Skin lesion segmentation with deep learning
Date Issued
2019-07-01
Author(s)
Lameski, Jane
Jovanov, Andrej
Abstract
Skin lesion segmentation is an important process
in skin diagnostics because it improves manual and computeraided diagnostics by focusing the medical personnel on specific
parts of the skin. Image segmentation is a common task in
computer vision that partitions a digital image into multiple
segments, for which deep neural networks have been proven
to be reliable. In this paper, we investigate the applicability of
deep learning methods for skin lesion segmentation evaluating
three architectures: a pre-trained VGG16 encoder combined with
SegNet decoder, TernausNet, and DeepLabV3+. The data set
consists of images with RGB skin lesions and the ground truth
of their segmentation. All the image sizes vary from hundreds to
thousands of pixels per dimension. We evaluated the approaches
with the Jaccard index and the computational efficiency of the
training. The results show that the three deep neural network
architectures achieve Jaccard Index scores of above 0.82, while
the DeeplabV3+ outperforms the other approaches with a score
of 0.876. The results are encouraging and can lead to fully-fledged
automated approaches for skin lesion segmentation.
in skin diagnostics because it improves manual and computeraided diagnostics by focusing the medical personnel on specific
parts of the skin. Image segmentation is a common task in
computer vision that partitions a digital image into multiple
segments, for which deep neural networks have been proven
to be reliable. In this paper, we investigate the applicability of
deep learning methods for skin lesion segmentation evaluating
three architectures: a pre-trained VGG16 encoder combined with
SegNet decoder, TernausNet, and DeepLabV3+. The data set
consists of images with RGB skin lesions and the ground truth
of their segmentation. All the image sizes vary from hundreds to
thousands of pixels per dimension. We evaluated the approaches
with the Jaccard index and the computational efficiency of the
training. The results show that the three deep neural network
architectures achieve Jaccard Index scores of above 0.82, while
the DeeplabV3+ outperforms the other approaches with a score
of 0.876. The results are encouraging and can lead to fully-fledged
automated approaches for skin lesion segmentation.
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