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TSHVNet: Simultaneous Nuclear Instance Segmentation and Classification in Histopathological Images Based on
Yuli Chen1, Yuhang Jia1, Xinxin Zhang1
1School of Computer Science, Shaanxi Normal University, Xi'an 710119, China.
Biomed Research International
|December 2, 2022
Summary
A new deep learning framework, TSHVNet, enhances nuclear instance segmentation and classification in histopathologic images by integrating multi-attention modules. This improves cancer diagnosis accuracy by better distinguishing nuclei and handling clustered instances.
Area of Science:
- Computational pathology
- Medical image analysis
- Deep learning for oncology
Background:
- Accurate nuclear instance segmentation and classification are crucial for cancer diagnosis and prognosis.
- Existing methods face challenges with similar nuclear appearances, clustered instances, and limited global dependency modeling.
Purpose of the Study:
- To propose TSHVNet, a novel deep learning framework for accurate simultaneous nuclear instance segmentation and classification.
- To improve upon the state-of-the-art HoVer-Net by integrating multi-attention modules (Transformer and SimAM).
Main Methods:
- TSHVNet integrates Transformer attention on the HoVer-Net trunk for long-distance relationships.
- SimAM attention modules are applied to both trunk and branches for 3D channel and spatial weighting.
- The framework was validated on the public PanNuke and CoNSeP datasets.
Main Results:
- TSHVNet demonstrated superior performance compared to existing state-of-the-art methods.
- Nuclear instance segmentation (PQ index) improved by 1.4% (CoNSeP) and 2.8% (PanNuke).
- Nuclear classification (F1_score) increased by 2.4% (CoNSeP) and 2.5% (PanNuke).
Conclusions:
- The proposed TSHVNet effectively addresses challenges in nuclear instance segmentation and classification.
- The integration of multi-attention modules significantly enhances performance.
- TSHVNet shows great potential for advancing cancer diagnosis and prognosis through improved histopathologic image analysis.
