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Deep information-guided feature refinement network for colorectal gland segmentation

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Reliable quantification of colorectal histopathological images is based on the precise segmentation of glands but precise segmentation of glands is challenging as glandular morphology varies widely across histological grades, such as malignant glands and non-gland tissues are too similar to be identified, and tightly connected glands are even highly possibly to be incorrectly segmented as one gland.

Methods

A deep information-guided feature refinement network is proposed to improve gland segmentation. Specifically, the backbone deepens the network structure to obtain effective features while maximizing the retained information, and a Multi-Scale Fusion module is proposed to increase the receptive field. In addition, to segment dense glands individually, a Multi-Scale Edge-Refined module is designed to strengthen the boundaries of glands.

Results

The comparative experiments on the eight recently proposed deep learning methods demonstrated that our proposed network has better overall performance and is more competitive on Test B. The F1 score of Test A and Test B is 0.917 and 0.876, respectively; the object-level Dice is 0.921 and 0.884; and the object-level Hausdorff is 43.428 and 87.132, respectively.

Conclusion

The proposed colorectal gland segmentation network can effectively extract features with high representational ability and enhance edge features while retaining details to the maximum, dramatically improving the segmentation performance on malignant glands, and better segmentation results of multi-scale and closed glands can also be obtained.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

This work was supported by National Science Foundation of P.R. China (Grants: 62233016), Key R& D Program Projects in Zhejiang Province (Grant: 2020C03074).

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Author notes

  1. Shuling Shi, Zhenbang Fan, Xiongxiong He and Ni Zhang have equally contributed to this work.

Authors and Affiliations

  1. College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310014, Zhejiang, China

    Sheng Li, Shuling Shi, Zhenbang Fan, Xiongxiong He & Ni Zhang

Authors
  1. Sheng Li
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  2. Shuling Shi
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  3. Zhenbang Fan
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  4. Xiongxiong He
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Correspondence to Ni Zhang.

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Li, S., Shi, S., Fan, Z. et al. Deep information-guided feature refinement network for colorectal gland segmentation. Int J CARS 18, 2319–2328 (2023). https://doi.org/10.1007/s11548-023-02857-7

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  • DOI: https://doi.org/10.1007/s11548-023-02857-7

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