Abstract
BACKGROUND: Skin lesion segmentation plays a significant role in skin cancer diagnosis. However, due to the complex shapes, varying sizes, and different color depths, precise segmentation of skin lesions is a challenging task. Therefore, the aim of this study was to design a customized deep learning (DL) model for the precise segmentation of skin lesions, particularly for complex shapes and small target lesions. METHODS: In this study, an adaptive deformable fusion convolutional network (Seg-SkiNet) was proposed. Seg-SkiNet integrated dual-channel convolution encoder (Dual-Conv encoder), Multi-Scale-Multi-Receptive Field Extraction and Refinement (Multi(2)ER) module, and local-global information interaction fusion decoder (LGI-FSN decoder). In the Dual-Conv encoder, a Dual-Conv module was proposed and cascaded with max pooling in each layer to capture the features of complex-shaped skin lesions. The design of the Dual-Conv module not only effectively captured edge features of the lesions but also learned deep internal features of the lesions. The Multi(2)ER module was composed of an Atrous Spatial Pyramid Pooling (ASPP) module and an Attention Refinement Module (ARM), and integrated multi-scale features of small target lesions by expanding the receptive field of the convolutional kernel, thereby improving the learning and accurately segmentation of small target lesions. In the LGI-FSN decoder, we integrated convolution and Local-Global Attention Fusion (LGAF) module in each layer to enable interactive fusion of local-global information in feature maps while eliminating redundant feature information. Additionally, we designed a densely connected architecture that fuses the feature maps from a specific layer of the Dual-Conv encoder and all of its preceding layers into the corresponding layer of the LGI-FSN decoder, preventing information loss caused by pooling operations. RESULTS: We validated the performance of Seg-SkiNet for skin lesion segmentation on three public datasets: International Skin Imaging Collaboration (ISIC)-2016, ISIC-2017, and ISIC-2018. The experimental results demonstrated that Seg-SkiNet achieved a Dice coefficient (DICE) of 93.66%, 89.44% and 92.29%, respectively. CONCLUSIONS: The Seg-SkiNet model performed excellently in segmenting complex-shaped lesions and small target lesions.