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Evolutionary Deep Attention Convolutional Neural Networks for 2D and 3D Medical Image Segmentation.

Tahereh Hassanzadeh1, Daryl Essam2, Ruhul Sarker2

  • 1University of New South Wales, Canberra, Australia. t.hassanzadehkoohi@student.unsw.edu.au.

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|November 3, 2021
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Summary
This summary is machine-generated.

This study introduces an automated method using neuroevolution to create deep attention convolutional neural networks for medical image segmentation. The novel approach efficiently enhances segmentation accuracy for both 2D and 3D images, outperforming existing models.

Keywords:
3D Medical Image SegmentationAttention NetworkDeep Convolutional Neural NetworkGenetic AlgorithmNeuroevolution

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Neuroscience

Background:

  • Medical image segmentation using deep convolutional neural networks (CNNs) faces challenges due to limited labeled data and computational constraints.
  • Incorporating attention mechanisms in CNNs is crucial for improving segmentation accuracy despite image noise and diversity, but designing such complex networks is difficult.
  • Neuroevolution offers automated network design but is computationally intensive, particularly for 3D network architectures.

Purpose of the Study:

  • To develop an automatic, efficient, accurate, and robust technique for creating deep attention CNNs for 2D and 3D medical image segmentation.
  • To address the computational expense of neuroevolution for complex network structures and 3D applications.
  • To optimize the integration of attention modules within a U-Net-based architecture for enhanced spatial information recovery.

Main Methods:

  • An automated technique utilizing neuroevolution to design deep attention CNNs for medical image segmentation.
  • The evolutionary approach identifies optimal combinations of six attention modules to improve spatial information transfer in a U-Net architecture.
  • Evaluation performed on six diverse CT and MRI datasets for both 2D and 3D image segmentation tasks.

Main Results:

  • The proposed neuroevolutionary technique successfully developed deep attention CNNs that significantly improved medical image segmentation.
  • The model demonstrated superior performance in recovering and transferring spatial information between the downsampling and upsampling paths of the U-Net network.
  • Comparative analysis showed the proposed model outperformed all state-of-the-art manual and automatically designed segmentation models across all tested datasets.

Conclusions:

  • The presented neuroevolutionary approach provides an efficient and effective method for automatically designing high-performance deep attention CNNs for medical image segmentation.
  • This technique overcomes the limitations of manual design and computationally expensive neuroevolution for complex 3D networks.
  • The model's ability to outperform existing methods highlights its potential for advancing medical image analysis and segmentation accuracy.