/978-3-030-11726-9_22http://dx.doi.org/10.1007/978-3-030-11726-9_22

ZHANG J X， JIANG Z K， DONG J， et al. Attention gate ResU-net for automatic MRI brain tumor segmentation［J］. IEEE Access， 2020， 8： 58533-58545. doi: 10.1109/access.2020.2983075http://dx.doi.org/10.1109/access.2020.2983075

SHENG N， LIU D W， ZHANG J X， et al. Second-order ResU-Net for automatic MRI brain tumor segmentation［J］. Mathematical Biosciences and Engineering： MBE， 2021， 18（5）： 4943-4960. doi: 10.3934/mbe.2021251http://dx.doi.org/10.3934/mbe.2021251

AKBAR A S， FATICHAH C， SUCIATI N. Single level UNet3D with multipath residual attention block for brain tumor segmentation［J］. Journal of King Saud University-Computer and Information Sciences， 2022， 34（6）： 3247-3258. doi: 10.1016/j.jksuci.2022.03.022http://dx.doi.org/10.1016/j.jksuci.2022.03.022

LIU H X， HUO G Q， LI Q， et al. Multiscale lightweight 3D segmentation algorithm with attention mechanism： brain tumor image segmentation［J］. Expert Systems with Applications， 2023， 214： 119166. doi: 10.1016/j.eswa.2022.119166http://dx.doi.org/10.1016/j.eswa.2022.119166

CHANG Y K， ZHENG Z Z， SUN Y W， et al. DPAFNet： a residual dual-path attention-fusion convolutional neural network for multimodal brain tumor segmentation［J］. Biomedical Signal Processing and Control， 2023， 79： 104037. doi: 10.1016/j.bspc.2022.104037http://dx.doi.org/10.1016/j.bspc.2022.104037

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