Sage Journals: Discover world-class research

Abstract

In recent years, the incidence of skin diseases has increased significantly, and some malignant tumors caused by skin diseases have brought great hidden dangers to people’s health. In order to help experts perform lesion measurement and auxiliary diagnosis, automatic segmentation methods are very needed in clinical practice. Deep learning and contextual information extraction methods have been applied to many image segmentation tasks. However, their performance is limited due to insufficient training of a large number of parameters and these parameters sometimes fail to capture long-term dependencies. In addition, due to the many interfering factors of the skin disease image, the complex boundary and the uncertain size and shape of the lesion, the segmentation of the skin disease image is still a challenging problem. To solve these problems, we propose a long-distance contextual attention network(LCA-Net). By connecting the non-local module and the channel attention (CAM) in parallel to form a non-local operation, the long-term dependence is captured from the two dimensions of space and channel to enhance the network’s ability to extract features of skin diseases. Our method has an average Jaccard index of 0.771 on the ISIC2017 dataset, which represents a 0.6%improvement over the ISIC2017 Challenge Champion model. The average Jaccard index of 5-fold cross-validation on the ISIC2018 dataset is 0.8256. At the same time, we also compared with some advanced methods of image segmentation, the experimental results show our proposed method has a competitive performance.

Keywords

Skin lesion segmentation attentional mechanism artificial intelligence deep learning

Get full access to this article

View all access options for this article.

References

Desantis

C.E.

, et al., Breast cancer statistics, CA: ACancer Journal for Clinicians 69(6) (2019).

Berseth

, ISIC 2017 - Skin Lesion Analysis Towards Melanoma Detection, (2017).

pmhdev and Pub Med Health, Dermoscopy compared with naked eye examination for the diagnosis of primary melanoma: a meta-analysis of studies performed in a clinical setting –National Library of Medicine –Pub Med Health, Centre for Reviews & Dissemination (2008).

Yuan

and Lo

Y.C.

, Improving Dermoscopic Image Segmentation withEnhanced Convolutional-Deconvolutional Networks, IEEE Journalof Biomedical and Health Informatics 99(1) (2017).

, Yu

, Chen

, Fu

C.W.

and Heng

P.A.

, Semisupervised Skin Lesion Segmentation via Transformation Consistent Self-ensembling Model,” (2018).

“U-Net: Convolutional Networks for Biomedical Image Segmentation,” (2015).

Zhou

, Siddiquee

, Tajbakhsh

and Liang

, UNet++: A NestedU-Net Architecture for Medical Image Segmentation, (2018).

Sun

, Xiao

, Liu

and Wang

, Deep High-Resolution Representation Learning for Human Pose Estimation, (2019).

Woo

, Park

, Lee

J.Y.

and Kweon

I.S.

, CBAM: Convolutional Block Attention Module, Springer, Cham, (2018).

10.

Jie

, Li

, Gang

and Albanie

, Squeeze and-Excitation Networks, IEEE Transactions on Pattern Analysis and Machine Intelligence (99) (2017).

11.

, Wang

, Hu

and Yang

, Selective Kernel Networks, IEEE (2019).

12.

Wang

, Girshick

, Gupta

and He

, Non-local Neural Networks, (2017).

13.

Alom

M.Z.

, Hasan

, Yakopcic

, Taha

T.M.

and AsariRecurrent

V.K.

, Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation, (2018).

14.

Jha

, Riegler

M.A.

, Johansen

, Halvorsen

and JohansenDoubleU-Net:

H.D.

, DoubleU-Net:ADeep Convolutional Neural Network for Medical Image Segmentation, (2020).

15.

Yuan

, Chao

and Lo

Y.C.

, Automatic Skin Lesion SegmentationUsing Deep Fully Convolutional Networks With Jaccard Distance, IEEE Trans Med Imaging (9) (2017), 1.

16.

Feyjie

A.R.

, Azad

, Pedersoli

, Kauffman

and Dolz

, Semi-supervised few-shot learning for medical image segmentation, (2020).

17.

