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DB-DCAFN:dual-branch deformable cross-attention fusion network for bacterial segmentation

Visual Computing for Industry,Biomedicine,and Art 2023年第1期6卷 155-170页

作者： Jingkun Wang Xinyu Ma Long Cao Yilin Leng Zeyi Li Zihan Cheng Yuzhu Cao Xiaoping Huang Jian Zheng School of Biomedical Engineering(Suzhou) Division of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefei 230026China Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of SciencesSuzhou 215163China Department of Infectious Diseases the First Affiliated Hospital of Soochow UniversitySuzhou 215006China Institute of Biomedical Engineering School of Communication and Information EngineeringShanghai UniversityShanghai 200444China College of Computer and Information Hohai UniversityNanjing 210098China School of Electronic and Information Engineering Changchun University of Science and TechnologyChangchun 130022China Jinan Guoke Medical Technology Development Co. LtdJinan 250101China

Sputum smear tests are critical for the diagnosis of respiratory diseases. Automatic segmentation of bacteria from spu-tum smear images is important for improving diagnostic efficiency. However, this remains a challenging task owing to the high interclass similarity among different categories of bacteria and the low contrast of the bacterial edges. To explore more levels of global pattern features to promote the distinguishing ability of bacterial categories and main-tain sufficient local fine-grained features to ensure accurate localization of ambiguous bacteria simultaneously, we propose a novel dual-branch deformable cross-attention fusion network (DB-DCAFN) for accurate bacterial segmen-tation. Specifically, we first designed a dual-branch encoder consisting of multiple convolution and transformer blocks in parallel to simultaneously extract multilevel local and global features. We then designed a sparse and deformable cross-attention module to capture the semantic dependencies between local and global features, which can bridge the semantic gap and fuse features effectively. Furthermore, we designed a feature assignment fusion module to enhance meaningful features using an adaptive feature weighting strategy to obtain more accurate segmentation. We conducted extensive experiments to evaluate the effectiveness of DB-DCAFN on a clinical dataset comprising three bacterial categories: Acinetobacter baumannii, Klebsiella pneumoniae, and Pseudomonas aeruginosa. The experi-mental results demonstrate that the proposed DB-DCAFN outperforms other state-of-the-art methods and is effective at segmenting bacteria from sputum smear images.

关键词： bacterial segmentation Dual-branch parallel encoder Deformable cross-attention module Feature assignment fusion module

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