A progressive fusion network for endoscopic medical image segmentation.
Authors
Affiliations (5)
Affiliations (5)
- School of Integrated Circuits, Anhui University, HeFei, 230601, China.
- Anhui Engineering Laboratory of Agro-Ecological Big Data, HeFei, 230601, China.
- School of Integrated Circuits, Anhui University, HeFei, 230601, China. [email protected].
- Anhui Engineering Laboratory of Agro-Ecological Big Data, HeFei, 230601, China. [email protected].
- School of Big Data And Statistics, Anhui University, HeFei, 230601, China.
Abstract
Endoscopic image segmentation plays a key role in assisting doctors to accurately locate focal areas and improve diagnostic efficiency. However, the existing methods are insufficient in utilizing local details and global semantic information at the same time, which makes it difficult to effectively segment organs and tissues with complex morphology, fuzzy boundaries and similar textures. Therefore, we propose a progressive fusion network (PFNet) in this paper. First, PFNet uses Pvtv2 with Transformer as the backbone encoder to capture multi-scale global features. Secondly, a noise filtering attention module (NFAM) is designed to suppress the noise and enhance the semantics of the multilevel features output by the encoder. Then, a boundary and location awareness module (BLAM) is proposed to generate high-quality boundary and position information by blending deep global features with shallow local details. Then, the auxiliary information embedding module (AIEM) is designed to embed the boundary and position information into each level feature dynamically to enhance the context-aware ability of the decoding process. Finally, the feature fusion module (FFM) supplemented the boundary and location information through layer by layer iteration to ensure the collaborative recovery of global semantics and local details. Through extensive experiments, we demonstrate that our proposed PFNet outperforms current state-of-the-art (SOTA) methods in segmentation performance on datasets including Ureter, Re-TMRS, Kvasir, CVC-ClinicDB, CVC-ColonDB, ETIS and CVC-300. In particular, the mDice on Re-TMRS dataset reached 91.07%, and the mDice on CVC-ClinicDB reached 93.09%.