BibTex format

author = {Zhang, L and Yang, G and Ye, X},
doi = {10.1117/1.JMI.6.2.024001},
journal = {Journal of Medical Imaging},
title = {Automatic skin lesion segmentation by coupling deep fully convolutional networks and shallow network with textons},
url = {},
volume = {6},
year = {2019}

RIS format (EndNote, RefMan)

AB - Segmentation of skin lesions is an important step in computer-aided diagnosis of melanoma; it is also a very challenging task due to fuzzy lesion boundaries and heterogeneous lesion textures. We present a fully automatic method for skin lesion segmentation based on deep fully convolutional networks (FCNs). We investigate a shallow encoding network to model clinically valuable prior knowledge, in which spatial filters simulating simple cell receptive fields function in the primary visual cortex (V1) is considered. An effective fusing strategy using skip connections and convolution operators is then leveraged to couple prior knowledge encoded via shallow network with hierarchical data-driven features learned from the FCNs for detailed segmentation of the skin lesions. To our best knowledge, this is the first time the domain-specific hand craft features have been built into a deep network trained in an end-to-end manner for skin lesion segmentation. The method has been evaluated on both ISBI 2016 and ISBI 2017 skin lesion challenge datasets. We provide comparative evidence to demonstrate that our newly designed network can gain accuracy for lesion segmentation by coupling the prior knowledge encoded by the shallow network with the deep FCNs. Our method is robust without the need for data augmentation or comprehensive parameter tuning, and the experimental results show great promise of the method with effective model generalization compared to other state-of-the-art-methods.
AU - Zhang,L
AU - Yang,G
AU - Ye,X
DO - 10.1117/1.JMI.6.2.024001
PY - 2019///
SN - 2329-4302
TI - Automatic skin lesion segmentation by coupling deep fully convolutional networks and shallow network with textons
T2 - Journal of Medical Imaging
UR -
UR -
UR -
VL - 6
ER -