A Bi-Directionally Fused Boundary Aware Network for Skin Lesion Segmentation

• Published in: IEEE transactions on image processing Vol. 33; pp. 6340 - 6353
• Main Authors: Yuan, Feiniu, Peng, Yuhuan, Huang, Qinghua, Li, Xuelong
• Format: Journal Article
• Language: English
• Published: United States IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Artificial neural networks; Attention; Bidirectional control; Biomedical imaging; Boundaries; CNN; Coders; Convolutional neural networks; Decoders; Decoding; deep learning; Design; Feature extraction; Feature maps; Image segmentation; Lesions; Segmentation; Semantics; Skin; Skin lesion segmentation; Spatial data; transformer; Transformers
• ISSN: 1057-7149; 1941-0042; 1941-0042
• DOI: 10.1109/TIP.2024.3482864

It is quite challenging to visually identify skin lesions with irregular shapes, blurred boundaries and large scale variances. Convolutional Neural Network (CNN) extracts more local features with abundant spatial information, while Transformer has the powerful ability to capture more global information but with insufficient spatial details. To overcome the difficulties in discriminating small or blurred skin lesions, we propose a Bi-directionally Fused Boundary Aware Network (BiFBA-Net). To utilize complementary features produced by CNNs and Transformers, we design a dual-encoding structure. Different from existing dual-encoders, our method designs a Bi-directional Attention Gate (Bi-AG) with two inputs and two outputs for crosswise feature fusion. Our Bi-AG accepts two kinds of features from CNN and Transformer encoders, and two attention gates are designed to generate two attention outputs that are sent back to the two encoders. Thus, we implement adequate exchanging of multi-scale information between CNN and Transformer encoders in a bi-directional and attention way. To perfectly restore feature maps, we propose a progressive decoding structure with boundary aware, containing three decoders with six supervised losses. The first decoder is a CNN network for producing more spatial details. The second one is a Partial Decoder (PD) for aggregating high-level features with more semantics. The last one is a Boundary Aware Decoder (BAD) proposed to progressively improve boundary accuracy. Our BAD uses residual structure and Reverse Attention (RA) at different scales to deeply mine structural and spatial details for refining lesion boundaries. Extensive experiments on public datasets show that our BiFBA-Net achieves higher segmentation accuracy, and has much better ability of boundary perceptions than compared methods. It also alleviates both over-segmentation of small lesions and under-segmentation of large ones.