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Skin lesion segmentation using high-resolution convolutional neural network.

Fengying Xie¹, Jiawen Yang¹, Jie Liu²

¹Image Processing Center, School of Astronautics, Beihang University, Beijing 100191, China; Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing 100191, China.

Computer Methods and Programs in Biomedicine

|December 15, 2019

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This study introduces an attention-based convolutional neural network for precise skin lesion segmentation in dermoscopy images. The novel method effectively preserves boundary details, outperforming existing techniques for computer-aided diagnosis.

Area of Science:

Dermatology
Medical Imaging
Computer Vision

Background:

Skin lesion segmentation is crucial for computer-aided diagnosis but challenging due to loss of spatial detail in standard CNNs.
Existing methods often struggle to accurately delineate lesion boundaries, impacting diagnostic accuracy.

Purpose of the Study:

To develop an advanced skin lesion segmentation method for dermoscopy images.
To improve the accuracy of boundary extraction in skin lesion segmentation using deep learning.

Main Methods:

A novel convolutional neural network architecture incorporating a high-resolution feature block with spatial and channel-wise attention mechanisms was designed.
The network utilizes three branches to extract and fuse detailed spatial information, enhancing feature discriminability.

Keywords:

Attention mechanism Convolutional neural network High-resolution feature Skin lesion segmentation

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Main Results:

The proposed method achieved high Jaccard indices (0.783-0.858) on multiple benchmark datasets.
It demonstrated robustness against image artifacts like hair fibers and outperformed established segmentation networks (FCN-8s, U-Net).

Conclusions:

The attention-enhanced network effectively preserves spatial details and enhances relevant features for high-performance skin lesion segmentation.
This approach offers a significant advancement in accurate and robust automated skin lesion analysis.