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Dense pooling layers in fully convolutional network for skin lesion segmentation.
Comput Med Imaging Graph 2019; 78:101658CM

Abstract

One of the essential tasks in medical image analysis is segmentation and accurate detection of borders. Lesion segmentation in skin images is an essential step in the computerized detection of skin cancer. However, many of the state-of-the-art segmentation methods have deficiencies in their border detection phase. In this paper, a new class of fully convolutional network is proposed, with new dense pooling layers for segmentation of lesion regions in skin images. This network leads to highly accurate segmentation of lesions on skin lesion datasets, which outperforms state-of-the-art algorithms in the skin lesion segmentation.

Authors+Show Affiliations

Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran.Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran.Department of Electrical Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran.Department of Internal Medicine, University of Michigan, Ann Arbor, 48109, USA.Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran; Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON L8S4L8, Canada; Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, 48109, USA.Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, 48109, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, 48109, USA. Electronic address: ssoroush@umich.edu.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

31634739

Citation

Nasr-Esfahani, Ebrahim, et al. "Dense Pooling Layers in Fully Convolutional Network for Skin Lesion Segmentation." Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society, vol. 78, 2019, p. 101658.
Nasr-Esfahani E, Rafiei S, Jafari MH, et al. Dense pooling layers in fully convolutional network for skin lesion segmentation. Comput Med Imaging Graph. 2019;78:101658.
Nasr-Esfahani, E., Rafiei, S., Jafari, M. H., Karimi, N., Wrobel, J. S., Samavi, S., & Reza Soroushmehr, S. M. (2019). Dense pooling layers in fully convolutional network for skin lesion segmentation. Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society, 78, p. 101658. doi:10.1016/j.compmedimag.2019.101658.
Nasr-Esfahani E, et al. Dense Pooling Layers in Fully Convolutional Network for Skin Lesion Segmentation. Comput Med Imaging Graph. 2019 Oct 7;78:101658. PubMed PMID: 31634739.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Dense pooling layers in fully convolutional network for skin lesion segmentation. AU - Nasr-Esfahani,Ebrahim, AU - Rafiei,Shima, AU - Jafari,Mohammad H, AU - Karimi,Nader, AU - Wrobel,James S, AU - Samavi,Shadrokh, AU - Reza Soroushmehr,S M, Y1 - 2019/10/07/ PY - 2018/06/08/received PY - 2019/09/06/revised PY - 2019/09/09/accepted PY - 2019/10/22/pubmed PY - 2019/10/22/medline PY - 2019/10/22/entrez KW - Deep neural networks KW - Dense pooling layer KW - Melanoma KW - Skin cancer SP - 101658 EP - 101658 JF - Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society JO - Comput Med Imaging Graph VL - 78 N2 - One of the essential tasks in medical image analysis is segmentation and accurate detection of borders. Lesion segmentation in skin images is an essential step in the computerized detection of skin cancer. However, many of the state-of-the-art segmentation methods have deficiencies in their border detection phase. In this paper, a new class of fully convolutional network is proposed, with new dense pooling layers for segmentation of lesion regions in skin images. This network leads to highly accurate segmentation of lesions on skin lesion datasets, which outperforms state-of-the-art algorithms in the skin lesion segmentation. SN - 1879-0771 UR - https://www.unboundmedicine.com/medline/citation/31634739/Dense_pooling_layers_in_fully_convolutional_network_for_skin_lesion_segmentation L2 - https://linkinghub.elsevier.com/retrieve/pii/S0895-6111(18)30348-3 DB - PRIME DP - Unbound Medicine ER -