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CLCU-Net: Cross-level connected U-shaped network with selective feature aggregation attention module for brain tumor

Y L Wang¹, Z J Zhao¹, S Y Hu²

¹School of Control Science and Engineering, Shandong University, Jinan 250061, China.

Computer Methods and Programs in Biomedicine

|May 25, 2021

Summary

This study introduces a new deep learning network for precise brain tumor segmentation. The novel approach effectively utilizes multi-scale features, significantly improving diagnostic accuracy for clinicians.

Keywords:

Brain tumor segmentation Deep learning Multi-scale feature connection Segmented attention module Selective feature aggregation

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Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Brain tumors represent a critical global health challenge.
Deep convolutional neural networks have advanced brain tumor segmentation.
Existing methods often underutilize multi-scale features, limiting accuracy.

Purpose of the Study:

To develop an advanced deep learning network for enhanced brain tumor segmentation.
To improve the extraction and utilization of multi-scale features in segmentation tasks.
To provide clinicians with a more accurate tool for brain tumor diagnosis and surgical planning.

Main Methods:

Proposed a novel cross-level connected U-shaped network (CLCU-Net).
Introduced a Segmented Attention Module (SAM) for selective feature aggregation.
Employed deep supervision and spatial pyramid pooling (SSP) to optimize performance.

Main Results:

Achieved a Dice Score of 88.5% on the BRATS 2018 dataset for whole tumor segmentation.
Outperformed six state-of-the-art methods in segmentation accuracy.
Demonstrated superior suitability of the proposed attention module for segmentation tasks.

Conclusions:

The CLCU-Net with SAM effectively segments brain tumors.
The method shows significant potential for clinical practice implementation.
Accurate segmentation aids in improved patient diagnosis and treatment planning.