Ren

, Yu

, Tian

, Cheng

, Guo

and Zhang

, Serial attention network for skin lesion segmentation, Journal of Ambient Intelligence and Humanized Computing 1–12.

18.

Tomar

N.K.

, et al., FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation, arXiv preprint arXiv:2103.17235 (2021).

19.

Tomar

N.K.

, et al., FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation, arXiv preprint arXiv:2103.17235, (2021).

20.

Huang

Y.P.

, Singh

and Kuo

H.C.

, A Hybrid Fuzzy ClusteringApproach for the Recognition and Visualization of MRI Images ofParkinson’s Disease, IEEE Access 8(1) (2020).

21.

Thomas

S.M.

, Lefevre

J.G.

, Baxter

and HamiltonInterpretable

N.A.

, deep learning systems for multi-class segmentation andclassification of non-melanoma skin cancer, Medical ImageAnalysis 68 (2021), 101915.

22.

Alzubaidi

, Al-Amidie

, Al-Asadi

, Humaidi

A.J.

and YeNovel

, Transfer Learning Approach for Medical Imaging with LimitedLabeled Data, Cancers 13(7) (2021).

23.

, et al., X-Net: Brain Stroke Lesion Segmentation Based on Depthwise Separable Convolution and Longrange Dependencies, (2019).

24.

Codella

, et al., Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC), (2017).

25.

Tschandl

, Rosendahl

and Kittler

, The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions, Scientific Data (2018).

26.

Ebner

, A parallel algorithm for color constancy, Journal ofParallel & Distributed Computing 64(1) (2004), 79–88.

27.

Wen

, II-FCN for skin lesion analysis towards melanoma detection, (2017).

28.

T.T.

and Quang

N.H.

, Automatic skin lesion analysis towards melanoma detection, (2017).

29.

and Shen

, Skin Lesion Analysis towards Melanoma DetectionUsing Deep Learning Network, Sensors 18(2) (2018), 322.

30.

Kaul

, Manandhar

and Pears

, FocusNet: An attentionbased Fully Convolutional Network for Medical Image Segmentation, IEEE (2019).

31.

Jose

, Garcia-Arroyo

and Garcia-Zapirain , Segmentation of skin lesions in dermoscopy images using fuzzy classification of pixels and histogram thresholding, Computer Methods & Programs in Biomedicine, (2018).

32.

Saini

, Gupta

and Tiwari

A.K.

, Detector-SegMentor Network for Skin Lesion Localization and Segmentation, in National Conference on Computer Vision, Pattern Recognition, Image Processing, and Graphics, (2019), 589–599.

33.

Saha

, Prasad

and Thabit

, Leveraging adaptive color augmentation in convolutional neural networks for deep skin lesion segmentation, in 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) (2020), 2014–2017.

34.

Jafari

, Auer

, Francis

, Garibaldi

and Chen

, DRU-net: An Efficient Deep Convolutional Neural Network for Medical Image Segmentation, IEEE (2020).

35.

, et al., CE-Net: Context Encoder Network for 2D Medical Image Segmentation, IEEE Transactions on Medical Imaging (2019), 1.

36.

, et al., Dual Attention Network for Scene Segmentation, (2020).

37.

Chen

L.C.

, Zhu

, Papandreou

, Schroff

and Adam

, Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation, Springer, Cham, (2018).

38.

Zhao

, Shi

, Qi

, Wang

and Jia

, Pyramid Scene Parsing Network, IEEE Computer Society (2016).

39.

Alom

M.Z.

, Yakopcic

, Hasan

, Taha

T.M.

and AsariRecurrent

V.K.

, residual U-Net for medical image segmentation, Journal of Medical Imaging 6(1) (2019).

40.

Roy

A.G.

, NavAb

and Wachinger

, Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks, Springer, Cham, (2018).

41.

Badrinarayanan

, Kendall

and Cipolla

, SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation, IEEE Transactions on Pattern Analysis & Machine Intelligence (2017), 1.

Long-distance contextual attention network for skin disease segmentation

Abstract

Keywords

Get full access to this article

References