Abstract
Automated skin lesion segmentation is essential to assist doctors in diagnosis. Most methods focus on lesion segmentation of dermoscopy images, while a few focus on clinical images. Nearly all the existing methods tackle the binary segmentation problem as to distinguish lesion parts from normal skin parts, and are designed for diseases with localized solitary skin lesion. Besides, the characteristics of both the dermoscopy images and the clinical images are four-fold: (1) Only one skin lesion exists in the image. (2) The skin lesion mostly appears in the center of the image. (3) The backgrounds are similar between different images of same modality. (4) The resolution of images isn’t high, with an average of about \(1500\times 1200\) in several popular datasets. In contrast, this paper focuses on a four-class segmentation task for Cutaneous T-cell lymphomas (CTCL), an extremely aggressive skin disease with three visually similar kinds of lesions. For the first time, we collect a new dataset, which only contains clinical images captured from different body areas of human. The main characteristics of these images differ from all the existing images in four aspects: (1) Multiple skin lesion parts exist in each image. (2) The skin lesion parts are widely scattered in different areas of the image. (3) The background of the images has a large variety. (4) All the images have high resolutions, with an average of \(3255 \times 2535\). According to the characteristics and difficulties of CTCL, we design a new Multi Knowledge Learning Network (MKLN). The experimental results demonstrate the superiority of our method, which meet the clinical needs.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Jafari, M.H., Karimi, N., Nasr-Esfahani, E., et al.: Skin lesion segmentation in clinical images using deep learning. In: International Conference on Pattern Recognition, pp. 337–342 (2016)
Patiño, D., Avendaño, J., Branch, J.W.: Automatic skin lesion segmentation on dermoscopic images by the means of superpixel merging. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 728–736. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00937-3_83
Girardi, M., Heald, P.W., Wilson, L.D.: The pathogenesis of mycosis fungoides. New Engl. J. Med. 350(19), 1978–1988 (2004)
Wang, H., Wang, G., Sheng, Z., Zhang, S.: Automated segmentation of skin lesion based on pyramid attention network. In: Suk, H.-I., Liu, M., Yan, P., Lian, C. (eds.) MLMI 2019. LNCS, vol. 11861, pp. 435–443. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32692-0_50
Huan, W., Guotai, W., Zhihan, X., Wenhui, L., Shaoting, Z.: Star shape prior in fully convolutional networks for skin lesion segmentation. In: International Workshop on Machine Learning in Medical Imaging, pp. 611–619 (2019)
Filali, I., Belkadi, M.: Multi-scale contrast based skin lesion segmentation in digital images. Optik 185, 794–811 (2019)
Glaister, J., Wong, A., Clausi, D.A.: A segmentation of skin lesions from digital images using joint statistical texture distinctiveness. Pattern Recogn. 61(4), 1220–1230 (2014)
Korgavkar, K., Xiong, M., Weinstock, M.: Changing incidence trends of cutaneous t-cell lymphoma. JAMA Dermatol. 149(11), 1295–1299 (2013)
Song, L., Lin, J., Wang, Z.J., Wang, H.: Dense-residual attention network for skin lesion segmentation. In: Suk, H.-I., Liu, M., Yan, P., Lian, C. (eds.) MLMI 2019. LNCS, vol. 11861, pp. 319–327. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32692-0_37
Zortea, M., Flores, E., Scharcanski, J.: A simple weighted thresholding method for the segmentation of pigmented skin lesions in macroscopic images. Pattern Recogn. 64, 92–104 (2017)
Sarker, M.M.K., et al.: SLSDeep: skin lesion segmentation based on dilated residual and pyramid pooling networks. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 21–29. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00934-2_3
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Olsen, E., Whittaker, S., Kim, Y., et al.: Clinical end points and response criteria in mycosis fungoides and sézary syndrome: a consensus statement of the international society for cutaneous lymphomas, the united states cutaneous lymphoma consortium, and the cutaneous lymphoma task force of the European organisation for research and treatment of cancer. J. Clin. Oncol. 29(18), 2598 (2011)
Pulitzer, M.: Cutaneous t-cell lymphoma. Clin. Lab. Med. 37(3), 527–546 (2017)
Rogers, H.W., Weinstock, M.A., Feldman, S.R., Coldiron, B.M.: Incidence estimate of nonmelanoma skin cancer (keratinocyte carcinomas) in the US population, 2012. JAMA Dermatol. 151(10), 1081–1086 (2015)
Wilcox, R.A.: Cutaneous t-cell lymphoma: 2017 update on diagnosis, risk-stratification, and management. Am. J. Hematol. 92(10), 1085–1102 (2017)
Izadi, S., Mirikharaji, Z., Kawahara, J., Hamarneh, G.: Generative adversarial networks to segment skin lesions. In: IEEE 15th International Symposium on Biomedical Imaging, pp. 881–884 (2018)
Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics, 2015. CA Cancer J. Clin. 65(1), 5–29 (2015)
Willemze, R., Jaffe, E.S., Burg, G., et al.: WHO-EORTC classification for cutaneous lymphomas. Blood 105(10), 3768–3785 (2005)
Li, X., Yu, L., Fu, C.-W., Heng, P.-A.: Deeply supervised rotation equivariant network for lesion segmentation in dermoscopy images. In: Stoyanov, D., et al. (eds.) CARE/CLIP/OR 2.0/ISIC -2018. LNCS, vol. 11041, pp. 235–243. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01201-4_25
Mirikharaji, Z., Hamarneh, G.: Star shape prior in fully convolutional networks for skin lesion segmentation. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 737–745. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00937-3_84
Acknowledgement
This work was partially supported by the Natural Science Foundation of China under contracts 61572042, 61772041, 81922058 and National Key R&D Program of China 2019YFC0840700. We also acknowledge the Clinical Medicine Plus X-Young Scholars Project, and High-Performance Computing Platform of Peking University for providing computational resources.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Liu, Z. et al. (2020). Multi-class Skin Lesion Segmentation for Cutaneous T-cell Lymphomas on High-Resolution Clinical Images. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12266. Springer, Cham. https://doi.org/10.1007/978-3-030-59725-2_34
Download citation
DOI: https://doi.org/10.1007/978-3-030-59725-2_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59724-5
Online ISBN: 978-3-030-59725-2
eBook Packages: Computer ScienceComputer Science (R0)