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DFCA-Net: Dual Feature Context Aggregation Network for Bleeding Areas Segmentation in Wireless Capsule Endoscopy Images



Wireless capsule endoscopy (WCE) is an effective and non-invasive advanced technology for the diagnosis of gastrointestinal (GI) abnormalities. From a clinical perspective, one of the most common and valuable indication for WCE is GI bleeding. However, the bleeding point may be incorrectly localized by bleeding detection methods, due to the small size of bleeding areas and the bubbles interference in the bleeding areas. These problems make it difficult to accurately localize bleeding point in GI images.


Therefore, a pixel-level segmentation method for GI bleeding is proposed, in which the dual network branches based on attention mechanism are designed to correctly classify the pixel samples of the bleeding areas. These two branches complement each other and focus on extracting the color and the texture features of the bleeding areas, respectively. The outputs of the dual network branches are combined finally by the feature fusion module to obtain a more accurate segmentation result.


Extensive experiments have been done on public WCE image datasets to test the performance of our proposed network. The mean intersection over union (mIoU) of our proposed network is 86.858%, which shows its significant segmentation performance.


A novel network for GI bleeding segmentation is developed, which can obtain promising segmentation performance compared with some existing popular methods.

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This work was supported by National Science Foundation of P.R. China (Grants: 61873239), Key R&D Program Projects in Zhejiang Province (Grant: 2020C03074).

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Correspondence to Xiongxiong He.

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Li, S., Si, P., Zhang, Z. et al. DFCA-Net: Dual Feature Context Aggregation Network for Bleeding Areas Segmentation in Wireless Capsule Endoscopy Images. J. Med. Biol. Eng. 42, 179–188 (2022).

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  • Wireless capsule endoscopy
  • Automatic bleeding segmentation
  • Attention mechanism
  • Dual network branch