Seg-SkiNet: adaptive deformable fusion convolutional network for skin lesion segmentation.

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 (Multi²ER) 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 Multi²ER 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.